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
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 1,501–1,600 of 2,248
Deep Semantic Segmentation Models in Computer Vision
Andreini, P., & Dimitri, G. M. (2022) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2022.es2022-5 · Cited by 1
“In their work, [33] suggest that the dense vision transformer may allow for more accurate and globally consistent predictions than CNN-based networks.”
BackgroundF2Plenoxels: fast voxel radiance fields without neural networks for few-shot view synthesis
Peng, J. J., Chunyi, C. H. E. N., & Liu, Y. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00491-2 · Cited by 1
Video Foundation Model for Medical 3D Segmentation
Ma, Q., Sun, G., Tombak, G. I., Jain, S., Huber, N. B., Gool, L. V., & Konukoğlu, E. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88977-6_8 · Cited by 1
NoPe‐NeRF++: Local‐to‐Global Optimization of NeRF with No Pose Prior
Shi, D., Cao, S., Wu, B., Guo, J., Fan, L., Chen, R., Liu, L., & Ye, J. (2025) · Computer Graphics Forum · doi:10.1111/cgf.70012 · Cited by 1
“In our approach, we leverage the estimated depth D from DPT [RBK21] for supervision.”
High-Resolution and Few-Shot View Synthesis from Asymmetric Dual-Lens Inputs
Xu, R., Yao, M., Li, Y., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72646-0_13 · Cited by 1
“Specifically, we obtain the depth D from the image I ↑ ∈ { I ↑ i } Mi =1 using a pre-trained monocular depth estimator [34], and render the depth Z from 3D-GS using a differentiable depth rasterizer [44].”
AFHNet: Attention-Free Hybrid Network for Salient Object Detection in Underwater Images
Tang, Q., Wang, Z., Wang, X., & Zhang, S. (2025) · IEEE Access · doi:10.1109/access.2025.3567606 · Cited by 3
“Depth maps, obtained via the DPT method [33], help the model accurately identify and localize objects of interest.”
Kushwaha, D. S., Aljuaid, N. M., Pandey, R., et al. (2025) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.4070031 · Cited by 1
RGB-Guided Depth Feature Enhancement for RGB–Depth Salient Object Detection
Zeng, Z., He, J., Zhan, Y., Liu, H., & Tan, X. (2024) · Electronics · doi:10.3390/electronics13244915 · Cited by 1
Enhancing Multimedia Applications by Removing Dynamic Objects in Neural Radiance Fields
Yang, X., Wang, T., Liu, H., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0972-7_7 · Cited by 1
“We follow [34,10] to exploit the depth map generated by DPT [30,31], adding loss to relate the sample color to its ray origin distance through depth mapping, using translation and scale-invariant losses for depth supervision: where ˆ S ∗ and S ∗ are per-frame normalized depth.”
VIL-PPGen: A Novel Pseudo Point Generator Based on Visible Light Camera, Infrared Camera and Lidar
Ai, F., Amjad, H., Li, Z., Song, Y., Song, C., & Xu, Z. (2025) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3511923 · Cited by 1
“Numerous mainstream approaches formulate depth prediction as a regression problem [37], [38], [39] by directly predicting a depth value for each pixel, a classification problem [40], [41] by classifying into discrete depth intervals, or a classification-regression problem [42].”
Make optical transmission quality prediction more effective: A parallel computing view
Sun, X., Yang, S., Han, J., et al. (2024) · Optics Communications · doi:10.1016/j.optcom.2024.130947 · Cited by 1
Tan, H. P., Li, R., Zhang, J., Zhang, H., Qian, Y., Liu, J., & Gu, Z. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3646216 · Cited by 2
“We chose three categories of MDE models: a) convolutional neural networks (CNN)-based — MonoDepth2 [26], Depth-Hints [27], and Many-Depth2 [28]; b) Transformer-based —MiDaS [29], DPT [30], AdaBins [31], DepthAnything-v2 [32]; c) robustness-enhanced —MIMDepth [33], RobustDepth [34], AdvMono [35].”
Camera-view supervision for bird's-eye-view semantic segmentation
Yang, B., Yu, L., & Feng, C. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1431346 · Cited by 1
“For camera-view depth prediction, we use Relative Square Error (RSE), as depth prediction involves predicting continuous values rather than binary labels (Xie et al., 2022; Eigen et al., 2014; Ranftl et al., 2021).”
Improving Equivariance in State-of-the-Art Supervised Depth and Normal Predictors
Zhong, Y., Bhattad, A., Wang, Y.-X., & Forsyth, D. A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01990 · Cited by 1
“Figure 1 shows the failure of equivariance of depth [45] and surface normal predictors [1].”
BackgroundMethodInfluentialPhysical Parameters Estimation Using Roadside Monocular Vision
Zhang, N., Lu, M.-F., Yuan, S., Liu, C., Wang, Y., Yang, Z., Zhu, C., Chen, Z., Zhang, S., Zhang, F., et al. (2026) · IET Intelligent Transport Systems · doi:10.1049/itr2.70138 · Cited by 1
Map-Free Visual Relocalization Enhanced by Instance Knowledge and Depth Knowledge
Xiao, M., Chen, R., Luo, H., Zhao, F., Wu, F., Xiong, H., Ma, X., & Wang, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888917 · Cited by 1
“Specifically, we build the comparison method using three components: feature matching methods (SIFT [23], LoFTR [24], and SuperGlue [11]), depth estimation (DPT [25] fine-tuned on KITTI [26] and NYUv2 [27]), and pose estimation (5-point solver [20] with MAGSAC++ [28], PNP [29], and Procrustes [30]).”
Evaluating Atypical Gaze Patterns through Vision Models: The Case of Cortical Visual Impairment
Avramidis, K., Chang, M. Y., Sharma, R. A., et al. (2024) · doi:10.1109/embc53108.2024.10782199 · Cited by 1
Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention
Wu, Z., Li, Z., Fan, Z., Wu, Y., Gan, Y., & Pu, J. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/172 · Cited by 1
CAM3R: Camera-Agnostic Model for 3D Reconstruction
Guruprasad, N., Yadav, A., Chellappa, R., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37321-2_14 · Cited by 1
FOUCNet: a network focusing on unconventional crack detection
Zhao, W., Zhou, Z., Zhang, L., & Song, K. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-026-05164-1 · Cited by 1
Zhao, H., Liao, C.-Y., Yang, D. Z., Jiang, S. B., & Nguyen, D. T. D. (2025) · Machine Learning Health · doi:10.1088/3049-477x/adf076 · Cited by 1
mmWave Radar and Image Fusion for Depth Completion: a Two-Stage Fusion Network
Song, T., Yang, B., Wang, J., He, G., Dong, Z., & Zhong, F. (2024) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion59988.2024.10706480 · Cited by 1
“Lo et al. [22] designed a radar-camera fusion depth completion model: RCDPT, based on the ViT [23] backbone, which is an improvement of the DPT [24] network in monocular depth estimation.”
MethodLarge vision model supported tracking of high-density particles based on optical flow learning
Zhang, Y., Zhu, J., Du, Y., et al. (2025) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2025.102904 · Cited by 1
Gültekin, F., Koz, A., Bahmanyar, R., Azimi, S. M., & Süzen, M. L. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00393 · Cited by 1
“ViTs are also demonstrating success in depth estimation tasks on in-situ images, with models like Dense Prediction Transformers (DPT) [34], GLP-Depth [21], and Depth Anything [46] combining ViTs with feature extraction for precise depth predictions.”
Tan, K., Wu, Y., Yang, H., & Ma, X. (2026) · Remote Sensing · doi:10.3390/rs18020282 · Cited by 3
EFormer: Enhanced Transformer Towards Semantic-Contour Features of Foreground for Portraits Matting
Wang, Z., Miao, Q., Xi, Y., et al. (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/cvpr52733.2024.00372 · Cited by 1
“For pixel-level prediction tasks, such as segmentation, SETR[38], SegFormer[34] and DPT[27] apply transformer as an encoder to attain feature maps of images.”
MethodGao, H., Song, W., Cui, S., Zhou, B., Tan, H., & Wang, Q. (2024) · IEEE Access · doi:10.1109/access.2024.3419908 · Cited by 2
“Studies [34], [35] have shown that a large receptive field is a key factor in their success.”
BackgroundMVSM-CLP: Multi view synthesis method for Chinese landscape painting based on depth estimationPreprint
Peng, X., Zhou, W., Hu, Q., Li, T., Zhang, D., & Cao, R. (2024) · Research Square · doi:10.21203/rs.3.rs-5303350/v1 · Cited by 1
Test‐Time Optimization for Video Depth Estimation Using Pseudo Reference Depth
Zeng, L., & Kalantari, N. K. (2023) · Computer Graphics Forum · doi:10.1111/cgf.14729 · Cited by 1
ViT-FFrCnt: A Few-shot Fruit Counting Approach with Multiscale Vision Transformer
Quan, K.-A. C., Vuong, G.-H., & Tran, M. (2023) · International Conference on Multimedia Analysis and Pattern Recognition (MAPR) · doi:10.1109/mapr59823.2023.10289072 · Cited by 1
“Several studies have shown that using multi-scale features makes it possible to increase the accuracy compared to the vanilla ViT [12].”
BackgroundMethodHao, R., Xu, C., & Zhong, C. (2025) · Sensors · doi:10.3390/s25134022 · Cited by 1
“It has attracted significant attention from both academia and industry (Intel Labs [5], Google DeepMind [6], and Toyota Research Institute [7]) due to its hardware cost-effectiveness, compact design, and ease of deployment.”
DORec: Decomposed Object Reconstruction and Segmentation Utilizing 2D Self-Supervised Features
Wu, J., Li, S., Ji, S., Yang, Y., Wang, Y., Xiong, R., & Liao, Y. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3511425 · Cited by 1
“Recently, transformer architectures [27], [28] and self-supervised learning methods have improved the generalization of image vision models [29], [30].”
GuideDogNet: A Deep Learning Model for Guiding the Blind in Walking Environments
Hwang, Y., Yoon, T., & Park, K. (2021) · Journal of Student Research · doi:10.47611/jsrhs.v10i4.2064 · Cited by 1
“In the proposed system, we exploit DPT to generate the depth map from the input images.”
BackgroundMethodInfluentialBridging Implicit and Explicit Geometric Transformation for Single-Image View Synthesis
Park, B., Go, H., & Kim, C. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3378757 · Cited by 5
“To be a more fair comparison, we utilized DPT [46] as in SE3DS and conducted a comparison in Table VIII.”
MethodPose Networks Unveiled: Bridging the Gap for Monocular Depth Perception
Dayoub, Y., Savchenko, A. V., & Makarov, I. (2024) · IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) · doi:10.1109/ismar-adjunct64951.2024.00168 · Cited by 1
“Shifting the focus to recent advancements, several studies have incorporated transformer architectures into depth estimation networks, capitalizing on their ability to capture global context and long-range dependencies more effectively [18, 35].”
Sheng, Y., Wang, J., Tao, M., & Bao, B. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754728 · Cited by 1
Kim, M., Song, S., & Jang, H. (2025) · Journal of the Society of Naval Architects of Korea · doi:10.3744/snak.2025.62.2.77 · Cited by 1
Uncertainty-Aware Projection Network for Multi-View 3D Object Detection
Jiang, H., Yang, W., Zhang, T., & Zhang, Y. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3675364 · Cited by 2
“Some transformer-based methods [48], [49], [50], [51] have been proposed to process high-resolution global contextual information.”
Pradhan, N., Srivastava, G. K., & Kaushik, G. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108787 · Cited by 2
DepthFlow: Exploiting Depth-Flow Structural Correlations for Unsupervised Video Object Segmentation
Cho, S., Lee, M., Lee, J. S., et al. (2025) · IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw69036.2025.00404 · Cited by 1
Wang, M., Wang, B., Fan, X., et al. (2026) · Electronics · doi:10.3390/electronics15112321 · Cited by 1
A Novel Deep Learning-Based Object Detector Using SPOTNET-SNIPER Network
Sirisha, M., & Sudha, S. V. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-19-2069-1_43 · Cited by 1
Wang, J., Yu, J., Shi, H., & Wang, Y. (2024) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.24158 · Cited by 1
“DPT [25] is introduced with a transformer backbone embedded within the encoder-decoder design, aiming for fine-grained output segmentation predictions compared to fully convolutional networks.”
Multimodal 2.5D Feature Fusion and LSTM Classifier for Telerehabilitation
Kamal, A., Najam, S., & Jalal, A. (2025) · International Conference on Innovative Computing (ICIC) · doi:10.1109/icic68258.2025.11413093 · Cited by 1
Cross-domain feature interaction enhancement network for underwater image enhancement
Xu, D., Xu, W., Zhou, Y., et al. (2026) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2026.104824 · Cited by 1
JRN-Geo: A Joint Perception Network Based on RGB and Normal Images for Cross-View Geo-Localization
Zhou, H., Zhang, Y., Huang, T., et al. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127591 · Cited by 1
“To obtain normal images, we use a surface normal estimation model from the Omnidata framework [19], which employs the DPT-Hybrid [28] network for estimating pixel-level normal maps from the input images.”
Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on Aerial Images
AlaaEldin, Y., & Odone, F. (2026) · ACM Symposium on Applied Computing · doi:10.1145/3748522.3779827 · Cited by 1
“Indeed, while there is a trend in using foundation models for addressing these tasks (see, for instance, DepthAny-thing [58], SegmentAnything [21] and Any-to-Any [1]), they are large in size and not convenient for hardware deployment where memory and speed are critical factors, with current consumer hardware we can re…”
VIPHY: Probing “Visible” Physical Commonsense Knowledge
Singh, S., Qasemi, E., & Chen, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.473 · Cited by 5
Temporal3D: 2D-to-3D Video Conversion Network with Multi-frame Fusion
Zhang, Z., & Wang, R. (2022) · International Conference on Advances in Computer Technology, Information Science and Communications (CTISC) · doi:10.1109/ctisc54888.2022.9849751 · Cited by 1
Enhancing video salient object detection via SAM-based multimodal energy prompting
Jiang, T., Wang, Y., Hou, F., & Liu, L.-L. (2025) · Pattern Analysis and Applications · doi:10.1007/s10044-025-01531-9 · Cited by 2
“For each video frame, we obtain the depth map using the DPT model [ 60] and compute the optical flow using the RAFT [61 ].”
Mitigating the Impact of Attribute Editing on Face Recognition
Banerjee, S., Mullangi, S. P., Wagle, S., Hegde, C., & Memon, N. D. (2024) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb62174.2024.10744430 · Cited by 1
“5 with Control Net and the Dense Prediction Transformer (DPT)-Hybrid model (MiDaS 3.0) [31] for depth maps and Canny for edge maps.”
MethodInfluentialCui, Y., Han, Y., Guo, D., et al. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08395-8 · Cited by 1
Towards Egocentric 3D Hand Pose Estimation in Unseen Domains
Mucha, W., Wray, M., & Kampel, M. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00560 · Cited by 1
“During training, we incorporate an auxiliary pseudo-depth prediction task supervised by DPT-Hybrid [35], improving cross-domain generalisation.”
Developing a Depth Estimation Algorithm for an Ultra-thin Light-Field Camera
Na, J., Cha, Y.-G., Jo, Y., Lim, T., Jeong, K., & Kim, M. H. (2024) · Journal of the Korea Computer Graphics Society · doi:10.15701/kcgs.2024.30.5.11 · Cited by 1
Sarker, T. T., Embaby, M. G., Islam, T., AbuGhazaleh, A. A., & Ahmed, K. R. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00293 · Cited by 1
Enhancing Semi-Supervised Instance Segmentation via Depth-Guided Learning
Chen, X., Hu, J., & Zhang, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5755-4_21 · Cited by 1
GRIB: Combining Global Reception and Inductive Bias For Human Segmentation and Matting
Shen, Y., Xu, W., Lin, Q., et al. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00567 · Cited by 1
“0 [15] DPT-L [16] model.”
Latent Object Embedding for Self-Supervised Monocular Depth Estimation
Wang, S., Yu, T., Pan, S., Chen, W., Wang, Z., Leung, V. C. M., & Tian, Z. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3547851 · Cited by 1
“Furthermore, inspired by the success of Transformers in computer vision, recent works [8], [24], [25] have explored pure Transformer architectures or hybrid networks combining Transformers and CNNs to further improve depth prediction accuracy.”
A Survey of Zero-Shot Image Classification: Concepts, Developments, and Challenges
Xu, S. H., Wang, Y., Zhu, X., Guan, D., Zhang, Z., & Lin, F. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010131 · Cited by 1
Quanta Neural Networks: From Photons to Perception
Sundar, V., Zhang, T., Jungerman, S., et al. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00484 · Cited by 1
“To retain the original model’s strong generalization capabilities, we initialize with its weights and only finetune the QNN and the depth-prediction layers [58].”
SANet: An End-to-End Anaglyph Image Separation Network
Yan, T., Tan, L., Lu, Y., et al. (2025) · IEEE transactions on consumer electronics · doi:10.1109/tce.2025.3610995 · Cited by 1
“Several methods [34], [35], [36], [37], [38] have adopted Transformer encoders to capture global receptive fields, thereby enhancing contextual reasoning in unsupervised depth estimation.”
Human-Centric Depth Estimation: A Hybrid Approach with Minimal Data
Kim, Y., Ahn, H., Kim, T., et al. (2025) · Electronics · doi:10.3390/electronics14112283 · Cited by 1
A Monocular Depth Estimation Method for Blind Obstacle Avoidance
杨, 双. (2023) · Modeling and Simulation · doi:10.12677/mos.2023.125423 · Cited by 1
Wei, R., Chen, K., Ng, Y.-L., Ma, Y., Ho, J. D.-L., Tong, H., Wang, X., Dai, J., Kwok, K., & Dou, Q. (2026) · IEEE Transactions on Robotics · doi:10.1109/tro.2026.3672482 · Cited by 1
“The initial depth estimation D initial is then obtained from a DPT-based decoder [62].”
Leveraging airport CCTV footage through video understanding techniques for visibility prediction
Pun, Z., Tian, X., & Gao, S. (2025) · Kuwait Journal of Science · doi:10.1016/j.kjs.2025.100470 · Cited by 1
TransWaveNet: Transformer for Underwater Image Restoration with Wavelets
Mishra, P., Khan, M. R., Phutke, S. S., et al. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3613670 · Cited by 1
Visual Anomaly Detection on Topological Connectivity Under Improved YOLOv8
Li, Y., & Xie, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-2071-5_22
Task-prompted Embedding Encoder for Multi-Task Dense Prediction
Gao, Y., & Liu, Y. (2025) · doi:10.1109/ijcnn64981.2025.11228490
“For dense prediction tasks [3], while extracting task-specific features, associated information from other tasks should also be considered and utilized to improve the performance and generalization capability of the model.”
Xiong, K., Wang, Z., & Wang, R. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461566
“S geometry ( x, y ) = ∥∇ D ( x, y ) ∥ 2 + λ curv κ ( x, y ) where D ( x, y ) is the the monocular depth estimated with the Dense Prediction Transformer (DPT) [17] and κ ( x, y ) represents surface curvature estimated with the second-order gradient of the depth.”
DGF-Net: Underwater Image Enhancement via Depth Priors and Frequency-Domain Modeling
Huang, C., Shen, J., Liu, J., et al. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461879
Depth-Aware Vision Transformer for Transparent Object Depth Completion
Huang, J., He, J., & Fang, G. (2025) · doi:10.1109/eccst68196.2025.11440993
“Methods such as DPT [1], AdaBins [9], and DepthFormer [13] leverage global attention to propagate information across distant regions, enabling better shape reasoning and depth-consistency enforcement.”
Meng, B., Wang, J., & Liu, J. (2026) · Electronics · doi:10.3390/electronics15163691
OGDepth: Leveraging Object Guidance in Diffusion Models for Enhanced Monocular Depth Estimation
Yang, W., Pei, S., Liu, B., et al. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755409
Aerial-Guided Groupwise CRFs for Multiple Panorama Depth Estimation with Scale-Consistent
Xu, Y., Ma, J., & Yan, C. C. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650764
“Recent advancements have seen the incorporation of vision transformers for aggregating image information, as in [27].”
BackgroundZhao, T., Huang, Z., Jiang, X., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_29
Omaisan, A. Y. A., Gobee, S., Mohamed, I. S., et al. (2025) · doi:10.1109/roma66616.2025.11155508
“Ranftl et al. [9], [10] addressed the challenge of insufficient large-scale ground truth data by introducing MiDaS, a model trained on a combination of diverse datasets.”
Vila, G. C., Dai, A., & Gao, G. (2026) · Proceedings - IEEE Aerospace Conference · doi:10.1109/aero66936.2026.11519804
“• Depth Pro [23]: A model for zero-shot metric depth estimation that uses a multi-scale vision transformer and is designed to operate on high-resolution inputs.”
MS360: A Multi-Scale Feature Fusion Framework for 360 Monocular Depth Estimation
Mohadikar, P., Fan, C., & Duan, Y. (2024) · Graphics Interface · doi:10.1145/3670947.3670955
Memory and Parameter Efficient Adapters for 3D Surgical Scene Understanding
Mazzocchetti, S., Cercenelli, L., Bernante, P., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-33500-5_42
Multi-Modal Feature Fusion Distance Gating 3D Imaging Based on Edge Computing
Huang, H., Zhou, B., Xie, Y.-A., Lai, P., Zhang, X., & Zhu, J. (2025) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.1109/cloudcom67567.2025.11331355
“Vision models based on its architecture [11-16], such as DPT [17], the DINO series [18, 19], Swin Transformer [20], and ViT-Adapter [21], have demonstrated powerful global feature modeling capabilities in dense computer vision tasks like depth estimation and image segmentation [22-24].”
Peymanirad, A., & Semnani, S. H. (2026) · Intelligent Service Robotics · doi:10.1007/s11370-026-00737-y
“However, the use of deep neural networks with complex layers in LeReS means that it demands significant computational resources, potentially making it unsuitable for real-time applications and low-power devices [24].”
P, S. D. K., Harini, S., Nivedhitha, S., et al. (2026) · doi:10.1109/ccic68129.2026.11485926
“Fusion modules (purple) progressively fuse and upsample the representations to generate a fine-grained prediction [11].”
Do, W. K., Strong, M., Swann, A., et al. (2026) · IEEE Transactions on Robotics · doi:10.1109/tro.2026.3686277
“Prior work, such as the dense prediction transformer (DPT) head [56] upsamples features from the coarse level and fuses with fine level predictions.”
Non-Homogeneous Haze Synthesis for Hazy Image Depth Estimation Using Deep Learning
Choi, Y., Paik, J. H., Ju, G., Lee, D., Hwang, G., & Lee, S. (2022) · Journal of the Korea Computer Graphics Society · doi:10.15701/kcgs.2022.28.3.45
Hybrid SW-Conv Networks for Monocular Depth Estimation
Wang, H., Du, J., Chen, F., & Li, D. (2023) · International Symposium on Robotics & Intelligent Manufacturing Technology (ISRIMT) · doi:10.1109/isrimt59937.2023.10428367
Perceptual BEV Elevation Estimation Method for Road Surface Reconstruction
Zhang, S. (2026) · doi:10.1109/eespe68405.2026.11648301
“Therefore, further enhancing the expressive ability of the elevation head within the monocular framework is a reasonable direction that balances accuracy and real-time performance [28]-[35] .”
Gavryushin, A., Liu, Y., Huang, D., Kuo, Y., Valentin, J., Gool, L. V., Hilliges, O., & Wang, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91575-8_5
“Meanwhile, the recent advancements in foundation models for various core computer vision tasks such as visual grounding [16], segmentation [13] and depth estimation [1,19], showcasing remarkable performance, have opened up new possibilities for human-object interaction modeling.”
BackgroundMethodAdaptive Clustering and Frequency Division Network for Efficient Monocular Depth Estimation
Song, L., Song, H., & Jiang, B. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3585923
“Dense vision transformers were employed to capture pixel-level long-range dependencies, thereby enhancing depth estimation performance [20].”
AdaptiveSplat: Texture Aware Controllable 3D Gaussian Allocation for Feed-Forward Reconstruction
Singhal, B., G, S. K., Iyer, S., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37281-9_31
Monocular Accumulated Depth Estimation with Recursive Feature Fusion
Wang, Q., Shuai, H., & Liu, Q. (2022) · Journal of Computer-Aided Design & Computer Graphics · doi:10.3724/sp.j.1089.2022.19728
“…2.834 0.099 0.954 0.992 0.998 Liu 等 [27] 0.201 1.584 6.471 0.273 0.680 0.898 0.967 TransDepth [28] 0.064 0.252 2.755 0.098 0.956 0.994 0.999 DPT [29] 0.062 2.573 0.092 0.959 0.995 0.999 Kuznietsov 等 [30] 0.113 0.741 4.621 0.189 0.862 0.960 0.986 Gan 等 [31] 0.098 0.666 3.933 0.173 0.890 0.964…”
InfluentialYue, W., Chang, K., Liu, X., Tan, K., & Chen, W. (2025) · Symmetry · doi:10.3390/sym17122070
Integrating Expert Knowledge with Deep Reinforcement Learning Methods for Autonomous Driving
Chekroun, R. (2024) · theses.fr (ABES) · doi:10.70675/b7d8bc4ez6901z462dzb7d8z2482e665eb47
Pathak, S., Kaushik, V., & Lall, B. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209847
“Depth information, derived from pre-trained depth estimation network [12], is utilized to define spatially coherent neighborhoods around the masked regions, enabling the model to preserve the structural context of the scene.”
Full-Chain Optimization and Inter-Modular Synergy for Semi-Supervised Semantic Segmentation
Meng, J., Zhang, H., Liu, J., et al. (2026) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac71005.2026.11616256
“DPT [32] adapts vision transformers for dense prediction tasks, achieving state-of-the-art performance on semantic segmentation benchmarks.”
Monocular Depth Estimation with Guided Edge-Aware Attention for Endoscopic Images
Abdallah, B., & Raza, S. E. A. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515385
Bridging Geometric and Semantic Foundation Models for Generalized Monocular Depth Estimation
Ma, S., Choi, W., Park, J., Kim, J., Lee, S. H., Seo, J., & Im, S. (2026) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic69189.2026.11386238
“The quantitative results are presented in Table III for the base and large models, showcasing consistent performance improvements.”
Depth Estimation Leveraging Multi-Model Hybrid Fusion for Enhanced Depth Prediction
Biswas, K., Sinha, S., Mishra, A. K., et al. (2025) · doi:10.1109/aece67531.2025.11386715
“…robust, efficient and high-quality depth maps across variable images, whereas the DPT models include Vision Transformers (VT) in place of Convolutional Neural Networks (CNNs) for dense prediction to further improve depth estimation accuracy by capturing long-range dependencies within images [3][4].”
Wang, Z., Xu, S., Xu, Y., Xu, G., Deng, L., Zhu, H., & Pedersen, M. (2026) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2026.3673965
“Finally, the fused compact feature are sent to a depth head [52] to obtain depth maps.”
Free3R-GS: Endoscopic SfM-free rendering with reflection repair using 4D Gaussian splatting
Li, T., Zhou, Y., Xia, G., Dai, Y., Zhang, J. L., Wang, R., & Gu, H. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113980
Depth Estimation using Omnidirectional Stereo Imaging and Machine Learning
Shibasaki, N., Taguchi, A., & Bao, Y. (2024) · International Conference on Advanced Mechatronic Systems · doi:10.1109/icamechs63130.2024.10818719
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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