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 1,901–2,000 of 2,248
Deep Learning Residential Building Segmentation for Evaluation of Suburban Areas Development
Łysak, A., & Luckner, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-63783-4_9
“That includes the vision transformers-based solution DPT [19] published in 2021, the mask transformer-based method Mask2Former [3] published in 2022, and the latest version of the acclaimed real-time object detection and image segmentation model YOLOV8 [10] published in 2023.”
LG-NeRF: Local-global ray joint optimization for few-shot novel view synthesis
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Teng, 金. J., & Fangyi, 汪. W. (2026) · Laser & Optoelectronics Progress · doi:10.3788/lop251991
BuffetAR: A Food Recognition System with Stereo Audio Cues for Blind Users in Conference Dining
Chi, C.-N., Hsu, J.-L., & Hung, C.-H. (2025) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce65946.2025.11274593
“Depth Estimation : The RGB image is processed by DPT-Large [4], a Vision Transformer-based model, to produce a depth map.”
Monocular Image-Based 3D Object Detection for Industrial Pick-and-Place Tasks
Jun, J., Park, B., & Jeong, J. (2025) · WSEAS TRANSACTIONS on SYSTEMS archive · doi:10.37394/23202.2025.24.65
Frequency-Guided Network for Low-contrast Staining-free Dental Plaque Segmentation
Jiang, Y., Song, W., Li, S., Yang, Y., Xia, B., Hao, A., & Qin, H. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822013 · Cited by 1
Indoor Object Measurement Through a Redundancy and Comparison Method
Faria, P., Simões, T., Marques, T. N., et al. (2025) · Sensors · doi:10.3390/s25216744
Zhou, Z., Wei, X., Huang, H., et al. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3715962
Faseeh, M., Bibi, M., Khan, M. A., et al. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.70450
Martinez-Avila, J. A., & Fuentes-Álvarez, R. (2026) · Advanced structured materials · doi:10.1007/978-3-032-13551-3_22
JustDepth: Real-Time Radar-Camera Depth Estimation With Single-Scan LiDAR Supervision
Yun, W., Kim, D., & Lee, S. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3655274
“Monocular Depth Estimation: Recent monocular depth meth-ods increasingly adopt transformer backbones and large-scale training to produce more coherent predictions, e.g., DPT [14] and Depth Anything [15].”
Yang, Y., & Cheung, C. F. (2026) · AI · doi:10.3390/ai7090374
Prior-Guided 3D Gaussian Splatting for Data Augmentation in Endoscopic Monocular Depth Estimation
Colan, J., Davila, A., & Hasegawa, Y. (2026) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur69537.2026.11626692
“Standard supervised methods, such as Dense Prediction Transformers (DPT) [14] and Depth-Anything-V2 [15], establish strong baselines for dense geometric prediction.”
Breast Cancer Diagnosis from Histopathological Images Using Deep Neural Networks
Khalid, S., Chaudry, S. S., Awais, M. A., et al. (2025) · International Conference on IT Innovation and Knowledge Discovery (ITIKD) · doi:10.1109/itikd63574.2025.11004827
“Unlike CNNs, which struggle to capture long-range dependencies, the ViT leverages self-attention to learn relationships across distant regions in medical images, outperforming models like VGG16 and ResNet for breast cancer detection [19].”
SHPNeXt: A Novel Method of Multi-Scale and Variable Resolution AI-Based Tongue Image Segmentation
Peng, C.-X., Gao, Z., Wang, J., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3554487
Beyond the Diameter: 3D Surface Area Estimation for More Accurate Polyp Sizing
Nabijiang, A., Chen, Q., Cao, Y., et al. (2025) · ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) · doi:10.1145/3721201.3721404
“We employed the state-of-the-art Depth Anything V2 [23] as the backbone for our depth estimation model, which is based on the Dense Vision Transformer (DPT) [13] architecture.”
Huang, M., Zhang, X., Mai, Y., Zhang, J., & Zhang, D. (2025) · IEEE International Conference on Electronic Information and Communication Technology (ICEICT) · doi:10.1109/iceict66683.2025.11159965
“[25] 0.100 0.901 Depth Anything V2[4] 0.074 0.946 Marigold[26] 0.099 0.916 Ours 0.064 0.967 To rigorously validate the contribution of each component, we designed systematic ablation experiments.”
InfluentialJiang, W., Li, J., Niu, Y., et al. (2026) · Sensors · doi:10.3390/s26041199
“The recent emergence of attention-based architectures [21,37,38] has opened new avenues for accuracy improvements in depth estimation.”
DGFormer: A Dynamic Kernel with Gaussian Fusion Transformer for Semantic Image Segmentation
Yang, H., Tang, L., Wu, T., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72338-4_2
Acree, C., Krystofiak, E., Coate, K. C., DelGiorno, K. E., Winn, N. C., Novak, S. W., Zaganjor, E., Magnuson, M. A., & Drigo, R. A. E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.06.743293
Gu, S., Sun, M., Zhang, Z., et al. (2025) · Symmetry · doi:10.3390/sym17081237
Prakash, R., Abraham, A., & Prasad, S. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-28478-5_7
MAGiSt3R: Multi-agent Feed-Forward 3D Reconstruction from Monocular RGB Videos
Gong, Z., Li, X., Tosi, F., Xu, N., Mattoccia, S., Cai, J., & Poggi, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36984-0_23 · Cited by 1
Finer monocular depth estimation with long range in various driving lighting environments
Liu, Y., Yan, M., Xiao, Y., Cui, G., & Li, H. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-025-11436-y · Cited by 1
GUIDE: Generative Understanding Injection for Dense Estimation via Enhanced Shape Coherence
Yang, S., Liu, J., & Zhang, Q. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-4445-5_6
DSM generation from VHR spaceborne imagery using deep learning-based land-cover-aware GCP extraction
Gutjahr, K., Gregorac, A., Deutscher, J., et al. (2026) · ISPRS Open Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.ophoto.2026.100123
IAASNet: Ill-Posed-Aware Aggregated Stereo Matching Network for Cross-Orbit Optical Satellite Images
Huang, J., Sun, H., & Wang, T. (2025) · Remote Sensing · doi:10.3390/rs17213528
InstantHDR: Single-Forward Gaussian Splatting Initialization for HDR 3D Reconstruction
Ye, D., Xu, J., Ping, J., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37595-7_12
Consistent Monocular Depth Estimation with Contact Region Boundary-Aware Refinement
Wang, Y., Miao, Q., & Zuo, W. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37624-4_10
UAVNet: Explore High-Resolution Semantic Segmentation for UAV
Gong, X., Lv, J., Zhang, X., et al. (2025) · International Conference on Computer Vision and Data Mining (ICCVDM) · doi:10.1109/iccvdm66874.2025.11290349
“This capability under-pins numerous critical applications, from autonomous driving and robotic navigation to medical image analysis and depth estimation [10].”
GLUformer: An Efficient Transformer Network for Image Denoising
Xue, C., & Qian, P. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-4761-4_67
Deep Learning for Image/Video Restoration and Super-resolution
Tekalp, A. M. (2022) · doi:10.1561/9781680839739
Geometry-Consistent 3D Gaussian Splatting via Complementary Supervision from Sparse Views
Huang, Q., & Liang, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5737-0_39
Zhang, J., Hu, Y., Liu, S., et al. (2026) · Remote Sensing · doi:10.3390/rs18162789
HAD-Net: Underwater salient object detection via hierarchical ambiguity decoupling
Li, T., Sun, G., & Wang, M. (2026) · Ocean Engineering · doi:10.1016/j.oceaneng.2026.127226
Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker
Park, M., Lee, S., Choi, W., et al. (2023) · Proceedings e report · doi:10.36253/979-12-215-0289-3.62
Zookt: Task-Adaptive Knowledge Transfer of Model Zoo for Few-Shot LearningPreprint
Zhang, B., Li, A., Luo, C., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4802951
Monocular Vision for Low-Cost Indoor Robot Localization in Predefined Environments
Dhushanthan, K., Dissanayake, G., Kulatunga, A. K., et al. (2026) · International Conference on Image Processing and Robotics (ICIPRoB) · doi:10.1109/iciprob69625.2026.11497831
“Other state-of-the-art approaches, including DPT [25], MiDaS [26], UniDepth [27], and Depth Pro [28], similarly employ transformer or multi-scale feature architectures with parameter scales ranging from tens to hundreds of millions.”
Rehazing for Dehazing: A Physics-Guided Parametric Augmentation Net
Chang, C.-L., Tsai, F.-J., Huang, Z., Gu, L., & Lin, C. (2025) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2025.3626262
“Additionally, inspired by the remarkable success of Transformers [15] in various vision tasks [16], [17], [18], recent efforts have explored Transformer-based architectures for image dehazing.”
Unrectified-stereo: A new paradigm for stereo matching without epipolar rectification
Zhang, X., Zhang, G. L., Lin, J., et al. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114069
Depth-Gradient-Guided Decoupled Optimization in 3D Gaussian Splatting for Sparse-View Reconstruction
Lian, X., Wang, Z., & Wang, H. (2026) · Applied Sciences · doi:10.3390/app16084026
Simulation Verification Method for Robot Composite Task Planning in Open Environments
Qu, Y., & Hu, X. (2025) · International Conference on Artificial Intelligence and Automation Control (AIAC) · doi:10.1109/aiac68175.2025.11332296
“For 3D scene modeling performance, we compare the proposed method with two mainstream baselines: MiDaS [20] + Open3D [21] and OpenMVG [22], DPT [23] and DepthAnything [24].”
Pintos, N. U., Trabes, E., Valderrama, C., et al. (2026) · Latin American Symposium on Circuits and Systems · doi:10.1109/lascas67804.2026.11457117
“However, the high performance of models like MiDaS [2] and DORN [3] comes at a substantial computational cost, making them unsuitable for edge applications [4].”
Enhancing Weakly Supervised Semantic Segmentation through Multi-Class Token Attention LearningPreprint
Luo, H., & Zeng, Z. (2024) · Research Square · doi:10.21203/rs.3.rs-4716623/v1
A Vision Transformer Framework for Real-Time Path Planning in Unknown Environments
Zheng, Y., Chen, W., Zhang, Z., et al. (2025) · International Conference on Vision, Image and Signal Processing (ICVISP) · doi:10.1109/icvisp68610.2025.11451731
“More recently, imperative learning with bi-level optimization (iPlanner; Yang et al., 2023) [20]) demonstrate stronger long-range reasoning and transferability across domains— capabilities that are particularly valuable in unstructured navigation.”
Eisa, A., Gupta, M., Kong, A., et al. (2026) · Advanced Powder Technology · doi:10.1016/j.apt.2026.105304
Autonomous 3D Object Geolocation on UAVs Using Monocular Camera and Gimbaled Laser
Melkonyan, V., Sahakyan, V., Kirakosyan, L., et al. (2026) · Operations Research Forum · doi:10.1007/s43069-026-00684-5
“Within the passive category, monocular depth estimation models such as MiDaS [12] and DPT [13] infer the relative depth from a single image using learned priors.”
Smart Vision Aid for the Blind: Enhancing Exploration
Brehar, R. D., Elena-Andreea, S., Hulea, A.-F., & Muresan, M. P. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-3-032-17899-2_9
Pose-aware object recognition on a mobile platform via learned geometric representations
Beleznai, C., Ausserlechner, P., Kriegler, A., et al. (2022) · Asian Control Conference (ASCC) · doi:10.23919/ascc56756.2022.9828370
Zhang, K., Lin, J., Zhang, Z., & Heo, J. (2026) · Electronics · doi:10.3390/electronics15132963
“The compared reconstruction methods included CRM, LGM, DPTDepth [25], Hunyuan3D [26], ZoeDepth [27], SF3D [28], GaussianAnything [29], and the proposed method.”
GDC-depth: real-time high-fidelity head depth estimation for occlusion-aware AR displays
Yang, X., Wang, H., Chen, Y., et al. (2026) · Displays · doi:10.1016/j.displa.2026.103637
An Energy-Based Prior for Generative Saliency
Zhang, J., Xie, J., Barnes, N. M., & Li, P. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3290610 · Cited by 1
“Among vision transformers, DPT [49] adopts a U-shaped structure and uses ViT [43] as an encoder to perform semantic segmentation and monocular depth estimation.”
BackgroundMethodZero-Pose-Prior NeRF: Recursive Radiance Field Reconstruction From Unposed and Unordered Images
Liu, X., Zhang, Q., Wang, X., et al. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3666734
“…optimization stability for complex scenes [52], [62]: where D ∗ represents shifted and scaled invariant depth, which normalizes the per-frame monocular depth D predicted via o ff - the-shelf network DPT [63]: ˆ D denotes the depth map derived from NeRF obtained by volume rendering following Eq.”
Micro-Cells Enabling Spatio-Angular Joint Attention for High-Resolution Light Field Editing
Kim, G.-J., & Rhee, C. E. (2024) · IEEE Access · doi:10.1109/access.2024.3504730
“In the first step, geometry information is generated through monocular depth estimation [5], expanding the editing capabilities to encompass not only object removal but also object insertion and modification.”
InfluentialSegFly: A Dataset and 2D-3D-2D Paradigm for Aerial RGB-Thermal Semantic Segmentation at Scale
Gross, M., Matha, S. B., Song, R., Muthuveerappan, V., Christoph, C., Huber, J., & Cremers, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37624-4_29 · Cited by 1
Shyam, P., Okon, A., & Yoo, H. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00030
“To further improve performance, attention and transformer-based models [5, 27, 44, 49, 53] are used to correlate features from different regions of an image.”
MethodBoosting Text-Driven Video Segmentation via Geometry-Aware Distillation
Zhu, T., Liang, Y., Li, H., & Fu, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37252-9_24
Data Augmentation Method for Improving Vehicle Detection and Recognition Performance
Chen, X.-Z., Cheng, C.-P., & Chen, Y. (2022) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-taiwan55306.2022.9869222
“First, the depth image of the input image will be generated through deep learning techniques[6][7], then the point cloud information of the input image will be created.”
BackgroundFiros, V. M., Alphonse, P. J. A., Fiore, U., & Gangadharan, G. R. (2026) · Machine Vision and Applications · doi:10.1007/s00138-026-01913-x
“To address certain shortcomings of CNNs, vision trans-formers (ViT) [ 12] along with medically adapted hybrid frameworks such as TransUNet [13] and Swin-UNet [14] have emerged, leveraging self-attention mechanisms to capture broader global contextual information.”
Uncertainty-aware probabilistic streaming depth estimation for endoscopic video
Li, H., & Jiang, N. (2026) · The Visual Computer · doi:10.1007/s00371-026-04551-6
“This representation combines a Depth Anything 3 ViT-L image encoder [11], a Dense Prediction Transformer (DPT) decoder [16], and Mamba temporal modeling [6].”
InfluentialAfrina, R., Sarno, R., Sungkono, K. R., et al. (2026) · International Conference on Smart Computing, IoT and Machine Learning (SIML) · doi:10.1109/siml69834.2026.11621762
“The DPT represents the state-of-the-art in this domain [18].”
OV3DSeg-VGGT: Open-Vocabulary 3D Segmentation with Visual Geometry-Grounded Transformers
Zhou, J., Huang, X., Ren, Y., Fan, D., Gu, S., Tian, B., Liu, X., & Shao, T. (2026) · Visual Informatics · doi:10.1016/j.visinf.2026.100311
Efficient Token Fusion for Transformer-Based Semantic Segmentation
Zhang, Z., Zhang, T., & Xu, A. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8180-8_35
Hierarchical vision transformer model for polyp segmentation
Geetha, S., Gopakumar, C., & Vinod, V. (2023) · International Conference on Advances in Intelligent Computing and Applications (AICAPS) · doi:10.1109/aicaps57044.2023.10074447
“Our work uses progressive shrinking hierarchy and spatialreduction attention [13] but tries with simple convolution at the decoder part [16].”
MethodZhou, X., Zhu, C.-W., Bao, L.-X., et al. (2026) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2026.3696712
“In recent years, with the significant success in the natural language processing (NLP) domain, the Transformer architecture has been extensively applied to a variety of computer vision tasks [37], [38], [39], [40], [41], [42], [43], [44], including SOD task [45], [46], [47].”
Depth Estimation and Dynamic Synthesis of Single Frame Unnatural Images
余, 林. (2023) · Computer Science and Application · doi:10.12677/csa.2023.134070
Zhang, J., Wang, X., Wang, X., Zhu, C., & Zhu, L. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10625-9
MAIR++: Improving Multi-View Attention Inverse Rendering With Implicit Lighting Representation
Choi, J., Lee, S., Park, H., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3548679
“A pioneering work by Li et al. [1] conducted inverse rendering using a single image, and IRISformer [23] further improved the performance by replacing convolutional neural networks (CNNs) with a Transformer [29].”
Stitched ViTs are Flexible Vision Backbones
Pan, Z., Liu, J., He, H., Cai, J., & Zhuang, B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72940-9_15 · Cited by 3
Infrared Monocular Depth Estimation via Parameter-Efficient Adaptation
Pyun, J., Moon, S., Jeong, Y., & Kang, S. (2026) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic69189.2026.11386423
“We attach LoRA modules to the encoder and train the DPTHead [12] depth decoder end to end, while freezing all remaining backbone weights.”
InfluentialSun, W., Song, H., Jiang, Y., et al. (2025) · International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC) · doi:10.1109/icairc68035.2025.11385227
Swetha, K., N, A. K., Santhosh, L., et al. (2025) · doi:10.1109/icoiics67115.2025.11390458
Rethinking Encoder-Decoder Flow Through Shared Structures
Laboyrie, F., Yucel, M. K., & Saà-Garriga, A. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890765
Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker
Park, M., Lee, S., Choi, W., et al. (2023) · Proceedings e report · doi:10.36253/10.36253/979-12-215-0289-3.62
Liu, Z., Su, L., Yu, J., Geng, X., Wang, L. S., Wang, J., & Liu, J. (2026) · Biomedicines · doi:10.3390/biomedicines14091935 · Cited by 1
“It consists of a frozen UNI v1 encoder [47] and a multi-scale feature fusion decoder inspired by dense prediction transformer designs [64].”
Relative Pose Estimation Using Optical Flow-Guided Hybrid Supervision for Visual Odometry
Chen, X., Cao, Y., & Yu, H. (2026) · IEEE Access · doi:10.1109/access.2026.3688850
“On KITTI, we categorize the related work into three groups: geometric methods [1], [43], [44], as well as self-supervised [24], [25], [26], [34], [35], [36] and supervised [6], [19], [27], [28], [29], [45], [46], [47] in order to clearly distinguish different research paradigms.”
Analysis of Fractal Techniques using Python Technologies and Integration of Artificial Intelligence
Popa, B., Dorin, Ş., Ureche, G.-T., et al. (2025) · International Carpathian Control Conference · doi:10.1109/iccc65605.2025.11022799
“Vision Transformers [11] will also be explored as an alternative approach to handle the intricate recursive patterns of the Mandelbrot set, leveraging their global attention mechanism.”
AL-Net: Adaptive Learning for Enhanced Cell Nucleus Segmentation in Pathological Images
Chen, Z. X., Peng, S., Yang, R., Zhao, M., & Zhang, C. (2025) · Electronics · doi:10.3390/electronics14173507 · Cited by 1
Lightweight Thermal Udder Segmentation via Structured Pruning for On-Device Farm Deployment
Fonisto, M., Verde, M. T., Bonavolontà, F., Liccardo, A., Matera, R., Santinello, M., & Amato, F. (2026) · IEEE Access · doi:10.1109/access.2026.3665389
“We adopt FPN [9] as a low-overhead multi-scale decoder for hierarchical CNN features and DPT [11] as a ViT-friendly decoder for dense tasks.”
Shinde, S., Aundhakar, V. R., Gramopadhye, A. S., Atugade, S. S., & Avhad, P. M. (2026) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit69151.2026.11656588
TD-H:A Method for High-Quality Computer-Generated Holograms
Wang, Z., Dong, X., Zuo, J., & Geng, S. (2024) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai65059.2024.10893819
“To validate the effectiveness of the ADPT model, this study conducted depth prediction experiments on two datasets, comparing it with three state-of-the-art methods: SimIPU[21], DepthFormer[22], and DPT[23], as shown in Figure 3.”
RCLiteDepth: A Lightweight Framework for Robust Depth Estimation from 4D Radar and Camera
Cheng, P., Wei, X., Wang, L., et al. (2026) · International Conference on Image, Signal Processing and Pattern Recognition (ISPP) · doi:10.1109/ispp69262.2026.11542873
WildDepth: A Multimodal Dataset for 3D Wildlife Perception and Depth Estimation
Aamir, M., Muramatsu, N., Shin, S., Wijers, M., Zhong, J.-X., Hou, X., Patel, A., Loveridge, A. J., & Markham, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37422-6_3
Multi-Modal Data Fusion for Dynamic Target Depth Retrieval in Aquatic Environments
Liu, X., Xu, Z., & Ding, T. (2026) · Remote Sensing · doi:10.3390/rs18132230
From satellite image to glacier scenery: A novel GlacierPix2Pix model for glacier visualization
Duan, X. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/86/20241544
Ali, A. M., & Yu, J. (2026) · Sensors · doi:10.3390/s26165013
“[18] EfficientNet-B5 78 M 0.903 0.103 0.364 GLPDepth [42] ResNet-50 58 M 0.900 0.102 0.360 NeWCRFs [20] Swin-L 110 M 0.922 0.095 0.334 DPT [3] ViT-hybrid 123 M 0.904 0.109 0.357 BinsFormer [43] Swin-L 110 M 0.907 0.099 0.357 UniDepth v2 [4] ViT-L – 0.988 0.046 0.180 Metric3D v2 [23] ViT-L The…”
InfluentialUnified Egocentric Dual-Hand Mesh Reconstruction and Action Recognition from Monocular RGB
Kamal, Roy, A., Kalita, L., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-18477-1_30
Wang, A., Liao, G., Zhao, Z., Li, R., Fang, Z., Ma, S., & Hwang, J. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3711298
“Single-image methods [1], [2], [3], [4], [5], [6], [7], [8], [9], [10] leverage semantic cues and dense appearance information but remain inherently ill-posed due to scale ambiguity and limited geometric constraints.”
Temporally Stable Monocular Depth Estimation in Surgical Vision
Xu, J., Colleoni, E., Toussaint, N., Asad, M., Sánchez-Matilla, R., Mazomenos, E. B., Luengo, I., & Stoyanov, D. V. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-09784-2_8
Dual-branch Monocular Depth Estimation Method with Attention Mechanism
Zhou, C., He, L., Wang, H., et al. (2024) · International Conference on Electronic Technology and Information Science (ICETIS) · doi:10.1109/icetis61828.2024.10593699
“With the popularity of Transformer, many researchers [13][17][18][20][21][22] have introduced ViT(Vision Transformer) model [16] and Swin Transformer [19] into the field of monocular depth estimation and achieved good estimation results.”
MethodInfluentialRecover the Structure of Monocular Depth Estimation base ViT-CNNs
Yang, X., Chang, Q., & Liu, X. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00222
“ViT shows an amazing capability in almost of vision tasks including the monocular depth estimation, such as [4] [5] [7].”
BackgroundMMDiff: Extending Diffusion Transformers for Multi-modal Generation
Akarken, Y., Kupyn, O., & Rupprecht, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37020-4_15
Thomanek, D., & Gühmann, C. (2025) · Applied Sciences · doi:10.3390/app15105361
DSC: depth data quality optimization framework for RGBD camouflaged object detection
Zhang, D., Wang, C., Fu, Q., et al. (2025) · Open Physics · doi:10.1515/phys-2025-0236
“For instance, Wang et al. [13] employed New CRFs [17], Wu et al. [14] opted for DPT [18], while Bi et al. [9] and Liu et al. [15] utilized MiDaS [19].”
Art3D-Fusion: A Hybrid Framework for Visual Synthesis with Artistic Control
Wang, K., Chen, P., Liu, Z., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3398-5_8 · Cited by 1
Rosetum3D: A Large-Scale 3D Vision Dataset from Preharvest Roses
Liu, S., Liu, X., Liu, X., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37092-1_15
George, J., & Sherly, E. (2026) · Neural Computing and Applications · doi:10.1007/s00521-025-11812-3
Boosting 3D Foundation Models with Edge-Based Pose Optimization
D'Urso, M., Sormann, C., Rossi, M., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37235-2_28
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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