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
Cites
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“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
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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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- 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 1,001–1,100 of 2,248
Reduction of Class Activation Uncertainty With Background Information
Kabir, H. M. D. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3570282 · Cited by 27
“SOTA performances with significant improvement over traditional CNNs [59], [60].”
Few-Shot Novel View Synthesis Using Depth Aware 3D Gaussian Splatting
Kumar, R., & Vats, V. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91989-3_1 · Cited by 4
National-Scale Detection of New Forest Roads in Sentinel-2 Time Series
Trier, Ø. D., & Salberg, A.-B. (2024) · Remote Sensing · doi:10.3390/rs16213972 · Cited by 4
RoSe: Robust Self-Supervised Stereo Matching Under Adverse Weather Conditions
Wang, Y., Hu, J., Hou, J., Zhang, C., Yang, R., & Wu, D. O. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3622186 · Cited by 4
“For volume-free methods, CroCo-Stereo [10] uses an encoder-decoder transformer for cross-view completion and refines disparity by incorporating a dense prediction transformer head [28].”
BokehMe++: Harmonious Fusion of Classical and Neural Rendering for Versatile Bokeh Creation
Peng, J., Cao, Z., Luo, X., et al. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3501739 · Cited by 4
EMTNet: efficient mobile transformer network for real-time monocular depth estimation
Yan, L., Yu, F., & Dong, C. (2023) · Pattern Analysis and Applications · doi:10.1007/s10044-023-01205-4 · Cited by 4
“Furthermore, we use the DPT [21] decoder to restore resolution and fuse multi-scale depth information to produce high-resolution depth maps.”
MethodDeep Reinforcement Learning with Omnidirectional Images: application to UAV Navigation in Forests
Artizzu, C.-O., Allibert, G., & Demonceaux, C. (2022) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv57592.2022.10004267 · Cited by 4
“In recent years, monocular depth estimation in perspective images has seen significant progress, reaching a very high accuracy [6], [7], [8].”
BackgroundSPDepth: Enhancing Self-Supervised Indoor Monocular Depth Estimation via Self-Propagation
Guo, X., Zhao, H., Shao, S., et al. (2024) · Future Internet · doi:10.3390/fi16100375 · Cited by 4
GlobalDepth: A Global Information Aggregation Network for Depth Estimation
Li, G., Zhi, Z., & Ling, B. W. (2024) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2024.3354068 · Cited by 4
Reyes-Angulo, A. A., Paheding, S., Rajaneesh, A., Sajinkumar, K. S., & Oommen, T. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00041 · Cited by 5
“With recent advancements in DL algorithms, several studies have achieved state-of-the-art results in computer vision tasks such as image classification [14, 49], object detection [18, 33], and semantic segmentation [11, 39].”
Just Noticeable Visual Redundancy Forecasting: A Deep Multimodal-Driven Approach
Xie, W., Wang, S., Tian, S., Huang, L., Liu, Y., & Wang, M. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25399 · Cited by 4
Exploring Vision Transformer Layer Choosing for Semantic Segmentation
Lin, F., Ma, Y., & Tian, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096645 · Cited by 4
“Method Backbone GFLOPs Params SS MS DPT[17] ViT-L/16 328 338M 49.”
MethodLFM-3D: Learnable Feature Matching Across Wide Baselines Using 3D Signals
Karpur, A., Perrotta, G., Martin-Brualla, R., et al. (2024) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00079 · Cited by 4
“Despite the scale ambiguity problem, MDE models can be trained on massive amounts of data from varying image domains to produce robust relative depth estimates [33, 34].”
BackgroundMethodInfluentialAnuar, M. F. A. M. S., Zaman, F. H. K., Abdullah, S. A. B. C., Ng, K. M., Vijyakumar, K. N. A.-L., & Ng, S. K. (2026) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2025.3650561 · Cited by 4
“The Dense Prediction Transformer (DPT) [57] utilizes a ViT-based encoder to retain high-resolution feature representations and a global receptive field throughout the network.”
Image–text aggregation for open-vocabulary semantic segmentation
Cheng, S., Huang, J., Wang, X., Huang, L., & Wei, Z. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129702 · Cited by 5
Scalable Autoregressive Monocular Depth Estimation
Wang, J., Liu, J., Tang, D., Wang, W., Li, W., Chen, D., Chen, J., & Wu, J. (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/cvpr52734.2025.00587 · Cited by 4
“…0.994 AdaBins [4] E-B5+mini-ViT 40M 0.103 0.364 - 0.044 0.903 0.984 0.997 P3Depth [38] ResNet-101 45M 0.104 0.356 - 0.043 0.904 0.988 0.998 DPT [44] VIT-L 343M 0.110 0.357 0.045 0.904 0.988 0.998 LocalBins [5] E-B5 30M 0.099 0.357 - 0.042 0.907 0.987 0.998 NeWCRFs [64] Swin-Large 270M 0.095…”
InfluentialV2Depth: Monocular Depth Estimation via Feature-Level Virtual-View Simulation and Refinement
Wu, Z., Li, Z., Fan, Z.-G., Wu, Y., Pu, J., & Li, X. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611751 · Cited by 4
“Moreover, the dense prediction transformer (DPT) was proposed [63] that leverages the vision transformer (ViT) [12] as the backbone for accomplishing global-aware feature extraction.”
BackgroundTowards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation
Chakraborty, N., Fang, Y., Schreiber, A., Ji, T., Huang, Z., Mihigo, A., Wall, C., Almana, A., & Driggs-Campbell, K. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128424 · Cited by 7
“Later advancements also involved using the Vision Transformer [44] as an encoder instead of a CNN [45] to provide greater global image context and improved prediction accuracy.”
Monocular Road Planar Parallax Estimation
Yuan, H., Chen, T., Sui, W., Xie, J., Zhang, L., Li, Y., & Zhang, Q. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3289323 · Cited by 4
“IV, the proposed RPANet outperforms the depth estimation networks both with CNN (ResNet-18 [58]) and transformer (DPT-Hybrid [57]) backbone in absolute relative error but lags behind on square-based errors.”
BackgroundMethodInfluentialUniss-MDF: A Multidimensional Face dataset for assessing face analysis on the move
Ruiu, P., Cadoni, M. I., Lagorio, A., Nixon, S., Casu, F., Farina, M., Fadda, M., Trunfio, G. A., Massimo Tistarelli, A., & Grosso, E. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104384 · Cited by 4
Towards Monocular Neural Facial Depth Estimation: Past, Present, and Future
Khan, F., Farooq, M. A., Shariff, W., Basak, S., & Corcoran, P. (2022) · IEEE Access · doi:10.1109/access.2022.3158950 · Cited by 5
“The performance of the facial depth estimation model LapDepth [87] is compared to the SoA models (i.e., MiDaS [90], DPT [91], and BTS [89]) on the synthetic human facial dataset in Fig.”
MethodCRISP: Object Pose and Shape Estimation with Test-Time Adaptation
Shi, J., Talak, R., Zhang, H., Jin, D. S., & Carlone, L. (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/cvpr52734.2025.01087 · Cited by 4
“We use a Dense Prediction Trans-former (DPT) to estimate the PNC [37].”
Lai, S., Cheng, Z., Zhang, W., et al. (2025) · Applied Sciences · doi:10.3390/app15147714 · Cited by 4
Schiavella, C., Cirillo, L., Papa, L., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-51023-6_32 · Cited by 4
Glaucoma Detection Based on Joint Optic Disc and Cup Segmentation Using Dense Prediction Transformer
Wiguna, D. I. C., Rachmawati, E., & Kosala, G. (2023) · International Conference on Information Technology, Computer, and Electrical Engineering · doi:10.1109/icitacee58587.2023.10277656 · Cited by 4
“In particular, DPT uses ViT as the backbone [10].”
MethodDemocratizing interactivity: An overview of interfaces for multimedia machine learning
Kopiler, A. A., Schardong, G. G., Schirmer, L., Perazzo, D., Novello, T., & Velho, L. (2025) · Computers & Graphics · doi:10.1016/j.cag.2025.104412 · Cited by 4
FewarNet: An Efficient Few-Shot View Synthesis Network Based on Trend Regularization
Song, C., Wang, S., Wei, J. J., et al. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3395447 · Cited by 4
“Inspired by SparseNeRF [52], we introduced the monocular depth estimation ViT model DPT [56] to constrain depth.”
BackgroundMethodDai, D., & Xing, Y. (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/cvpr52734.2025.01538 · Cited by 4
“NeRF [22] is a typical example of Implicit Neural Representation (INR), where scene details are embedded within a neural network, offering high resolution and compactness.”
HPD-Depth: High performance decoding network for self-supervised monocular depth estimation
Wu, L., Wang, L., Wei, G., & Yu, Y. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105360 · Cited by 5
Wen, Y., Song, L., Liu, Y., et al. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127897 · Cited by 4
“We use DPT [34] to compute the inverse depth D , represented as a grayscale image.”
Depth Estimation from Indoor Panoramas with Neural Scene Representation
Chang, W., Zhang, Y., & Xiong, Z. (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/cvpr52729.2023.00093 · Cited by 4
Wu, X., Wang, J., Wang, J., et al. (2025) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2025.3606045 · Cited by 4
Attention-Guided Masked Autoencoders for Learning Image Representations
Sick, L., Engel, D., Hermosilla, P., & Ropinski, T. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00091 · Cited by 6
“To test our methods robustness against changes in texture, we also evaluate on the Describable Textures Dataset (DTD) [12] and the Oxford-IIIT Pets dataset [34].”
Lei, 肖. X., Peng, 胡. H., & Junjie, 马. M. (2025) · Laser & Optoelectronics Progress · doi:10.3788/lop241870 · Cited by 4
Monocular Depth Estimation Network Based on Swin Transformer
Yu, S., Zhang, R., Ma, S., et al. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2428/1/012019 · Cited by 4
“( c) DPT [8] .”
MethodInfluentialA Study on Self-Supervised Pretraining for Vision Problems in Gastrointestinal Endoscopy
Sanderson, E., & Matuszewski, B. J. (2024) · IEEE Access · doi:10.1109/access.2024.3381517 · Cited by 7
“For our monocular depth estimation experiments, we used the listed ViT-B backbones with the depth estimation variant of the dense prediction transformer (DPT) [53] decoder, using the implementation provided in the official codebase.”
MethodCropViT: A light-weight Transformer Model for Crop Disease Detection
Selvi, G. C., Charan, H. J., & Kumar, D. (2024) · International Conference on Artificial Intelligence for Internet of Things (AIIoT) · doi:10.1109/aiiot58432.2024.10574729 · Cited by 6
Akrouchi, M. E., Mhada, M., Gracia, D. R., et al. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1472688 · Cited by 4
“In the context of dense prediction in computer vision (Ranftl et al., 2021), proposed using vision transformers as the backbone in an encoder-decoder structure.”
Zhang, Z., & Wang, Q. (2024) · IEEE Access · doi:10.1109/access.2024.3395517 · Cited by 4
“It has also demonstrated its potential and advantages in processing high-dimensional data and solving complex nonlinear problems [4], [5], [6].”
BackgroundSemantic-Guided Multiview Stereo Reconstruction for Aerial Image
Zhang, W., Yang, Z., Li, Q., & Wang, Q. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3585623 · Cited by 4
“The decoder module uses the dense prediction Transformer (DPT) [39] for depth regression.”
Hirohata, Y., Sogabe, M., Miyazaki, T., Kawase, T., & Kawashima, K. (2023) · Scientific Reports · doi:10.1038/s41598-023-42713-x · Cited by 4
“The encoder is initialized with the DPT-Hybrid 27 learned in advance, and the decoder is randomly initialized.”
InfluentialSirisha, M., & Sudha, S. V. (2023) · Traitement du signal · doi:10.18280/ts.400434 · Cited by 4
“Studies have demonstrated that vision transformers [16] have resilience to extreme occlusions, domain shifts, ETC when compared to convolutional neural networks (CNNs).”
BackgroundLeveraging Image Matching Toward End-to-End Relative Camera Pose Regression
Khatib, F., Margalit, Y., Galun, M., & Basri, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-85187-2_12 · Cited by 6
“As in the previous experiment, our method surpasses SIFT [36], but achieves inferior results compared to [50, 53] combined with a depth estimation method [43].”
ResultZhang, T., Zhang, L., Zhang, F., Zhao, S., & Zhou, Y. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3492315 · Cited by 6
“Given the original image sequence I = { I 1 , . . . , I N } , we use the powerful monocular depth estimation framework, namely DPT [79], to extract corresponding depth maps D = { D 1 , . . . , D N } .”
Complex Scene Segmentation With Local to Global Self-Attention Module and Feature Alignment Module
Ou, X., Wang, H., Liu, X., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3311264 · Cited by 4
VRVideos: A flexible pipeline for Virtual Reality Video Creation
Dickson, A., Shanks, J. A., Ventura, J., et al. (2022) · IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) · doi:10.1109/aivr56993.2022.00039 · Cited by 4
“For depth estimation, we use the DPT depth estimation model [10] on each of the RGB frames.”
MethodA robust metric distance and height estimation pipeline for wildlife camera trap imagery
Aamir, M., Wijers, M., Loveridge, A. J., & Markham, A. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103520 · Cited by 5
UW-Adapter: Adapting Monocular Depth Estimation Model in Underwater Scenes
Ye, X., Chang, Y., Xu, R., & Li, H. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3543089 · Cited by 5
“Building upon this foundation, a series of Transformer-based models has achieved further performance improvement [19], [20].”
Self-supervised Pretraining and Finetuning for Monocular Depth and Visual Odometry
Antsfeld, L., & Chidlovskii, B. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611058 · Cited by 3
“We argue that CroCo is better suited for the DVO task than the existing backbones [12], [21], [28], [35] which commonly use ImageNet-based pretrained models.”
MethodInfluentialSRNet-Trans: A Singal-Image Guided Depth Completion Regression Network for Transparent Object
Tao, T., Zheng, H. Y., Xiao, J., et al. (2025) · Applied Sciences · doi:10.3390/app151910566 · Cited by 3
GFI-Net: Global Feature Interaction Network for Monocular Depth Estimation
Zhang, C., Xu, K., Ma, Y., et al. (2023) · Entropy · doi:10.3390/e25030421 · Cited by 3
“The DPT requires additional training datasets.”
BackgroundMethodResultInfluentialMoBluRF: Motion Deblurring Neural Radiance Fields for Blurry Monocular Video
Bui, M.-Q. V., Park, J.-M., Oh, J., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3574644 · Cited by 3
“…ˆ C B ( r p ; t ) is a blurry rendered color of the base ray r p ; t and A ( · , · ) is an average function of the sharp rendered color ˆ C ( r p ; t ) of r p ; t and the set of rendered colors { ˆ C (˙ r p ; t ; q ) } N b q =1 of the corresponding latent sharp rays { ˙ r p ; t ; q } N b q =1 .”
Liu, H., Zhang, S., & He, K. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3552326 · Cited by 4
“D EEP neural networks (DNNs) play an essential role in various fields of computer vision, such as image classifi-cation [1], [2], object detection [3], [4], and semantic segmentation [5], [6], owing to the exponential advancement of convolutional neural networks (CNNs) [7], [8], [9].”
Mismatched: Evaluating the Limits of Image Matching Approaches and Benchmarks
Bonilla, S., Vece, C. D., Daher, R., Ju, X., Stoyanov, D. V., Vasconcelos, F., & Bano, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91989-3_8 · Cited by 5
Stereo Conversion with Disparity-Aware Warping, Compositing and Inpainting
Mehl, L., Bruhn, A., Groß, M. H., et al. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv57701.2024.00421 · Cited by 3
“Directly related is stereo conversion literature, but we also cover works from the broader novel view synthesis field that could be applied to our use case.”
BackgroundLocation, Neighborhood, and Semantic Guidance Network for RGB-D Co-Salient Object Detection
Zhou, W., Wang, B., Dong, X., et al. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3564238 · Cited by 3
“We utilized random panning, random cropping, level flipping, and color enhancement augmentation methods, and the proposed LNSNet-T, LNSNet-S, and LNSNet-S ∗ were all trained by employing the Adam optimizer [74] with 50, 100, and 100 epochs, respectively.”
Convex Decomposition of Indoor Scenes
Vavilala, V., & Forsyth, D. A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00842 · Cited by 3
“Depth map for refinement is inferred by [39].”
BackgroundMethodInfluentialRiver Ice Fine-Grained Segmentation: A GF-2 Satellite Image Dataset and Deep Learning Benchmark
Wei, C., Li, H., Chen, L., et al. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3604644 · Cited by 3
Mono3R: Exploiting Monocular Cues for Geometric 3D Reconstruction
Li, W., Liu, S., Qiao, P., et al. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755722 · Cited by 3
“…generates false depth discontinuities that violate the planar surface prior. producing multi-view-aware features; following, the DPT regression head[32] aggregates intermediate features from different de-coder layers through layer-wise feature fusion, and then sends the fused feature to predict…”
Road surface semantic segmentation for autonomous driving
Zhao, H., Wang, S., Peng, X., Pan, J., Wang, R., & Liu, X. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2250 · Cited by 3
“DPT improves the performance of dense prediction tasks by building transformer-based encoders ( Ranftl, Bochkovskiy & Koltun, 2021 ).”
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the Wild
Kim, D., Cho, S., Kim, S., Luo, C., & Hong, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73337-6_24 · Cited by 4
DPF: Learning Dense Prediction Fields with Weak Supervision
Chen, X., Zheng, Y., Zheng, Y., et al. (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/cvpr52729.2023.01473 · Cited by 3
“To compare with CRF, we additionally post-process the outputs of DPT baselines using denseCRF [2], and the results are shown in Tab.”
BackgroundMethodInfluentialLoLep: Single-View View Synthesis with Locally-Learned Planes and Self-Attention Occlusion Inference
Wang, C., Wang, Y.-P., & Manocha, D. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00995 · Cited by 3
“(b) is generated using DPT [29], consistent with AdaMPI.”
MethodFog computing-driven logistics: leveraging few-shot learning and foundational computer vision models
Losada-del-Olmo, J. J., Ballesteros, E. P., Gómez, Á. L. P., López-de-Teruel, P. E., Ruiz, A., & Clemente, F. J. G. (2025) · Cluster Computing · doi:10.1007/s10586-025-05662-w · Cited by 3
AMENet is a monocular depth estimation network designed for automatic stereoscopic display
Wu, T., Xia, Z., Zhou, M., Kong, L. B., & Chen, Z. (2024) · Scientific Reports · doi:10.1038/s41598-024-56095-1 · Cited by 3
AD-Lite Net: A Lightweight and Concatenated CNN Model for Alzheimer’s Detection from MRI Images
Roy, S., Gupta, A., Tiwari, S., & Sahu, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78198-8_1 · Cited by 3
Shen, C., Sun, S., Zhang, H., & Jiang, M. (2025) · Journal of Real-Time Image Processing · doi:10.1007/s11554-025-01777-w · Cited by 5
“AIFI applies self-attention mechanisms on high-level feature maps [27], while CCFM integrates S4 and S5 feature semantic information [28] and enhances detection capabilities for targets of different scales.”
Learning Indoors Free-Space Segmentation for a Mobile Robot from Positive Instances
Sevastopoulos, C., Hussain, J., An, Q., et al. (2023) · IEEE International Conference on Robotic Computing (IRC) · doi:10.1109/irc59093.2023.00010 · Cited by 3
“We intend to associate superpixel clusters with features that have been extracted by a Dense Prediction Transformer [15], that uses the Vision Transformer (ViT) architecture as a back-bone for dense prediction tasks such as semantic segmentation.”
MethodKrawi, O., & Rada, L. (2025) · IEEE Access · doi:10.1109/access.2025.3552928 · Cited by 3
“ViTs have demonstrated remarkable versatility across multiple tasks, including segmentation, detection, classification, image generation, and captioning [23], [26].”
Redefining Deep Learning Across NLP, Image Classification and Beyond - Vision Transformers
Mathews, A., Moharana, S. K., Singh, P. N., & Pattnaik, S. (2025) · IEEE International Conference on ICT in Business Industry & Government (ICTBIG) · doi:10.1109/ictbig68706.2025.11323670 · Cited by 5
“Researchers in [11] explored the application of ViT for dense prediction tasks.”
Enhancing Unsupervised Semantic Segmentation Through Context-Aware Clustering
Zhuo, W., Wang, Y., Chen, J., Deng, S., Wang, Z., Shen, L., & Zhu, W. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3405648 · Cited by 3
“SAM is trained for class-agnostic segmentation, and the patch representations, i.e. the tokens, of large-scale pretrained transformers are intrinsically inundated with contextual information that does not retain the spatial positions [59].”
BackgroundTest-Time Prompt Tuning for Zero-Shot Depth Completion
Jeong, C., Bae, I., Park, J.-H., & Jeon, H. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00881 · Cited by 4
“Recent works have explored techniques such as data mixing [55, 60, 61] and augmentation [25, 66] to achieve zero-shot generality.”
Semantic to Structure: Learning Structural Representations for Infringement Detection
Huang, C., Jia, Z., Fei, H., Zhu, Y., Yuan, Z., Zhang, J., & Zhou, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888100 · Cited by 6
“Given a source image x src , we use the DPT [15] model to generate its depth map, which is then used as a control condition for ControlNet to ensure the generated image x syn has a similar structure.”
Evangelista, I. R. S., Bandala, A. A., & Dadios, E. P. (2025) · Technologies · doi:10.3390/technologies13080372 · Cited by 3
Toward Robust Depth Fusion for Mobile AR With Depth from Focus and Single-Image Priors
Ganj, A., Su, H., & Guo, T. (2024) · IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) · doi:10.1109/ismar-adjunct64951.2024.00149 · Cited by 3
“Recent Models [14, 13], achieve strong zero-shot performance through scale-invariant loss functions and diverse dataset training.”
Bai, X., Jiang, L., Luo, Y., et al. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096852 · Cited by 3
“DPT [20] utilizes vision transformer (ViT) as its backbone, yielding detailed high-quality depth maps with high resolution.”
MethodA simple monocular depth estimation network for balancing complexity and accuracy
Liu, X., Tang, S., Feng, M., Guo, X., Zhang, Y., & Wang, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-97568-1 · Cited by 3
“Drawing from prior research 52 , we opted for λ = 0 .”
Evaluating Transferability of Attacks across Generative Models
Vallabhaneni, R. (2024) · Engineering and Technology Journal · doi:10.47191/etj/v9i06.12 · Cited by 3
Zhang, Q., Song, Y., & Lou, H. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06388-z · Cited by 3
TReX- Reusing Vision Transformer’s Attention for Efficient Xbar-Based Computing
Moitra, A., Bhattacharjee, A., Kim, Y., & Panda, P. (2024) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2024.3480524 · Cited by 4
“T HE high accuracy and robustness of vision transformers (ViT) [1], [2], [3] on large-scale image recognition tasks have made them suitable candidates for edge intelligence [4], [5].”
Lens Parameter Estimation for Realistic Depth of Field Modeling
Piché‐Meunier, D., Hold-Geoffroy, Y., Zhang, J., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00052 · Cited by 3
“[35] 0.186 0.190 MiDaSv3 (DPT) [34] 0”
MethodInfluentialPseudoDepth-SLR: Generating Depth Data for Sign Language Recognition
Sarhan, N., Willruth, J. M., & Fritnrop, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44137-0_5 · Cited by 3
Multi-Exit Vision Transformer for Dynamic Inference
Bakhtiarnia, A., Zhang, Q., & Iosifidis, A. (2021) · Scopus (Elsevier) · doi:10.5281/zenodo.5604479 · Cited by 3
Multi-Scale Monocular Depth Estimation Based on Global Understanding
Xiao, J., Li, L., Su, X. W., & Tan, G. (2024) · IEEE Access · doi:10.1109/access.2024.3382572 · Cited by 3
“For example, DPT [27] and Pix-elFormer [28] significantly improved performance with the introduction of transformers.”
BackgroundDomain-Adaptive Video Deblurring via Test-Time Blurring
He, J.-T., Tsai, F.-J., Wu, J., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73404-5_8 · Cited by 3
HDPNERF: Hybrid Depth Priors for Neural Radiance Fields from Sparse Input Views
Xu, W., Wang, Q., Pan, X., et al. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446844 · Cited by 3
“We use monocular depths estimated by DPT[12] which is pre-trained on a large-scale mixed dataset.”
MethodA Novel Video-CTU Registration Method with Structural Point Similarity for FURS Navigation
Yang, M., Chen, Y., Li, B., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43996-4_12 · Cited by 3
ISS Monocular Depth Estimation Via Vision Transformer
Ghilardi, L., Scorsoglio, A., & Furfaro, R. (2023) · Studies in Computational Intelligence · doi:10.1007/978-3-031-25755-1_11 · Cited by 3
Sparse4DGS: Flow-Geometry Assisted 4D Gaussian Splatting for Dynamic Sparse View Synthesis
Hu, D., Zhou, Y., Huang, X., et al. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755819 · Cited by 3
“Here, we employ a pre-trained 2D monocular depth estimator (DPT) [34] to generate real-time monocular depth maps 𝐷 ( 𝑡 ) at timestamp 𝑡 during training.”
ACCELERATION OF TRANSFORMER ARCHITECTURES ON JETSON XAVIER USING TENSORRT
Nikoghosyan, K. H., Khachatryan, T. B., Harutyunyan, E. A., & Galstyan, D. M. (2023) · doi:10.53297/18293336-2023.2-30 · Cited by 2
High-Rate Monocular Depth Estimation via Cross Frame-Rate Collaboration of Frames and Events
Liu, X., Fan, X., Li, J., et al. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02488-2 · Cited by 3
“…of deep learning, monocular depth estimation methods based on a single image or videos (Eigen et al., 2014; Garg et al., 2016; Godard et al., 2017; Ranftl et al., 2021; Yuan et al., 2022; Lee et al., 2022; Zhang et al., 2023b; Xian et al., 2024; Xiang et al., 2024) has undergone astonishing…”
Unsupervised Camouflaged Object Segmentation as Domain AdaptationPreprint
Zhang, Y., & Xu, C. (2024) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/3rtgu · Cited by 4
Saleh, F. S., Aliakbarian, S., Hewitt, C., Petikam, L., Xiao-Xian, Criminisi, A., Thomas, J., & Baltrušaitis, T. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00508 · Cited by 10
“We adapt the dense prediction transformer (DPT) [35] to handle variable input resolutions efficiently.”
SVTNet: Dual Branch of Swin Transformer and Vision Transformer for Monocular Depth Estimation
Jia, S., Wang, Y., Chen, H., et al. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887818 · Cited by 3
“Recent researchers broadly use the attention-based backbone such as Vision Transformer [5] and Swin Transformer [6] to capture global information in images due to their larger receptive field.”
GSDC Transformer: An Efficient and Effective Cue Fusion for Monocular Multi-Frame Depth Estimation
Fang, N., Qiu, L., Zhang, S., Wang, Z., Zhou, Z., & Hu, K. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3355738 · Cited by 3
“With the emergence of Vision Transformers, Ranftl et al. [24] applied this task within a pure attention framework and gained a substantial precision improvement.”
Bao, C., Zhang, X., Yu, Z., 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/cvpr52734.2025.01527 · Cited by 5
“In this paper, we present a novel neural rendering framework to realize unbounded 360 ◦ view synthesis and 3D reconstruction from extremely sparse views (e.g., 3-4 views) by analyzing and addressing two key challenges in unposed and unbounded scenarios.”
Visual Watermark Identification From the Transparent Window of Currency by Using Deep Learning
Tong, D., & Yan, W. Q. (2023) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-6684-4945-5.ch003 · Cited by 4
@BENCH: Benchmarking Vision-Language Models for Human-centered Assistive Technology
Jiang, X., Zheng, J., Liu, R.-P., Li, J., Zhang, J., Matthiesen, S., & Stiefelhagen, R. (2024) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00387 · Cited by 7
“The representative works for each task are: MaskFormer [7], Mask2Former [6] and kMaX-DeepLab [44] for panoptic segmentation; BTS [21], DPT [32] and GLP [19] for depth estimation; ASTER [35], SEED [30] and MaskOCR [25] for OCR; VizWiz Cap, AoANet [13] for captioning, VizWiz VQA, S-VQA [18] and…”
DepthCloud2Point: Depth Maps and Initial Point for 3D Point Cloud Reconstruction from a Single Image
Asafa, G. F., Ren, S., Mamun, S. S., et al. (2025) · Electronics · doi:10.3390/electronics14061119 · Cited by 3
DustNet++: Deep Learning-Based Visual Regression for Dust Density Estimation
Michel, A., Weinmann, M., Kuester, J., AlNasser, F., Gómez, T., Falvey, M. J., Schmitz, R., Middelmann, W., & Hinz, S. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02376-9 · Cited by 4
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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