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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“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
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 4×
“We perform a similar experiment with the hybrid architecture in Table 6 (bottom), where R0 and R1 refer to us-ing features from the first and second downsampling stages of the ResNet50 embedding network.”
MethodInfluentialVaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · cited 3×
“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
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
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
Research works printed but not linked: 3
- yuan, 2020, Objectcontextual representations for semantic segmentation, ECCV
- 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 1,301–1,400 of 2,248
TPDepth: Leveraging Text Prompts with ControlNet to Boost Diffusion-based Depth Estimation
Liu, Y., Sun, K., Tang, C., Qian, Y., & Li, X. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755384 · Cited by 4
“However, even with large-scale training, depth estimation models[19, 32, 33, 42, 43] struggle to extract high-level semantic cues from RGB inputs alone, limiting their ability to generalize to challenging cases involving ambiguity or atypical structure.”
From Pixels to Paths: SIGHT - A Vision-Based Navigation Aid for the Visually Impaired
Acar, T., Solmaz, A., Cengiz, I., & Bozkır, A. S. (2024) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/hora61326.2024.10550694 · Cited by 2
“0 [17], Transformer-based backbones are investigated, incorporating variants of ViT [18] into the MiDaS framework to create Dense Prediction Transformers [19, 20] MiDaS employs the features in the image to predict the depth of every pixel in the scene.”
MethodMonoRelief: Recovering 2.5D Relief From a Single Image
Gao, L., Zhang, Y., Wei, M., Liu, H., Chen, Y., Qiu, H., & Zhang, C. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3561361 · Cited by 2
“Omnidata[3]playeda crucial role in advancing the field by synthesizing large-scale training data from various types of 3D sources and combining 2Dand3Ddataaugmenttechniques[35]totraintheDPT-Hybrid network[2],makingitoneoftheleadingmodels.”
TRICKY 2025 Challenge on Monocular Depth from Images of Specular and Transparent Surfaces
Ramirez, P. Z., Costanzino, A., Tosi, F., Poggi, M., Stefano, L. D., Weibel, J.-B., Antensteiner, D., Vincze, M., Busam, B., Zhai, G., et al. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00348 · Cited by 2
“A key milestone was the development of affine-invariant models predicting relative rather than absolute depth [63, 64].”
InfluentialNovel View Synthesis with View-Dependent Effects from a Single Image
Bello, J. L. G., & Kim, 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/cvpr52733.2024.00991 · Cited by 2
“In contrast, we introduce a new rendering pipeline, the NVSVDE-Net, which learns to approximate volumetric rendering for estimating novel views and view-dependent effects (VDE) from videos without requiring camera pose or depth labels.”
MethodStableDepth: Scene-Consistent and Scale-Invariant Monocular Depth
Zhang, Z., Yang, L., Yang, T., Yu, C., Guo, X., Lao, Y., & Zhao, H. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00664 · Cited by 1
“Video Depth Anything freezes the Depth Anything V2 encoder and incorporates cross-frame attention in the DPT [40] decoder for temporal consistency, though these methods still produce depth with inherent shift ambiguity.”
InfluentialGan, J., Chen, H., Zhuang, Z., Xiong, J., Xu, W., & Chen, Z. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00296-9 · Cited by 1
“Zhou et al. (2023) utilized an inception model that contains a convolutional block attention module (CBAM) for multi scale local feature extraction and a vision transformer (ViT) (Ranftl et al. 2021) to capture global features, fusing local and global features.”
MODepth: Benchmarking Mobile Multi-frame Monocular Depth Estimation with Optical Image Stabilization
Lu, Y. Y., Pan, H., Ding, D., Ding, J., Fu, Y., Chen, Y., Ren, J., & Xue, G. (2025) · SIGGRAPH Asia Conference Papers · doi:10.1145/3757377.3763991
ScaleLSD: Scalable Deep Line Segment Detection Streamlined
Ke, Z., Tan, B., Zheng, X., et al. (2025) · 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/cvpr52734.2025.00593 · Cited by 1
“Our network architecture is simple, follows the best practices of vision transformers for dense predictions [34].”
A Survey of Deep Learning Technology in Visual SLAM
Meng, H., & Lu, H. (2024) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc61514.2024.10592584 · Cited by 4
“Ren´e et al. [41] and Guanglei et al. [42]proposed a depth prediction framework based on transformer.”
BackgroundComparison of Network Architectures for Semantic SegmentationPreprint
Boyadzhiev, T. (2024) · Research Square · doi:10.21203/rs.3.rs-4512461/v1 · Cited by 1
WAHNet: A Wave‐Attention Hybrid Network for Surface Wave Suppression in Seismic Data
Gao, L., Zheng, S., & Min, F. (2026) · Geophysical Prospecting · doi:10.1111/1365-2478.70151 · Cited by 1
Zhang, C., Clarkson, M. J., & Islam, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13961-0_30 · Cited by 1
Liu, Y., Yu, D., Liu, L., Xiao, Y., & He, T. (2025) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc66358.2025.11200278 · Cited by 1
“The encoder is initialized with pre-trained weights from DepthAnything (DA) [12], while the decoder incorporates a Dense Prediction Transformer (DPT)-style multi-scale fusion head [28] to enhance the extraction of mucosal texture.”
High-Performance Image Restoration with SpectraNet: A Lightweight CNN Architecture
Patnaik, N., Raj, R., Singh, P. K., et al. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.480 · Cited by 1
Wang, X., He, Z., Zhang, Q., et al. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3535340 · Cited by 1
“To achieve zero-shot cross-dataset metric depth inference, Wu et al. [29] devised DistDepth to perform self-supervised structural distillation of DPT [19] using stereo image pairs.”
Karimi, F., Mehrpanah, A., & Rawassizadeh, R. (2024) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2024.104784 · Cited by 2
Adapting Segment Anything Model for self-supervised monocular depth estimation
Zhang, D., Wang, C., & Fu, Q. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.6.063045 · Cited by 1
“Thanks to the capability of robust long-range correlation modeling under the global sensory field, Vision Transformer (ViT) has been applied to monocular depth estimation frameworks 12,13 to obtain better depth results.”
Token Sparsification for Faster Medical Image Segmentation
Zhou, L., Liu, H., Bae, J., He, J., Samaras, D., & Prasanna, P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34048-2_57 · Cited by 2
On the Viability of Monocular Depth Pre-training for Semantic Segmentation
Lao, D., Yang, F., Wang, D., Park, H., Lu, S., Wong, A., & Soatto, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72913-3_19 · Cited by 10
“…The first involves directly pre-training within the specific domain of interest, leveraging the simplicity of data gathering; The second way is scaling up pre-training by incorporating diverse sources of data, which is made possible by recent developments [40,48,62] on relative depth estimation.”
BackgroundMethodInfluentialEvent Camera Monocular Depth Estimation: An Event-Tailored State-Space Model-Based Approach
Wang, J., Liuyi, L., Shao, W., & Wang, L. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3659681 · Cited by 1
“Besides, we evaluate two frame-based approaches: DPT [52] and a Baseline combining Vim [35] with ConvLSTM [17] since both methods process event images through generic vision backbones without event-specific adaptation.”
Densely activated self-attention for semantic segmentation
Xiao, L., Liu, W., Wang, Z., et al. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111920 · Cited by 1
SHARE: Scene-Human Aligned Reconstruction
Li, J., Chharawala, B., Shu, C., et al. (2025) · SIGGRAPH Asia Technical Communications · doi:10.1145/3757376.3771393 · Cited by 1
UniDense: Unleashing Diffusion Models with Meta-Routers for Universal Few-Shot Dense Prediction
Dong, L., Zhai, W., & Zha, Z.-J. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680831 · Cited by 1
“Specifically, we customize MoE attention layers and MoE MLP layers within the transformer blocks of the DPT-Base model [26] to create a MoE ViT model.”
Enhanced Monocular Depth Estimation Based on Uncertainty Edge Weighting Mask
Kim, Y., & Kim, B. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010036 · Cited by 1
Analyzing Results of Depth Estimation Models with Monocular Criteria
Theiner, J., Nommensen, N., Rhotert, J., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00385 · Cited by 1
“We apply several pre-trained state-of-the-art models 1 including Dense Prediction Transformers (DPT) [17] with cross-dataset training ( MiDaS ) [18] and different back-bones ( SwinFormer, BEiT, ResNet ), namely DPT Large , midas DPT SwinV2 L 384 , midas DPT BEiT L 512 .”
MethodPathoCellBench: A Comprehensive Benchmark for Cell Phenotyping
Lüscher, J., Koreuber, N., Franzen, J., Reith, F. H., Winklmayr, C., Baumann, E., Schürch, C. M., Kainmueller, D., & Rumberger, J. L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_39 · Cited by 1
CH 3 Depth: Efficient and Flexible Depth Foundation Model with Flow Matching
Li, J., Wang, Y., Zheng, J., Zhang, J., Shen, L., Liu, T., & Cao, Z. (2025) · 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/cvpr52734.2025.00677 · Cited by 1
“MiDaS [34] and DPT [35] achieve generalized depth estimation through a multi-dataset training strategy.”
Accurate and fast monocular endoscopic depth estimation of structure-content integrated diffusion
Tan, M., Tao, Y., Zheng, B., Xie, G., Xia, Z., & Xiong, J. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102640 · Cited by 1
Depth Infused Binaural Audio Generation using Hierarchical Cross-Modal AttentionPreprint
Parida, K. K., Srivastava, S., Matiyali, N., & Sharma, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.04906 · Cited by 1
Samarin, A., Savelev, A., Toropov, A., Dozortseva, A., Kotenko, E., Nazarenko, A., Motyko, A., Narova, G., Mikhailova, E., & Malykh, V. A. (2025) · Journal of Imaging · doi:10.3390/jimaging11100359 · Cited by 1
Jun, J., Lee, J.-H., & Kim, C. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104252 · Cited by 1
Arısan, A., & Duran, G. S. (2025) · Sensors · doi:10.3390/s25216512 · Cited by 1
He, J., Suolang, D., Fu, K., & Zhao, Q. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116509 · Cited by 1
Shedding Light on Depth: Explainability Assessment in Monocular Depth Estimation
Cirillo, L., Schiavella, C., Papa, L., Russo, P., & Amerini, I. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228948 · Cited by 1
“Nowadays, MDE is addressed through deep learning models, particularly Convolutional Neural Networks (CNNs) [3] [4], and Vision Transformers (ViTs) [5] [6].”
Kashevnik, A. M., & Ali, A. (2023) · doi:10.3390/engproc2023033028 · Cited by 1
“We used the methods presented in the following papers [8,9] to annotate our dataset for the training of the neural network architecture presented in this paper.”
MethodUnsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer
Liu, J., Zhang, Q., Li, X., Li, J., Wang, G., Lu, M., Huang, T., & Zhang, S. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610511 · Cited by 7
Ly, V.-T., Truong, H. M., & Nguyen, X.-H. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00577 · Cited by 1
Learning with Geometric Priors in U-Net Variants for Polyp Segmentation
Vazquez, F., Nuñez, J. A., Adame, D., Moreno, A., Zhan, A., Li, H., Yang, J., Tang, H., Fu, B., & Gu, P. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515449 · Cited by 1
“A lightweight camera head outputs camera parameters, while a DPT head [14] produces dense per-frame predictions (depth/point maps and tracking features).”
Wei, R., Chen, K., Ma, Y., Li, B., Liu, Y., & Dou, Q. (2026) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-026-03591-6 · Cited by 1
“Furthermore, combining depth estimates obtained at multiple resolutions was explored to enhance the quality of monocular depth, without requiring complex network architectures [13] or auxiliary tasks [14, 15].”
A Deep Joint Network for Monocular Depth Estimation Based on Pseudo-Depth Supervision
Tan, J., Gao, M. Z., Duan, T., & Gao, X. (2023) · Mathematics · doi:10.3390/math11224645 · Cited by 1
Deep Learning-Based Monocular Depth Estimation Method for Forklift Collision Avoidance
Kim, D.-J., Lee, C.-Y., Kim, H., et al. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-84148-4_8 · Cited by 1
Infrared Monocular Depth Estimation Based on Radiation Field Detail Enhancement
Hao, R., Xu, C., & Zhong, C. (2025) · IEEE Access · doi:10.1109/access.2025.3574932 · Cited by 1
“Compared to stereo or multi-view systems, monocular depth estimation has attracted significant attention from both academia and industry (Intel Labs [5], Google DeepMind [6], Toyota Research Institute [7]) due to its hardware cost efficiency, compact form factor, and ease of deployment.”
Height-Assisted Semi-Supervised Building Footprint Extraction From Optical Remote Sensing Images
Huang, W., Shi, Y., Xiong, Z., et al. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3642212 · Cited by 1
“…experiments utilize three advanced segmentation models to evaluate the adaptability of Height-Match across di ff erent architectures: one convolutional neural network (CNN), ResNet50-based DeepLabv3 + [56], [57], and two vision transformers (ViTs), SegFormer B2 [58] and DINOv2-Small-based DPT [59].”
A Simple yet Effective Test-Time Adaptation for Zero-Shot Monocular Metric Depth Estimation
Marsal, R., Chapoutot, A., Xu, P., & Filliat, D. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247168 · Cited by 11
“First, an affine-invariant disparity map d is predicted from an image with a neural network such as Depth Anything [2] while in parallel a sensor is used to estimate a set of 3D points P .”
BRUM: Robust 3D Vehicle Reconstruction from 360° Sparse Images
Nucci, D. D., Tomei, M., Borghi, G., et al. (2025) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv64158.2025.11097621 · Cited by 1
“To ensure fair comparison, SplatFields is evaluated using GT camera poses, while DNGaussians employs also GT depth maps directly, bypassing the DPT [48] computation suggested in the original work.”
Simões, D. S., Carvalho, A. E., & Portugal, D. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-15812-3_9 · Cited by 1
Cha, J., Cho, D., & Lee, S. J. (2025) · doi:10.1109/embc58623.2025.11254375 · Cited by 1
“Depth-Anything-V2 is a recently prominent monocular depth estimation DNN that predicts high-quality depth from various images, adopting DPT [20] as the decoder and DinoV2 [22] as the encoder.”
Gerats, B. G. A., Wolterink, J. M., & Broeders, I. A. M. J. (2024) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-024-03261-5 · Cited by 1
“Recently, several of these estimators were proposed that achieve good results [23, 25, 26].”
Lee, C. Y., Kim, D. J., Suh, Y.-J., & Hwang, D. K. (2024) · IEEE Access · doi:10.1109/access.2024.3523912 · Cited by 1
Cross-scale sampling transformer for semantic image segmentation
Ma, Y., Yu, L., Lin, F., & Tian, S. (2022) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-220976 · Cited by 1
“Recently,Transformer has received more and more attention on semantic segmentation [11, 45, 48, 57, 58, 66].”
InfluentialHTEB: Hybrid Transformer Enhancement Block for Robust Image Flare Suppression
Mostafa, M. Z., Elmongui, H. G., & Torki, M. (2025) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa66935.2025.11315142 · Cited by 1
“First, a Dense Vision Transformer (DPT) [1], [2] estimates a depth map from the input RGB image, leveraging its ability to model geometric relationships critical for distinguishing depth-agnostic flare artifacts from scene content [3].”
Depth-Aware Guidance with Self-Estimated Depth Representations of Diffusion ModelsPreprint
Kim, G., Jang, W., Lee, G., et al. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4498132 · Cited by 1
PanoDthNet: Depth Estimation Based on Indoor and Outdoor Panoramic Images
Cai, J., Xu, J., Chang, Q., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8502-5_4 · Cited by 1
Gaussian splashing enables direct volumetric rendering underwater
Mualem, N., Amoyal, R., Freifeld, O., & Akkaynak, D. (2026) · Scientific Reports · doi:10.1038/s41598-026-56917-4 · Cited by 1
Depth Estimation Using Monocular Camera for Real-World Multi-Object Grasp Detection for Robotic Arm
Jain, V., Anunay, A., Srivastava, A., et al. (2023) · doi:10.1145/3634865.3634869 · Cited by 1
Fundus-Based RNFL Quadrant Map Generation Using Depth Estimation
Sangchocanonta, S., Patchimnan, K., Pooprasert, P., Munthuli, A., Puangarom, S., Phienphanich, P., Itthipanichpong, R., Ratanawongphaibul, K., Chansangpetch, S., Manassakorn, A., et al. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782592 · Cited by 1
L4P: Towards Unified Low-Level 4D Vision Perception
Badki, A., Su, H., Wen, B., & Gallo, O. (2026) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv69130.2026.00011 · Cited by 1
“We adopt the DPT [47] architecture for dense prediction due to its proven performance and efficiency on single-image depth estimation.”
InfluentialDivAvatar: Diverse 3D Avatar Generation with a Single Prompt
Tao, W., Lei, B., Liu, K., et al. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00255 · Cited by 1
“This clear delineation simplifies the process of identifying individual body parts, rendering our semantic zoom approach for each part an intuitive solution.”
Selective multi-scale depth estimation with liquid lens and scanning mirror
li, C., Xing, H., Zheng, K., Yang, W., Hao, Q., & Cheng, Y. J. (2025) · Measurement · doi:10.1016/j.measurement.2025.120063 · Cited by 1
Joint Optimization of Neural Radiance Fields and Continuous Camera Motion from a Monocular Video
Nguyen, H. C., Mao, W., Álvarez, J. M., et al. (2025) · 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/cvpr52734.2025.01071 · Cited by 1
“NoPe-NeRF [2] tackles this by leveraging a pre-trained depth network [23].”
Merizzi, F., & Loukos, H. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12066-3 · Cited by 2
GPA: Enhancing Generalizable Physical Adversarial Attacks Across Multiple Vision Tasks
Xie, M., Xiang, S., Ruan, J., Gao, X., Yu, Z., Liu, T., & Fu, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888416 · Cited by 1
“In this section, we evaluate GPA and other methods against models such as MiDaS [25] and Metric3D [26].”
Tan, S. H., Zhang, X., Han, L., Huang, H., & Wang, H. (2025) · Journal of Remote Sensing · doi:10.34133/remotesensing.0880 · Cited by 3
EDE-Distill: Boosting Event-Based Monocular Depth Estimation Performance via Knowledge Distillation
Hu, C., Jiang, J., Li, Y., et al. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3583486 · Cited by 1
“Recently, deep learning-based MDE methods using conventional cameras [4] have achieved substantial advancements.”
Contrastive Feature Bin Loss for Monocular Depth Estimation
Song, J., & Hyun, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3551435 · Cited by 1
“In monocular depth estimation, transformer-based models pre-trained on ImageNet [4], such as [1], [15], [16], [17], [23], and [29], demonstrate remarkable performance.”
Pooling Pyramid Vision Transformer for Unsupervised Monocular Depth Estimation
Zhang, Q., Wei, C., Li, Q., Tian, X., & Li, C. (2022) · IEEE International Conference on Smart Internet of Things (SmartIoT) · doi:10.1109/smartiot55134.2022.00025 · Cited by 1
“Then, we change the backbone of Mondepth2 from ResNet18 to ResNet50 for comparison with PPViT18 and fine-tune the DPT [21] to adapt the same unsupervised training mode and loss function.”
BackgroundMethodInfluentialTask Indicating Transformer for Task-Conditional Dense Predictions
Lu, Y., Sirejiding, S., Bayramli, B., et al. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10445743 · Cited by 1
“Consequently, the produced representations span from suited for dense vision tasks [18, 19, 20].”
BackgroundAugmentation-Based Approaches for Overcoming Low Visibility in Street Object Detection
Novo, J. P., Goulão, M., Bandeira, L., et al. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00294 · Cited by 1
“In order to generate the depth map image, MiDaS (Monocular depth estimation method) [8] [9], more specifically Midas-DPT Large, a Dense Prediction Trans-former, was used, which due to its computing requirements had to be performed offline.”
MethodVistaDepth: Improving Far-Range Depth Estimation With Spectral Modulation and Adaptive Reweighting
Zhan, M., Zhang, L., Wang, Y., Chu, X., Wang, B., & Zhang, Y. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3679288 · Cited by 2
Deformable Cross Attention for Learning Optical Flow
Abdein, R., Xiang, X., Lv, N., et al. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094728 · Cited by 1
Zhu, X., Li, S., Bi, H., et al. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3597026 · Cited by 1
“…of the XZMH-Choroid Dataset: We compare the MAMFF-Net with other deep learning methods on the XZMH-Choroid dataset: U-Net [13], PSPNet [32], DeepLabv3 + [33], HRNet [34], Segformer [30], Dpt [35], Segnext [36], Convnext [37], DDRNet [38], Biformer [39], RepVit-SAM [40], Transnext [29], FADC [41].”
Color Correction of Deep Underwater Images Using Depth Maps for 3D Scene Reconstruction
Berk, J. N., Gerdes, J., Wechsler, S., Hess, M., Bilinski, M., Wasimi, A., & Provost, R. C. (2025) · doi:10.23919/oceans59106.2025.11245014 · Cited by 1
“The large BEiT-DPT model [24] we used leverages a vision transformer as the backbone, while also using a BEiT model to learn contextual image representations through self-supervised pretraining [3].”
Unleashing the Power of Pretrained Transformer for Dense Prediction in Physiological Signals
Hu, Q., Wang, D., Wu, H. K., et al. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3592687 · Cited by 1
“Existing dense prediction architectures in physiological signals generally adopt an encoder-decoder paradigm, with UNet being the most widely used [13], [14].”
Fso-Vo: Visual Odometry Based on Dense Optical Flow Prediction and Sequence OptimizationPreprint
Liu, G., Zhang, S., Gao, Q., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5134457 · Cited by 1
Fully Convolutional Transformer with Local-Global Attention
Lee, S., Yi, E., Lee, J., et al. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981339 · Cited by 1
“However, the design change of integrating convolution also led to improvements in dense prediction tasks such as detection [4], segmentation [9], and depth estimation [10], as such tasks would require features that encode both local and global information.”
BackgroundResultHierarchical Weighting Network with Depth Cues for Real-Time Video Salient Object Detection
Mengnan, X., Pan, G., Zhang, Z., et al. (2022) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip56608.2022.10016560 · Cited by 1
“Therefore, we use the depth estimation network DPT [1] to generate depth images from both the DAVIS and DAVSOD datasets in the current VSOD dataset, as shown in fig.”
MethodInfluentialAre Multi-view Edges Incomplete for Depth Estimation?
Khan, N., Kim, M. H., & Tompkin, J. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-023-01890-y · Cited by 1
“These include photometric stereo [10], shape from shading [11], depth from defocus [12, 13], active illumination [10, 14– 17], and deep-learning-based methods including monocular settings [18–21].”
Karthikeyan, N., Natarajan, B., Mahapatro, S. R., & Hussain, D. M. (2026) · Scientific Reports · doi:10.1038/s41598-025-32610-w · Cited by 1
DualMIL-ViT: A Dual-Attention Multiple-Instance Vision Transformer for Diabetic Retinopathy Grading
Hu, S., & Okuwobi, I. P. (2026) · Sensing and Imaging · doi:10.1007/s11220-026-00776-w · Cited by 1
Transformer-Based Feature Extraction and Optimized Deep Neural Network for Gastric Cancer Detection
Uçar, E. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01699-w · Cited by 1
Yang, Z., Cheng, A., Zhu, H., Li, T., Tao, P. Y., & Mao, K. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00841 · Cited by 2
“Single-scale 1026 distillation improves these metrics to an ε acc of 1.05 and 1027 an RMSE of 0.078 m by leveraging the teacher’s knowl-1028 edge at the final scale, though it struggles to capture de-1029 tails at intermediate resolutions.”
InfluentialHong, C., Wang, Z., Ma, Z., et al. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3677871 · Cited by 1
“Recent trends in lane detection have been driven by two key developments: the successful adaptation of transformer architectures to vision tasks [48], [49], [50], [51], [52], and the growing adoption of bird’s-eye-view (BEV) representations in autonomous driving.”
Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentation
Porta, H., Dalsasso, E., Marcos, D., & Tuia, D. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00284 · Cited by 2
“This expansion was driven by its increasing performance on a multitude of tasks and benchmarks, often coinciding with an increase in model complexity [11, 64, 86, 89].”
Can Language Really Understand Depth?
Chen, F., & Lu, Y. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8178-6_29 · Cited by 1
NeRF-SDP: Efficient Generalizable Neural Radiance Field with Scene Depth Perception
Wang, Q., Guo, S., Wu, H., Xie, R., Song, L., & Zhang, W. (2023) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3595916.3626380 · Cited by 1
“We inherit a pre-trained DPT [26] model for monocular depth estimation of the reference views.”
MethodLeOp-GS: Learned Optimizer With Dynamic Gradient Update for Sparse-View 3DGS
Lei, X., Wang, X., Liu, L., Li, H., & Zhang, H. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3616156 · Cited by 1
“We use the estimated depth [64] as the ground truth.”
Towards Large-Scale Video-Based Highway Traffic Monitoring
Spoecklberger, J., Micorek, J., Possegger, H., & Bischof, H. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10421876 · Cited by 1
“More recently, attention modules have been leveraged for depth estimation [1], [25].”
BackgroundChoudhry, V., Shekhar, S., Ratish, R. A., et al. (2026) · IEEE Access · doi:10.1109/access.2026.3670397 · Cited by 1
“Monodepth2 [13] learns depth from reprojection consistency, while transformer-based methods such as AdaBins [8] and DPT [14] employ attention [15] to capture long-range dependencies.”
Enhancing Salient Object Detection in RGB-D Videos via Tri-modal Complementary Fusion
Yang, C., Wang, A., Ren, C., & Yun, S. (2025) · The Visual Computer · doi:10.1007/s00371-025-04239-3 · Cited by 2
“The optical flow map is rendered using RAFT[44], while the depth map is generated using DPT[45].”
Chen, Y., Yin, Q., Zhao, L., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-36755-0 · Cited by 1
“…factors: first, representational limitations in the feature extraction stage—the fixed-size patch embedding mechanism in visual Transformers 9,10 tends to mix small-scale distant targets with background information, weakening their independent representations; while CNNs excel at capturing…”
SwiftDepth++: An Efficient and Lightweight Model for Accurate Depth Estimation
Dayoub, Y., & Makarov, I. (2024) · Doklady Mathematics · doi:10.1134/s1064562424602038 · Cited by 1
“Additionally, transformer architectures have been integrated into depth networks [28, 38].”
MIMIC: Masked Image Modeling with Image Correspondences
Marathe, K., Bigverdi, M., Khan, N., Kundu, T., Howe, P., Sharan Ranjit, S., Bhattad, A., Kembhavi, A., Shapiro, L. G., & Krishna, R. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00076 · Cited by 1
“We use DPT (Ranftl et al., 2021) head as in MultiMAE for finetuning.”
MethodVisiPrint: Previewing 3D-Print Appearance from Real Material Samples
Perroni-Scharf, M., Faruqi, F., Ahn, S., Hernandez, R., Rusinkiewicz, S. M., Freeman, W. T., & Mueller, S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790851
“Depth is estimated with DPT [64], while grayscale masking preserves illumination.”
He, W., Yan, Y., Tang, S., Deng, Y., Zhong, Y., Luo, P., & Qi, D. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128694 · Cited by 1
“By transforming RGB images into the frequency domain and emphasizing their low-frequency components, we obtain a rough segmentation representation and pseudo-depth maps labeled by [25].”
Lin, J., Chen, J., Guo, L., et al. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109498 · Cited by 1
Few-shot Pose-free View Synthesis with 3D Gaussian Splatting
Huang, Y., & Xu, C. Y. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10865686 · Cited by 1
“We produce monocular depth maps as ground truth depth for interpolated views, using the pre-trained Dense Prediction Transformer (DPT) [29].”
Abbariki, M., & Shoman, M. (2025) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw65960.2025.00076 · Cited by 1
Design and Implementation of Smart Infrastructures and Connected Vehicles in a MiniCity Platform
Vargas, D., Haque, E., Carroll, M., Perez, D., Roman, T., Nguyen, P., & Habibi, G. (2024) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc58415.2024.10919892 · Cited by 1
“After extracting the required data, different depth estimation algorithms were evaluated and compared: DPT (Dense Prediction Transform) [13], Depth Anything [14], and Marigold [15].”
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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