The DOI matches its Crossref record; 71 of the 88 references it cites were found in a registry or an index; 8 of the 27 sources its claims cite were checked for support.
Vision Transformers for Dense Prediction
Open accessAbstract
We introduce dense prediction transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks. We assemble tokens from various stages of the vision transformer into image-like representations at various resolutions and progressively combine them into full-resolution predictions using a convolutional decoder. The transformer backbone processes representations at a constant and relatively high resolution and has a global receptive field at every stage. These properties allow the dense prediction transformer to provide finer-grained and more globally coherent predictions when compared to fully-convolutional networks. Our experiments show that this architecture yields substantial improvements on dense prediction tasks, especially when a large amount of training data is available. For monocular depth estimation, we observe an improvement of up to 28% in relative performance when compared to a state-of-the-art fully-convolutional network. When applied to semantic segmentation, dense prediction transformers set a new state of the art on ADE20K with 49.02% mIoU. We further show that the architecture can be fine-tuned on smaller datasets such as NYUv2, KITTI, and Pascal Context where it also sets the new state of the art. Our models are available at https://github.com/intel-isl/DPT.
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32 Own result · 23 Own interpretation · 11 No basis given · 10 No full text of the sources · 3 Supported · 2 Partly supported · 1 Supported so far · 1 Own method · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Specifically, we use the recently proposed vision transformer (ViT) [11] as a backbone architecture.”
§ Vision Transformers for Dense Prediction > 1. Introduction
What the sources say
- Supportedcites [11] 11. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. source
Section 3.1 (p. 3) introduces and describes the Vision Transformer (ViT) architecture. The citation identifies the ViT used as the citing authors’ backbone; their choice to use it is not a claim attributed to this source. (LLM, conf=0.93)
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In the paper
“Virtually all existing architectures for dense prediction are based on convolutional networks [6,31,34,42,49,50,53].”
§ Vision Transformers for Dense Prediction > 1. Introduction
What the sources say
- Partly supportedcites [6] 6. Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille. DeepLab: Semantic im- age segmentation with deep convolutional nets, atrous con- volution, and fully connected crfs. TPAMI, 40(4):834-848, 2018. sourceExperimental — accuracy not yet measured
The Introduction (p. 1) says convolutional models such as PixelCNN had received much more attention for pixel-by-pixel image generation. It does not establish that virtually all architectures across the broader category of dense prediction are convolutional; it also discusses recurrent models and proposes a self-attention alternative (Introduction, p. 2). (LLM, conf=0.78)
- Not checked — no full text of the sourcecites [31] 31. Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U- Net: Convolutional networks for biomedical image segmen- tation. In MICCAI, 2015. source
- Supportedcites [34] 34. Evan Shelhamer, Jonathan Long, and Trevor Darrell. Fully convolutional networks for semantic segmentation. CVPR, 2015. source
The paper is an example of a convolutional-network architecture for dense prediction: its Abstract (p. 1) describes predicting a depth map, and §3.1.2 (p. 3) identifies its convolutional layers and target-depth output. As one of several citations, it supports the sentence by exemplifying such an architecture, rather than establishing the prevalence claim on its own. (LLM, conf=0.88)
- Could not verifycites [42] 42. Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, and Bin Xiao. Deep high-resolution representation learning for visual recogni- tion. TPAMI, 2020. sourceExperimental — accuracy not yet measured
Section 2.1 (p. 8) describes transformer language-model architectures, not architectures for dense prediction. Searches for dense prediction, convolutional networks, and pixel-wise segmentation found no passage addressing whether dense-prediction architectures are predominantly convolutional. (LLM, conf=0.78)
- Not matchedcites [49] 49. Fisher Yu and Vladlen Koltun. Multi-scale context aggrega- tion by dilated convolutions. In ICLR, 2016.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [50] 50. Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object- contextual representations for semantic segmentation. In ECCV, 2020. source
- Not checked — no full text of the sourcecites [53] 53. Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In CVPR, 2017. source
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In the paper
“We notice that the biggest gains in performance for zero-shot transfer were achieved for datasets that feature dense, high-resolution evaluations [15,55,59].”
§ Vision Transformers for Dense Prediction > Additional information
What the sources say
- Not checked — no full text of the sourcecites [15] 15. Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. In CVPR, 2012. source
- Supportedcites [55] 55. D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black. A naturalistic open source movie for optical flow evaluation. In ECCV, 2012. source
The citation identifies MPI-Sintel as one of the datasets in the citing authors’ comparison; the performance gains are the citing authors’ observation, not a result attributed to this paper. Design Decisions and Comparison to Middlebury (p. 5) describes Sintel’s higher-resolution frames, and The Sintel Data Set (p. 9) describes its flow fields and pixel-level error evaluation. (LLM, conf=0.86)
- Not checked — no full text of the sourcecites [59] 59. Thomas Schöps, Johannes L. Schönberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and An- dreas Geiger. A multi-view stereo benchmark with high- resolution images and multi-camera videos. In CVPR, 2017. source
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In the paper
“Note that ResNext101-WSL was pretrained on a billion-scale corpus of weakly supervised data [25] in addition to ImageNet pretraining.”
§ Vision Transformers for Dense Prediction > Skip connections.
What the sources say
- Partly supportedcites [25] 25. Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten. Exploring the limits of weakly supervised pretraining. In ECCV, 2018. sourceExperimental — accuracy not yet measured
Section 2.3 (p. 4) identifies the models as ResNeXt-101, and Section A.5 (p. 23) says they were pretrained on billions of Instagram images using hashtags as labels. But it describes subsequent training on ImageNet as fine-tuning, not additional ImageNet pretraining; Section 3.2 (p. 12) also distinguishes Instagram-pretrained models from ImageNet-pretrained baselines. (LLM, conf=0.88)
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In the paper
“While ignoring the token yields good performance, projection provides slightly better performance on average.”
§ Vision Transformers for Dense Prediction > Skip connections.
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In the paper
“Adding the token, on the other hand, yields worse performance than simply ignoring it.”
§ Vision Transformers for Dense Prediction > Skip connections.
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In the paper
“Both DPT variants significantly outperform the state of the art.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“Note that both architectures have similar latency to MiDaS (Table 9).”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“ViT-Large outperforms all other backbones but is also almost three times larger than ViT-Base and ViT-Hybrid.”
§ Vision Transformers for Dense Prediction > Skip connections.
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In the paper
“We again see that DPT can provide strong performance even on smaller datasets.”
§ Vision Transformers for Dense Prediction > ADE20K.
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- Probably not a substantial claim. 11
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Every source the paper cites for this claim was checked and supports it.
In the paper
“In particular, it has recently been demonstrated that a direct application of token-based transformer architectures that have been successful in NLP can yield competitive performance on image classification [11].”
§ Vision Transformers for Dense Prediction > 2. Related Work
What the sources say
- Supportedcites [11] 11. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. source
The Introduction (p. 1) says image patches are treated like NLP tokens and fed to a standard Transformer. The Conclusion (p. 9) reports that this approach matches or exceeds the state of the art on many image-classification datasets when coupled with pre-training on large datasets. (LLM, conf=0.93)
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In the paper
“A key insight of this work was that, like transformer models in NLP, vision transformers need to be paired with a sufficient amount of training data to realize their potential.”
§ Vision Transformers for Dense Prediction > 2. Related Work
What the sources say
- Supportedcites [11] 11. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. source
The Introduction (p. 1) says ViT performs modestly on mid-sized datasets and does not generalize well with insufficient data, but attains excellent results when pre-trained at sufficient scale. Section 4.3 (p. 6) reports that larger ViT models show their full benefit only with the largest pre-training dataset tested. The Conclusion (p. 9) also describes the approach as working well when coupled with pre-training on large datasets. (LLM, conf=0.94)
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In the paper
“Similarly, the ViT-Hybrid architecture extracts features at 1 16 the input resolution, which is twice as high as the lowest-resolution features that are commonly used with convolutional backbones.”
§ Vision Transformers for Dense Prediction > 3. Architecture
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In the paper
“It contains about 1.4 million images and is, to the best of our knowledge, the largest training set for monocular depth estimation that has ever been compiled.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“When applied to semantic segmentation, dense vision transformers set a new state of the art on ADE20K with 49.02% mIoU.”
§ Vision Transformers for Dense Prediction > Abstract
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In the paper
“We observe that the DPT tends to produce cleaner and finer-grained delineations of object boundaries and that the predictions are also in some cases less cluttered.”
§ Vision Transformers for Dense Prediction > ADE20K.
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In the paper
“DPT can better reconstruct fine details while also improving global coherence in areas that are challenging for the convolutional architecture (for example, large homogeneous regions or relative depth arrangement across the image).”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“The average relative improvement over the best published architecture, MiDaS, is more than 23% for DPT-Hybrid and 28% for DPT-Large.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“DPT-Hybrid achieves this with a comparable network capacity (Table 9), while DPT-Large is about 3 times larger than MiDaS.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“We observe the tendency that attention is spatially more localized close to the reference token in shallow layers (leftmost columns), whereas deeper layers (rightmost columns) frequently attend across the whole image.”
§ Vision Transformers for Dense Prediction > Additional information
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In the paper
“ViT-Base has comparable performance to ResNext101-WSL, while ViT-Hybrid and ViT-Large improve performance even though they have been pretrained on significantly less data.”
§ Vision Transformers for Dense Prediction > Skip connections.
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In the paper
“While we observe a general trend of an improvement in per-class IoU in comparison to the baseline [51], we do not observe a strong pattern across classes.”
§ Vision Transformers for Dense Prediction > Additional information
What the sources say
- Not checked — no full text of the sourcecites [51] 51. Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Muller, R. Manmatha, Mu Li, and Alexander Smola. ResNeSt: Split- attention networks. arXiv preprint arXiv:2004.08955, 2020.
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In the paper
“While we observe a general trend of an improvement in per-class IoU in comparison to the baseline [51], we do not observe a strong pattern across classes.”
§ Vision Transformers for Dense Prediction > Additional information
What the sources say
- Not checked — no full text of the sourcecites [51] 51. Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Muller, R. Manmatha, Mu Li, and Alexander Smola. ResNeSt: Split- attention networks. arXiv preprint arXiv:2004.08955, 2020.
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In the paper
“We observe more details and also better global depth arrangement in DPT predictions when compared to the fullyconvolutional baseline.”
§ Vision Transformers for Dense Prediction > Additional information
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In the paper
“The transformer backbone processes representations at a constant and relatively high resolution and has a global receptive field at every stage.”
§ Vision Transformers for Dense Prediction > Abstract
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In the paper
“As long as the image size is divisible by p, the embedding procedure can be applied and will produce a varying number of image tokens N p .”
§ Vision Transformers for Dense Prediction > 3. Architecture
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In the paper
“Unlike fully-convolutional networks, the vision transformer backbone foregoes explicit downsampling operations after an initial image embedding has been computed and maintains a representation with constant dimensionality throughout all processing stages.”
§ Vision Transformers for Dense Prediction > 1. Introduction
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In the paper
“Since transformers are known to realize their full potential only when an abundance of training data is available, monocular depth estimation is an ideal task to test the capabilities of DPT.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“This is in stark contrast to convolutional networks, which progressively increase their receptive field as features pass through consecutive convolution and downsampling layers.”
§ Vision Transformers for Dense Prediction > 3. Architecture
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In the paper
“DPT-Large performs slightly worse, likely because of the significantly smaller dataset compared to our previous experiments.”
§ Vision Transformers for Dense Prediction > ADE20K.
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In the paper
“DeIT trains the ViT architecture with a more data-efficient pretraining procedure.”
§ Vision Transformers for Dense Prediction > Skip connections.
What the sources say
- Not checked — no full text of the sourcecites [38] 38. Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through at- tention. arXiv preprint arXiv:2012.12877, 2020. source
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In the paper
“Note that the DeIT-Base architecture is identical to ViT-Base, while DeIT-Base-Dist introduces an additional distillation token, which we ignore in the Reassemble operation.”
§ Vision Transformers for Dense Prediction > Skip connections.
What the sources say
- Not checked — no full text of the sourcecites [38] 38. Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through at- tention. arXiv preprint arXiv:2012.12877, 2020. source
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In the paper
“Similar to prior work on transformers, DPT unfolds its full potential when trained on large-scale datasets.”
§ Vision Transformers for Dense Prediction > 5. Conclusion
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In the paper
“Note that results for DPT and Mi-DaS are computed at the same input resolution (384 pixels).”
§ Vision Transformers for Dense Prediction > Additional information
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
The choice of backbone architecture has a large influence on the capabilities of the overall model, because information lost in the encoder cannot be recovered in the decoder.
“However, it is widely recognized that the choice of backbone architecture has a large influence on the capabilities of the overall model, as any information that is lost in the encoder is impossible to recover in the decoder.”
The paper relies on this without support.
Feature resolution and granularity are critical for dense prediction, where the architecture should ideally resolve features at or close to the input resolution.
“While feature resolution and granularity may not matter for some tasks, such as image classification, they are critical for dense prediction, where the architecture should ideally be able to resolve features at or close to the resolution of the input image.”
The paper relies on this without support.
Convolutions are linear operators with a limited receptive field.
“Convolutions, by definition, are linear operators that have a limited receptive field.”
The paper relies on this without support.
Downsampling intermediate representations is necessary to keep memory consumption at levels feasible with existing computer architectures.
“Downsampling the intermediate representations is necessary to keep memory consumption at levels that are feasible with existing computer architectures.”
The paper relies on this without support.
The transformer backbone maintains a constant feature resolution.
“Since the transformer backbone maintains a constant feature resolution, it is not clear at which points in the backbone features should be tapped.”
The paper relies on this without support.
Performance of fully-convolutional architectures suffers heavily when performing inference at an input resolution significantly different from the training resolution.
“Performance thus suffers heavily when performing inference at an input resolution that is significantly different from the training resolution.”
The paper relies on this without support.
Transformer encoders have a global receptive field in every layer.
“Transformer encoders, on the other hand, have a global receptive field in every layer.”
The paper relies on this without support.
The reduced meta-dataset datasets provide high-quality ground truth.
“We choose these datasets since they provide high-quality ground truth.”
The paper relies on this without support.
Convolutional architectures offer natural points of interest for passing features from the encoder to the decoder, namely before or after downsampling of the representation.
“Convolutional architectures offer natural points of interest for passing features from the encoder to the decoder, namely before or after downsampling of the representation.”
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In the citing paper
“Vision transformers, which are a deep learning approach to encoding low-dimensional input into a high dimensional feature space, have established new frontiers in depth estimation compared to convolutional neural networks (Ranftl et al., 2021).”
From Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar (2024) · cites this paper as Ranftl et al., 2021
In the citing paper
“However, the Transformer framework does not involve dimensionality reduction10,12, thus keeping all attention layer traceable to the original input features13, thus making the models interpretable.”
From Transformer for one stop interpretable cell type annotation (2023) · cites this paper as [10]
In the citing paper
“For example, the change of dimensions and non-linear aggregation of features throughout the autoencoder’s deep processing stages leads to untraceable and uninterpretable latent space and loss of information and feature resolution9,10.”
From Transformer for one stop interpretable cell type annotation (2023) · cites this paper as [10]
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In the paper
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
“Our architecture matches or improves state-of-the-art performance on both datasets in all metrics.”
§ Vision Transformers for Dense Prediction > 5. Conclusion
“| BTS [20] | 0.956 | 0.993 | 0.998 | 0.059 2.756 | 0.096 | | DPT-Hybrid 0.959 | 0.995 | 0.999 | 0.062 2.573 | 0.092 |”
Citations
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“These include training at higher input resolution (if the computational budget permits), dilated convolutions [51] to rapidly increase the receptive field without downsampling, appropriately-placed skip connections from multiple stages of the encoder to the decoder [33], or, more recently, by connecting multiresolutio…”
BackgroundAdam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · cited 5×
“The encoder is frequently based on an image classification network, also called the backbone, that is pretrained on a large corpus such as ImageNet [9].”
InfluentialBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325 · cited 6×
“Following work in NLP, the ViT additionally adds a special token that is not grounded in the input image and serves as the final, global image representation which is used for classification.”
BackgroundMethodInfluentialAn Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472 · cited 8×
“On a high level, the vision transformer (ViT) [11] operates on a bag-of-words representation of the image [38].”
BackgroundMethodResultInfluentialFully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · cited 2×
“Virtually all existing architectures for dense prediction are based on convolutional networks [6, 33, 36, 44, 51, 52, 55].”
BackgroundLanguage Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
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- godard, 2017, Unsupervised monocular depth estimation with leftright consistency, CVPR
- casser, 2019, Unsupervised learning of depth and ego-motion: A structured approach, AAAI
Cited by
Sorted by citations
Items 601–700 of 2,248
Hazarika, A., Vyas, A., Rahmati, M., & Wang, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3288112 · Cited by 14
“Transformer (ViT) [52], [53] to create a pre-trained, depthaware network from single monocular images captured by each camera.”
BackgroundLearning Representations from Foundation Models for Domain Generalized Stereo Matching
Zhang, Y., Wang, L., Li, K., Wang, Y., & Guo, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72946-1_9 · Cited by 11
AnyCalib: On-Manifold Learning for Model-Agnostic Single-View Camera Calibration
Tirado-Garín, J., & Civera, J. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00754 · Cited by 15
“◦ We propose an alternative “light” DPT decoder [67] that does not use expensive transposed convolutions to upsam-ple the predictions.”
Diffusion-Based Depth Inpainting for Transparent and Reflective Objects
Sun, T., Hu, D., Dai, Y., & Wang, G. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3434740 · Cited by 16
“Fail to estimate the real depth of an overlapped object The first reason is the existence of large transparent or reflective objects, as depicted in the first and second rows.”
Liu, Q., & Wang, X. (2024) · Remote Sensing · doi:10.3390/rs16132289 · Cited by 11
PPDTSA: Privacy-preserving Deep Transformation Self-attention Framework For Object Detection
Ma, B., Wu, J., Lai, E. M.-K., et al. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685855 · Cited by 10
Xia, Z., Wu, B., Chan, C. Y., Wu, T., Zhou, M., & Kong, L. B. (2024) · PLoS ONE · doi:10.1371/journal.pone.0306385 · Cited by 10
“A 1x1 convolution is used to change the channel, followed by a 3x3 convolution to resize [24].”
BackgroundMethodInfluentialParallax Inference for Robust Temporal Monocular Depth Estimation in Unstructured Environments
Fonder, M., Ernst, D., & Droogenbroeck, M. V. (2022) · Sensors · doi:10.3390/s22239374 · Cited by 10
“State-of-the-art depth estimation methods such as DPT [19,21] or ManyDepth [22] struggle to produce accurate estimates in cluttered and natural environments.”
BackgroundData Efficient 3D Learner via Knowledge Transferred from 2D Model
Yu, P.-C., Sun, C. J., & Sun, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19818-2_11 · Cited by 10
CRefNet: Learning Consistent Reflectance Estimation With a Decoder-Sharing Transformer
Luo, J., Zhao, N., Li, W., & Richardt, C. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3337870 · Cited by 10
“Several transformer variants were proposed for dense prediction tasks, such as semantic segmentation [58], [59], [60], monocular depth estimation [61], low-level image processing like denoising, super-resolution and deraining [62], [63], and high-resolution image generation [64].”
BackgroundMethodRGB2Point: 3D Point Cloud Generation from Single RGB Images
Lee, J. J., & Beneš, B. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00292 · Cited by 14
“Interestingly, our analysis reveals that more attention heads do not consistently enhance generation performance.”
Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos
Paruchuri, A., Ehrenstein, S., Wang, S., Fried, I., Pizer, S. M., Niethammer, M., & Sengupta, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73411-3_27 · Cited by 13
Monocular Depth Estimation from a Fisheye Camera Based on Knowledge Distillation
Son, E., Choi, J., Song, J., et al. (2023) · Sensors · doi:10.3390/s23249866 · Cited by 10
“The successful performance improvement of ViT [12] has led to the emergence of transformer-based networks in several vision tasks [13–16].”
BackgroundLaPose: Laplacian Mixture Shape Modeling for RGB-Based Category-Level Object Pose Estimation
Zhang, R., Huang, Z., Wang, G., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72698-9_27 · Cited by 10
Wang, X., Zhuang, Y., Cao, X., Huai, J., Zhang, Z., Zheng, Z., & El‐Sheimy, N. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.03.011 · Cited by 12
Pano2Room: Novel View Synthesis from a Single Indoor Panorama
Pu, G., Zhao, Y., & Lian, Z. (2024) · SIGGRAPH Asia Conference Papers · doi:10.1145/3680528.3687616 · Cited by 12
“After the inpainting process, we run the Poisson surface reconstruction [Kazhdan et al. 2006] to close any remaining holes and obtain a watertight mesh.”
Endo3R: Unified Online Reconstruction from Dynamic Monocular Endoscopic Video
Guo, J., Dong, W., Huang, T., Ding, H., Wang, Z., Kuang, H., Dou, Q., & Liu, Y.-H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05114-1_17 · Cited by 10
Self-supervised monocular Depth estimation with multi-scale structure similarity loss
Han, C., Cheng, D., Kou, Q., Wang, X., Chen, L., & Zhao, J. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14012-6 · Cited by 10
“The latest research focuses on multi-task framework-assisted depth estimation, such as semantic segmentation, instance segmentation and other methods, and some researches integrate transformer [27] and Gan [35] network into traditional depth estimation network.”
MethodSpatial-Temporal Data Mining for Ocean Science: Data, Methodologies and Opportunities
Yang, H., Cao, J., Li, W., Wang, S., Li, H., Guan, J., & Zhou, S. (2025) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3748259 · Cited by 16
“Recently, the large models (e.g., BERT [51], GPT [67], and ViT [200]) have received tremendous popularity in computer science, and surpassed many state-of-the-art machine learning models by pre-training on big datasets and fine-tuning for different downstream tasks.”
SenPa-MAE: Sensor Parameter Aware Masked Autoencoder for Multi-satellite Self-supervised Pretraining
Prexl, J., & Schmitt, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-85187-2_20 · Cited by 14
Ahmadi, M., Naeini, A. A., Sheikholeslami, M. M., Arjmandi, Z., Zhang, Y., & Sohn, G. (2023) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case56687.2023.10260361 · Cited by 10
“Recently, It has become a hot topic due to the success of deep learning methods [25, 26] and its various applications ranging from 3D reconstruction [27] to autonomous vehicles [28].”
BackgroundFall-Mamba: A Multimodal Fusion and Masked Mamba-Based Approach for Fall Detection
Zhang, X., Xu, Q., Feng, F., Lu, X., & Xu, L. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3510712 · Cited by 10
“Vision transformer (ViT) [33] was a pioneering work that introduced Transformer into image recognition, demonstrating that by directly processing image patches, Transformer could achieve performance comparable to traditional Convolutional Neural Networks.”
Phan, L., Nguyen, H. T. H., Warrier, H., & Gupta, Y. K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26284-5_13 · Cited by 10
Bharati, V. (2021) · IEEE Sensors Applications Symposium (SAS) · doi:10.1109/sas51076.2021.9530102 · Cited by 10
Dense Uncertainty EstimationPreprint
Zhang, J., Dai, Y., Xiang, M., Fan, D.-P., Moghadam, P., He, M., Walder, C., Zhang, K., Harandi, M. T., & Barnes, N. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.06427 · Cited by 14
GeoRefine: Self-supervised Online Depth Refinement for Accurate Dense Mapping
Ji, P., Yan, Q., Ma, Y., & Xu, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19769-7_21 · Cited by 13
NVDS+: Towards Efficient and Versatile Neural Stabilizer for Video Depth Estimation
Wang, Y., Shi, M., Li, J., Hong, C., Huang, Z., Peng, J., Cao, Z., Zhang, J., Xian, K., & Lin, G. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3476387 · Cited by 18
“Furthermore, these methods can not benefit from well-trained single-image depth predictors [7], [8], [10], [26].”
InfluentialSDNet: mutil-branch for single image deraining using swinPreprint
Tan, F., Kong, Y., Fan, Y., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.15077 · Cited by 10
“…to pixel information and location information in image de-rain tasks, while more attention is paid to semantic information in other advanced tasks such as image classification, so the patch blocks of both VIT [29] and literature [40] are set to 16 × 16 and the size of the location encoding is × .”
BackgroundMind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth Estimation
Talker, L., Cohen, A., Yosef, E., Dana, A., & Dinerstein, M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01009 · Cited by 14
“Mi-DAS [36] and DPT [35] were also trained on this dataset, achieving visually striking depth maps.”
MethodWildlife 3D multi-object tracking
Klasen, M., & Steinhage, V. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101790 · Cited by 10
Semantic Guidance Fusion Network for Cross-Modal Semantic Segmentation
Zhang, P., Chen, M., & Gao, M. (2024) · Sensors · doi:10.3390/s24082473 · Cited by 10
“Recently, vision transformers [35] have been adopted as the backbone in dense prediction tasks [36,37] as well as in semantic segmentation tasks [21,38–40], demonstrating the effectiveness of global receptive fields.”
BackgroundTowards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning
He, J., Wang, Y., Wang, L., Lu, H., Luo, B., He, J.-Y., Lan, J.-P., Geng, Y., & Xie, X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00380 · Cited by 12
“Follow-up methods exploit more powerful network architectures [2, 28, 31, 45] or reformulate the task as a classification problem and predict a fixed [14] or adaptive [2, 34] range for each pixel.”
MethodLandmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection
Zhou, J., Gao, B., Wang, K., Pei, J., Heng, P., & Qin, J. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3574198 · Cited by 10
“To this end, we conduct an ablation study with three cutting-edge monocular depth estimators in our framework, i.e., DPT [50], MiDaS [51], and Depth Anything V2 [52], to validate the impact on registration accuracy.”
Artificial intelligence techniques for stability analysis in modern power systems
Fang, J., & Liu, C. (2024) · iEnergy · doi:10.23919/ien.2024.0027 · Cited by 12
Schieber, H., Young, J., Langlotz, T., Zollmann, S., & Roth, D. (2025) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr59515.2025.00056 · Cited by 11
“After extracting images from the video stream we retrieve segmentation masks using DPT [38].”
Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids
Dong, W. F., Choy, C., Loop, C., Litany, O., Zhu, Y., & Anandkumar, A. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00415 · Cited by 10
“[49] improved the quality and convergence speed of neural field reconstruction on large-scale scenes by incorporating learned geometrical cues like depth and normal estimation [11, 31], however, training and evaluation remain inefficient.”
BackgroundMethodInfluentialTongueMobile: automated tongue segmentation and diagnosis on smartphones
Huang, Z.-H., Huang, W., Wu, H.-C., et al. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08902-5 · Cited by 10
Cross-Scale KNN Image Transformer for Image Restoration
Lee, H., Choi, H., Sohn, K., & Min, D. (2023) · IEEE Access · doi:10.1109/access.2023.3242556 · Cited by 10
“In order to recover fine-grained predictions, IPT [68] and DPT [21] propose an encoder-decoder architecture.”
BackgroundInfluentialHe, Z., Xiao, Z., Chan, K. S., Zuo, Y., Xiao, J., & Lam, K. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887746 · Cited by 11
“SparseNeRF [36] leverages dense depth maps, estimated by a pretrained dense prediction transformer (DPT) [37], to distill local depth ranking priors, promoting spatial continuity.”
The Second Monocular Depth Estimation Challenge
Spencer, J., Qian, C. S., Trescakova, M., Russell, C., Hadfield, S., Graf, E. W., Adams, W. J., Schofield, A. J., Elder, J., Bowden, R., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00308 · Cited by 13
“DJI&ZJU relied on Taskonomy and DIML, Pokemon on ScanNet, SceneNet, NYUD-v2 and more and cv-challenge made use of ZoeDepth [9] pretrained on the DPT dataset collection [66].”
InfluentialDepth-Guided Texture Diffusion for Image Semantic Segmentation
Sun, W., Li, Y., Ye, Q., Jiao, J., & Zhou, Y. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3484490 · Cited by 12
“Following PopNet [3], we use the state-of-the-art DPT model [80] with frozen weights as the depth estimation network, which offers promising source-free depth.”
Papa, L., Mattia, G. P., Russo, P., Amerini, I., & Beraldi, R. (2023) · Sensors · doi:10.3390/s23042223 · Cited by 11
Object-Aware Calibrated Depth-Guided Transformer for RGB-D Co-Salient Object Detection
Wu, Y. S., Liang, L., Zhao, Y., et al. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00196 · Cited by 10
“To ensure the fairness and comparability, as CTNet [3], we adopt DPT [26] to generate corresponding depth maps from CoSOD training dataset DUTS-Class [23] and take them as our training set.”
MethodUVMO: Deep unsupervised visual reconstruction-based multimodal-assisted odometry
Han, S., Li, M., Tang, H., Song, Y., & Tong, G. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110573 · Cited by 11
Decoding human safety perception with eye-tracking systems, street view images, and explainable AI
Kang, Y., Chen, J., Liu, L., et al. (2025) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2025.102356 · Cited by 10
Multi-view Semantic Information Guidance for Light Field Image Segmentation
Li, Y., Cong, R., Wang, S., Zhao, M., Zhang, Y., Li, F., & Sheng, H. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00348 · Cited by 10
“Based on this, some approaches explore the multiscale context relationship [3, 30] or introduce the attention mechanism [7,13,18,31] to improve accuracy.”
BackgroundTransferring Relative Monocular Depth to Surgical Vision with Temporal Consistency
Budd, C., & Vercauteren, T. K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72089-5_65 · Cited by 9
“The most recent MiDaS models [4], the culmination of a series of recent works [19,20], are trained on 1.4 million labelled images.”
BackgroundInfluentialBoosting Semantic Segmentation from the Perspective of Explicit Class Embeddings
Liu, Y., Liu, C., Han, K., Tang, Q., & Qin, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00082 · Cited by 9
From Composited to Real-World: Transformer-Based Natural Image Matting
Wang, Y., Tang, L., Zhong, Y., & Li, B. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3300731 · Cited by 9
“DPT [34] utilizes ViT [32] as an encoder in an encoder-decoder structure and fused features from different stages for final dense prediction.”
MethodDi-NeRF: Distributed NeRF for Collaborative Learning With Relative Pose Refinement
Asadi, M., Zareinia, K., & Sajad Saeedi, G. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3474551 · Cited by 9
“Foreachimage I mi (out of M i images) of robot i , we generate mono-depth sequence D mi , m = 0 , . . . , M i − 1 from input images with an off-the-shelf monocular depth network, i.e., DPT [32].”
MethodHQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality Decoder
Wang, F., & Cheng, J. S. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3312721 · Cited by 11
“…they cannot adaptively capture global and local information in parallel during the decoding stage and adaptively fuse this information into the original feature map (e.g., methods based on pure ViT/CNNs, which model only global [9] or only local [23] information within a feature extraction layer).”
BackgroundMethodInfluentialWeakly Supervised Caveline Detection for AUV Navigation Inside Underwater Caves
Yu, B.-X., Tibbetts, R., Барна, Т. Б., Morales, A., Rekleitis, I. M., & Islam, M. (2023) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros55552.2023.10342435 · Cited by 14
Point-DynRF: Point-based Dynamic Radiance Fields from a Monocular Video
Park, B., & Kim, C. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv57701.2024.00314 · Cited by 9
“Recently, flow-based methods [13, 22, 25, 45] directly regress 4D space-time radiance fields by using additional geometric proxies, such as depth [33] and optical flow [38] estimation networks.”
BackgroundMethodInfluentialDiffusion-Augmented Depth Prediction with Sparse Annotations
Li, J., Wang, Y., Huang, Z., Zheng, J., Xian, K., Cao, Z., & Zhang, J. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611807 · Cited by 12
“We also adopt three different depth predictors [35, 36, 53] and demonstrate the effectiveness of our plug-and-play paradigm.”
BackgroundMethodInfluentialOmniNOCS: A Unified NOCS Dataset and Model for 3D Lifting of 2D Objects
Krishnan, A., Kundu, A., Maninis, K.-K., Hays, J. H., & Brown, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73226-3_8 · Cited by 11
SAOR: Single-View Articulated Object Reconstruction
Aygün, M. M., & Aodha, O. M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00988 · Cited by 10
“Our model is trained using a combination of the following losses, The appearance loss, L appr ( I, ˆ I ) , is an RGB and perceptual loss [71], L depth ( D, ˆ D ) is the translation and shift-invariant depth loss introduced in [46], and L mask ( M, ˆ M ) estimates silhouette discrepancy.”
MethodTransient Stability Assessment of Power Systems Based on the Transformer and Neighborhood Rough Set
Bei, T., Xiao, J., & Wang, X. (2024) · Electronics · doi:10.3390/electronics13020270 · Cited by 9
“Once proposed, the transformer quickly became an important model in the fi eld of natural language processing, and it achieved successful applications in natural language processing [21], computer vision [22], audio processing [23], speech recognition [24], and machine translation [25].”
BackgroundUniAP: Towards Universal Animal Perception in Vision via Few-Shot Learning
Sun, M., Zhao, Z., Chai, W., Luo, H., Cao, S., Zhang, Y., Hwang, J., & Wang, G. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28305 · Cited by 12
Image Super-Resolution Using Dilated Window Transformer
Park, S., & Choi, Y. S. (2023) · IEEE Access · doi:10.1109/access.2023.3284539 · Cited by 11
“…ability of transformer-based networks to model long-range dependencies through self-attention has shown impressive performance in high-level vision tasks, such as image classification [11], [12], [13], [33], object detection [14], [15], [34], [35], [36], and dense prediction [17], [18], [37], [38].”
BackgroundSARFormer: Segmenting Anything Guided Transformer for semantic segmentation
Zhang, L., Huang, W., & Fan, B. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129915 · Cited by 9
Machine Learning Subsystem for Autonomous Collision Avoidance on a small UAS with Embedded GPU
Polosky, N., Gwin, T., Furman, S., et al. (2022) · IEEE Consumer Communications & Networking Conference (CCNC) · doi:10.1109/ccnc49033.2022.9700609 · Cited by 9
“In our knowledge distillation pipeline, we used the MiDaS network developed in [6] as the teacher network.”
BackgroundMethodFlexiCombFE: A flexible, combination-based feature engineering framework for brain tumor detection
Tuncer, I., Hafeez‐Baig, A., Barua, P. D., Hajiyeva, R., Salvi, M., Doğan, Ş., Tuncer, T., & Acharya, U. R. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107538 · Cited by 9
“After that, transformers have dominated the field of computer vision [36,37].”
Contextual HookFormer for Glacier Calving Front Segmentation
Wu, F., Gourmelon, N., Seehaus, T., Zhang, J., Braun, M., Maier, A., & Christlein, V. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3368215 · Cited by 13
“However, for dense prediction tasks, e. g., semantic segmentation, and object detection, which require an effective multiscale feature representation for detecting or classifying objects or regions with varying sizes, a single-scale feature representation would limit the model performance [67].”
BackgroundOpEnCam: Lensless Optical Encryption Camera
Khan, S. S., Yu, X., Mitra, K., Chandraker, M. K., & Pittaluga, F. (2024) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2024.3451953 · Cited by 10
“In the second step, we feed our initial estimate ˆ X T plus measurement Y , and multiplexing mask PSF P to a Dense Prediction Transformer (DPT) [28] , which we train via stochastic gradient descent to produce a refined version ˆ X , given input B = { Y, ˆ X T , P } .”
BackgroundMethodInfluentialDepth-Relative Self Attention for Monocular Depth Estimation
Shim, K., Kim, J.-Y., Lee, G., & Shim, B. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/155 · Cited by 10
Using LiDAR Data as Image for AI to Recognize Objects in the Mobile Robot Operational Environment
Nowakowski, M., Kurylo, J., Braun, J., et al. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-53036-4_9 · Cited by 9
Location-Aware Self-Supervised Transformers for Semantic Segmentation
Caron, M., Houlsby, N., & Schmid, C. (2022) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00019 · Cited by 33
“We follow [19] and train a Dense Prediction Transformer [47] with frozen back-bone on Waymo Open real-world driving dataset [55].”
MethodMonocular Depth Estimation for Drone Obstacle Avoidance in Indoor Environments
Zheng, H., Rajadnya, S., & Zakhor, A. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802577 · Cited by 9
“More specifically, the model was trained on image/label pairs of size (244 × 324) while the output depth-map is of size (62 × 82).”
Collaborative Content-Dependent Modeling: A Return to the Roots of Salient Object Detection
Jiao, S., Goel, V., Navasardyan, S., Yang, Z., Khachatryan, L., Yang, Y., Wei, Y., Zhao, Y., & Shi, H. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3293759 · Cited by 8
“Transformers originally proposed for NLP [50] are being rapidly adapted for computer vision task, including classification tasks [51], [52], [53], [54] and dense prediction tasks [47], [52], [55], [56].”
MethodHu, K., Xie, Z., & Hu, Q. (2023) · Computers & Graphics · doi:10.1016/j.cag.2023.12.015 · Cited by 9
HybridDepth: Robust Metric Depth Fusion by Leveraging Depth from Focus and Single-Image Priors
Ganj, A., Su, H., & Guo, T. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00104 · Cited by 16
“This specific formulation enables training on large datasets, and has been proven to be key to model generalization.”
Layered Depth Refinement with Mask Guidance
Kim, S. Y., Zhang, J., Niklaus, S., Fan, Y., Chen, S., Lin, Z., & Kim, M. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00383 · Cited by 9
“A common approach is to train a deep neural network on RGB-D datasets to learn the non-linear mapping from RGB to depth [20, 31, 32, 47].”
BackgroundMethodResultInfluential3D Scene Inference from Transient Histograms
Jungerman, S., Ingle, A. N., Li, Y., & Gupta, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20071-7_24 · Cited by 9
UC-NeRF: Uncertainty-Aware Conditional Neural Radiance Fields From Endoscopic Sparse Views
Guo, J., Wang, J., Wei, R., et al. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3496558 · Cited by 9
“We obtain the monocular depth from DPT [44] as priors to guide our UC-NeRF.”
Rethinking Attention Mechanisms in Vision Transformers with Graph Structures
Kim, H.-J., & Ko, B. C. (2024) · Sensors · doi:10.3390/s24041111 · Cited by 10
“A vision transformer (ViT) [3], applied in the field of computer vision, is a leading algorithm used in various vision problems, including image classification [3], image segmentation [4], object tracking [5], depth estimation [6], and action recognition [7].”
BackgroundAtmoDist: Self-supervised representation learning for atmospheric dynamics
Hoffmann, S., & Lessig, C. (2023) · Environmental Data Science · doi:10.1017/eds.2023.1 · Cited by 11
“(Ronneberger et al., 2015; Karras et al., 2019, 2020; Ranftl et al., 2021) where features at all scales have to match the target distribution.”
BackgroundTemporally Consistent Online Depth Estimation Using Point-Based Fusion
Khan, N., Penner, E., Lanman, D. R., & Xiao, L. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00880 · Cited by 10
“We use the efficient Hybrid variant of Ranftl et al .’s DPT [32] in all experiments, and the five-layer 3D-GAN version of Zhang et al .’s ST-CLSTM [56] .”
BackgroundMethodResultInfluentialTransVQA: Transferable Vector Quantization Alignment for Unsupervised Domain Adaptation
Sun, Y., Dong, W., Li, X., et al. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3352392 · Cited by 9
“Then, several variants of ViT [5], [34] were proposed and achieved comparable performance over CNN on computer vision tasks.”
MethodCARD: Semantic Segmentation With Efficient Class-Aware Regularized Decoder
Huang, Y., Kang, D., Chen, L., Jia, W., He, X., Duan, L., Zhe, X., & Bao, L. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3395132 · Cited by 10
Gated Stereo: Joint Depth Estimation from Gated and Wide-Baseline Active Stereo Cues
Walz, S., Bijelic, M., Ramazzina, A., Walia, A., Mannan, F., & Heide, F. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01273 · Cited by 12
“The proposed monocular gated network uses a DPT [48]-type architecture and outputs inverse depth bounded in [0 , 1] which results in absolute depth between [1 , ∞ ] .”
BackgroundMethodInfluentialDSC-MDE: Dual structural contexts for monocular depth estimation
Yan, W., Dong, L., Ma, W., Mi, Q., & Zha, H. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110308 · Cited by 10
Tracing unknown tumor origins with a biological-pathway-based transformer model
Xie, J., Chen, Y., Luo, S., Yang, W., Lin, Y., Wang, L., Ding, X., Tong, M., & Yu, R. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100797 · Cited by 9
Resolution-sensitive self-supervised monocular absolute depth estimation
Zhou, Y., Zhang, C., Deng, L., Fu, J., Li, H., Xu, Z., & Zhang, J. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05414-0 · Cited by 13
“Ranftl et al. [18] first applied the Transformer to the field of depth estimation.”
ViTA: Video Transformer Adaptor for Robust Video Depth Estimation
Xian, K., Peng, J., Cao, Z., Zhang, J., & Lin, G. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3309559 · Cited by 9
“…this article are as follows: (cid:2) We propose ViTA, a video transformer adaptor that adapts prior knowledge from the pre-trained image trans-former [16] and incorporates temporal embeddings adaptively to estimate spatially accurate and temporally consistent depth maps from an unconstrained video.”
BackgroundMethodInfluentialGu, X., Tang, W., Han, J., Sangha, V., Liu, F., Gowda, S. N., Ribeiro, A. L. P., Schwab, P., Branson, K., Clifton, L., et al. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01180-5 · Cited by 24
“To accommodate this need, we adopted the dense prediction head proposed by Ranftl et al. 28 , originally designed as a plug-and-play module for the ViT architecture.”
RTS-ViT: Real-Time Share Vision Transformer for Image Classification
Meng, C., Lin, W. J., Liu, B., Zhang, H., Gan, Z., & Ouyang, C. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3525054 · Cited by 13
“This is an important consideration given the impact of pre-training on model performance in downstream tasks [17].”
Learn to Adapt for Self-Supervised Monocular Depth Estimation
Sun, Q., Yen, G. G., Tang, Y. S., & Zhao, C. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3289051 · Cited by 13
“Recently, Ranftl et al. [31] and [32] mix multiple datasets during training and then propose vision transformers for monocular depth estimation, which in turn improve the robustness of the depth estimation model and achieve great results.”
BackgroundXü, K., Wang, W., Deng, X., et al. (2024) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2024.3353784 · Cited by 9
Guo, X., Zhao, H., Shao, S., Li, X., & Zhang, B. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108391 · Cited by 10
CliffPhys: Camera-Based Respiratory Measurement Using Clifford Neural Networks
Ghezzi, O., Boccignone, G., Grossi, G., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73013-9_13 · Cited by 8
“Depth maps are estimated using MiDaS DPT-Large with SwinV2 backbone [1, 39, 40].”
Shyam, P., & Yoo, H. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00030 · Cited by 8
“We leverage the zero-shot depth estimation network MiDAS [51] ( φ ) to generate depth maps for clear images ( I C ) in both synthetic and real datasets.”
MethodPolar Edge Distance Loss in Edge-aware Plug-and-play Scheme for Semantic Segmentation
Yi, J., Zhong, X., Liu, W., et al. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110810 · Cited by 8
Li, T., & Zhang, Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3370821 · Cited by 10
“Many ensuing works utilize convolutional neural networks (CNNs) [7], [8] or Transformers [9], [10] on the basis of the encoder–decoder framework, in which CNNs capture local patterns and spatial information of given images, and Transformer The authors are with the College of Computer Science,…”
BackgroundGlossyGS: Inverse Rendering of Glossy Objects With 3D Gaussian Splatting
Lai, S., Huang, L. Q., Guo, J. J., Cheng, K.-C., Pan, B., Long, X., Lyu, J., Lv, C., & Guo, Y. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3547063 · Cited by 8
“Specifically, we utilize the DINOv2 encoder to capture rich feature representations and pair it with the DPT decoder [61] to perform effective segmentation.”
Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation
Cambrin, D. R., Corley, I., & Garza, P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92387-6_5 · Cited by 21
“The model uses scale-and-shift invariant losses to ensure consistency and gradient matching loss for sharper depth predictions [25].”
BackgroundMethodDMZoomNet: Improving Object Detection Using Distance Information in Intralogistics Environments
Clavero, C., Patricio, M. A., Garcı́a, J., & Molina, J. M. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3381795 · Cited by 9
“Over the past few years, the state-of-the-art deep learning models have gone in the direction of altering the training process to be able to mix multiple datasets (with different depth range and scale) [24], or using trendy transformers based architectures like [25].”
BackgroundTransformer-based vehicle detection for surveillance images
Jin, Z. J., Zhang, Q., Gou, C., Lu, Q., & Li, X. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.5.051602 · Cited by 8
“Ranftl et al.(28) reorganized image-like feature representations at multiple resolutions to enhance fine-grained representation and achieve globally consistent predictions.”
BackgroundTransformer-based monocular depth estimation with hybrid attention fusion and progressive regression
Liu, P., Zhang, Z., Meng, Z., & Gao, N. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129268 · Cited by 8
SOCRATES: Introducing Depth in Visual Wildlife Monitoring Using Stereo Vision
Haucke, T., Kühl, H. S., & Steinhage, V. (2022) · Sensors · doi:10.3390/s22239082 · Cited by 10
“3. it allows to generate training data for monocular depth estimation methods such as Godard et al. (2019); Ranftl et al. (2020, 2021).”
Method
Metadata
- Authors
- Ranftl, R., Bochkovskiy, A., & Koltun, V.
- Affiliations
- Intel Labs
- Intel (United States) (US)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 12159-12168
- OpenAlex
- W3136635488
- Cited by
- 2,373
- References
- 88
- Citations by year
- 2026: 480 · 2025: 607 · 2024: 586 · 2023: 431 · 2022: 225 · 2021: 44
- FWCI
- 104.94
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- TIB Data Manager
- IEEE/CVF International Conference on Computer Vision (ICCV)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- vision transformer, dense prediction, monocular depth estimation, semantic segmentation, convolutional decoder, global receptive field, Pascal Context, NYUv2, KITTI, ADE20K
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