InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
Open accessAbstract
Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that our model not only has the large effective receptive field required for downstream tasks such as detection and segmentation, but also has the adaptive spatial aggregation conditioned by input and task information. As a result, the proposed InternImage reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large-scale parameters from massive data like ViTs. The effectiveness of our model is proven on challenging benchmarks including ImageNet, COCO, andADE20K. It is worth mentioning that InternImage-H achieved a new record 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K, outperforming current leading CNNs and ViTs.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 · see citing works
Vaswani, 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 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
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 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534 · see citing works
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137 · see citing works
Language 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 · see citing works
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392 · see citing works
An 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 21,793 · see citing works
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · see citing works
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054 · see citing works
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015 · see citing works
Self-citationEfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072 · see citing works
Layer NormalizationPreprint
Ba, J., Kiros, J., & Hinton, G. E. (2016) · arXiv.org · doi:10.48550/arxiv.1607.06450 · Cited by 13,745 · see citing works
Aggregated Residual Transformations for Deep Neural Networks
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.634 · Cited by 12,465 · see citing works
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., & Xie, S. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01167 · Cited by 8,494 · see citing works
Rethinking Atrous Convolution for Semantic Image Segmentation
Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1706.05587 · Cited by 7,517 · see citing works
Deformable Convolutional Networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513 · see citing works
Self-citationPyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00061 · Cited by 5,259 · cited 5× · see citing works
“To address this problem, PVT [10, 11] and Linformer [45] performed global attention on the downsampled key and value maps, DAT [46] employed deformable attention to sparsely sample information from value maps, while HaloNet [47] and Swin transformer [2] developed local attention mechanisms and used haloing and shift o…”
BackgroundMethodInfluentialSelf-citationSegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021) · arXiv · doi:10.48550/arxiv.2105.15203 · Cited by 5,090 · see citing works
Self-citationMasked-attention Mask Transformer for Universal Image Segmentation
Cheng, B., Misra, I., Schwing, A. G., Kirillov, A., & Girdhar, R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00135 · Cited by 3,565 · see citing works
Gaussian Error Linear Units (GELUs)
Hendrycks, D., & Gimpel, K. (2016) · arXiv · doi:10.48550/arxiv.1606.08415 · Cited by 3,537 · see citing works
Scene Parsing through ADE20K Dataset
Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., & Torralba, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.544 · Cited by 3,326 · see citing works
Deformable ConvNets V2: More Deformable, Better Results
Zhu, X., Hu, H., Lin, S., & Dai, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00953 · Cited by 2,943 · see citing works
Self-citationSwin Transformer V2: Scaling Up Capacity and Resolution
Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., et al. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01170 · Cited by 2,741 · see citing works
Swin Transformer V2: Scaling Up Capacity and Resolution
Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., et al. (2021) · arXiv · doi:10.48550/arxiv.2111.09883 · Cited by 2,679 · see citing works
CvT: Introducing Convolutions to Vision Transformers
Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., & Zhang, L. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00009 · Cited by 2,649 · see citing works
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590 · see citing works
Self-citationPvt v2: Improved baselines with pyramid vision transformer
Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0274-8 · Cited by 2,439 · see citing works
Self-citationSelf-Training With Noisy Student Improves ImageNet Classification
Xie, Q., Luong, M.-T., Hovy, E., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01070 · Cited by 2,393 · see citing works
Unified Perceptual Parsing for Scene Understanding
Xiao, T., Liu, Y., Zhou, B., Jiang, Y., & Sun, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01228-1_26 · Cited by 2,201 · see citing works
Cascade R-CNN: High Quality Object Detection and Instance Segmentation
Cai, Z., & Vasconcelos, N. M. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2956516 · Cited by 1,984 · see citing works
Deformable DETR: Deformable Transformers for End-to-End Object Detection
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., & Dai, J. (2020) · arXiv · doi:10.48550/arxiv.2010.04159 · Cited by 1,837 · cited 1× · see citing works
“for the deficiencies of regular convolution in terms of longrange dependencies and adaptive spatial aggregation; (2) Compared with attention-based operators such as common MHSA and closely-related deformable attention [46, 56], this operator inherits the inductive bias of convolution, making our model more efficient w…”
MethodSelf-citationScaling Laws for Neural Language Models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020) · arXiv · Cited by 1,749 · cited 1× · see citing works
“Scaling up models is an important strategy to improve feature representation quality, which has been well-studied in the natural language processing (NLP) domain [48].”
BackgroundOn Layer Normalization in the Transformer ArchitecturePreprint
Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H.-S., Lan, Y.-Y., Wang, L.-W., & Liu, T.-Y. (2020) · International Conference on Machine Learning · doi:10.48550/arxiv.2002.04745 · Cited by 1,709 · see citing works
Thomée, B., Shamma, D. A., Friedland, G. H., Elizalde, B., Ni, K. S., Poland, D., Borth, D., & Li, L.-J. (2016) · Communications of the ACM · doi:10.1145/2812802 · Cited by 1,632 · see citing works
Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs
Ding, X., Zhang, X., Han, J., & Ding, G. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01166 · Cited by 1,511 · see citing works
EfficientNetV2: Smaller Models and Faster TrainingPreprint
Tan, M., & Le, Q. V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00298 · Cited by 1,473 · see citing works
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows
Dong, X., Bao, J., Chen, D., Zhang, W., Yu, N., Yuan, L., Chen, D., & Guo, B. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01181 · Cited by 1,458 · see citing works
Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., & Wang, Y. (2021) · arXiv · doi:10.48550/arxiv.2103.00112 · Cited by 1,394 · cited 1× · see citing works
“With the remarkable success of transformers in large-scale language models [3–8], vision transformers (ViTs) [2, 9–15] have also swept the computer vision field and are becoming the primary choice for the research and practice of large-scale vision foundation models.”
BackgroundMetaFormer is Actually What You Need for Vision
Yu, W., Luo, M., Zhou, P., Si, C., Zhou, Y., Wang, X., Feng, J., & Yan, S. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01055 · Cited by 1,380 · see citing works
Self-citationDINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L. M., & Shum, H.-Y. (2022) · arXiv · Cited by 1,154 · see citing works
Dynamic Head: Unifying Object Detection Heads with Attentions
Dai, X., Chen, Y., Xiao, B., Chen, D., Liu, M., Yuan, L., & Zhang, L. (2021) · 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/cvpr46437.2021.00729 · Cited by 1,126 · see citing works
Big Transfer (BiT): General Visual Representation Learning
Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., & Houlsby, N. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58558-7_29 · Cited by 1,039 · see citing works
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
Fedus, W., Zoph, B., & Shazeer, N. (2021) · arXiv · doi:10.48550/arxiv.2101.03961 · Cited by 1,005 · see citing works
Vision Transformer Adapter for Dense PredictionsPreprint
Chen, Z., Duan, Y., Wang, W., He, J.-J., Lu, T., Dai, J.-F., & Qiao, Y. (2022) · International Conference on Learning Representations · doi:10.48550/arxiv.2205.08534 · Cited by 959 · see citing works
Self-citationCoAtNet: Marrying Convolution and Attention for All Data Sizes
Dai, Z., Liu, H., Le, Q. V., & Tan, M. (2021) · arXiv · doi:10.48550/arxiv.2106.04803 · Cited by 925 · see citing works
Exploring Plain Vision Transformer Backbones for Object Detection
Li, Y., Mao, H., Girshick, R., & He, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20077-9_17 · Cited by 903 · see citing works
Vision Transformer with Deformable Attention
Xia, Z., Pan, X., Song, S.-J., Li, L.-E.-R., & Huang, G. (2022) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.00475 · Cited by 902 · see citing works
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., & Catanzaro, B. (2019) · arXiv · doi:10.48550/arxiv.1909.08053 · Cited by 893 · see citing works
Zhai, X., Kolesnikov, A., Houlsby, N., & Beyer, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01179 · Cited by 891 · see citing works
Linformer: Self-Attention with Linear Complexity
Wang, S., Li, B. Z., Khabsa, M., Fang, H., & Ma, H. (2020) · arXiv · doi:10.48550/arxiv.2006.04768 · Cited by 867 · see citing works
Objects365: A Large-Scale, High-Quality Dataset for Object Detection
Shao, S., Li, Z., Zhang, T., Peng, C., Yu, G., Zhang, X., Li, J., & Sun, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00852 · Cited by 673 · cited 5× · see citing works
“For this dataset, we initialize our InternImage-H with the Objects365 [79]”
MethodInfluentialSelf-citationAn Empirical Study of Spatial Attention Mechanisms in Deep Networks
Zhu, X., Cheng, D., Zhang, Z., Lin, S., & Dai, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00679 · Cited by 659 · see citing works
Self-citationConceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
Changpinyo, S., Sharma, P. K., Ding, N., & Soricut, R. (2021) · 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/cvpr46437.2021.00356 · Cited by 626 · see citing works
End-to-End Semi-Supervised Object Detection with Soft Teacher
Xu, M., Zhang, Z., Hu, H., Wang, J., Wang, L., Wei, F., Bai, X., & Liu, Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00305 · Cited by 582 · see citing works
Scaling Local Self-Attention for Parameter Efficient Visual Backbones
Vaswani, A., Ramachandran, P., Srinivas, A., Parmar, N., Hechtman, B. A., & Shlens, J. (2021) · 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/cvpr46437.2021.01270 · Cited by 483 · see citing works
DaViT: Dual Attention Vision Transformers
Ding, M., Xiao, B., Codella, N., Luo, P., Wang, J., & Yuan, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20053-3_5 · Cited by 430 · see citing works
Touvron, H., Cord, M., & Jégou, H. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20053-3_30 · Cited by 409 · see citing works
Florence: A New Foundation Model for Computer Vision
Yuan, L., Chen, D., Chen, Y.-L., Codella, N., Dai, X., Gao, J., Hu, H., Huang, X., Li, B., Li, C., et al. (2021) · arXiv · doi:10.48550/arxiv.2111.11432 · Cited by 389 · see citing works
LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
Schuhmann, C., Vencu, R., Beaumont, R., Kaczmarczyk, R., Mullis, C., Katta, A., Coombes, T., Jitsev, J., & Komatsuzaki, A. (2021) · arXiv · doi:10.48550/arxiv.2111.02114 · Cited by 358 · see citing works
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using SparsityPreprint
Liu, S., Chen, T.-L., Chen, X.-H., Chen, X.-X., Xiao, Q., Wu, B.-Q., Kärkkäinen, T., Pechenizkiy, M., Mocanu, D., & Wang, Z.-Y. (2022) · International Conference on Learning Representations · doi:10.48550/arxiv.2207.03620 · Cited by 303 · see citing works
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions
Rao, Y., Zhao, W., Tang, Y., Zhou, J., Lim, S. N., & Lu, J. (2022) · neural information processing systems · doi:10.52202/068431-0752 · Cited by 295 · see citing works
Scaling Vision with Sparse Mixture of ExpertsPreprint
Riquelme, C., Puigcerver, J., Mustafa, B., Neumann, M., Jenatton, R., Pinto, A. S., Keysers, D., & Houlsby, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05974 · Cited by 287 · see citing works
Yang, J., Li, C., Dai, X., & Gao, J. (2022) · neural information processing systems · doi:10.52202/068431-0304 · Cited by 285 · see citing works
XCiT: Cross-Covariance Image TransformersPreprint
El-Nouby, A., Touvron, H., Caron, M., Bojanowski, P., Douze, M., Joulin, A., Laptev, I., Neverova, N., Synnaeve, G., Verbeek, J. J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09681 · Cited by 273 · see citing works
XCiT: Cross-Covariance Image TransformersPreprint
Ali, A., Touvron, H., Caron, M., Bojanowski, P., Douze, M., Joulin, A., Laptev, I., Neverova, N., Synnaeve, G., Verbeek, J. J., et al. (2021) · arXiv (Cornell University) · Cited by 242 · see citing works
CBNet: A Composite Backbone Network Architecture for Object Detection
Liang, T., Chu, X., Liu, Y., Wang, Y., Tang, Z., Chu, W., Chen, J., & Ling, H. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3216771 · Cited by 194 · see citing works
Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., & Jégou, H. (2020) · arXiv · doi:10.48550/arxiv.2012.12877 · Cited by 182 · see citing works
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksPreprint
Wang, W., Bao, H., Li, D., Björck, J., Peng, Z., Liu, Q., Aggarwal, K., Mohammed, O. K., Singhal, S., Som, S., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2208.10442 · Cited by 163 · see citing works
Self-citationContrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature DistillationPreprint
Wei, Y., Hu, H., Xie, Z., Zhang, Z., Cao, Y., Bao, J., Chen, D., & Guo, B. (2022) · arXiv.org · doi:10.48550/arxiv.2205.14141 · Cited by 148 · see citing works
On the Connection between Local Attention and Dynamic Depth-wise ConvolutionPreprint
Han, Q., Fan, Z., Dai, Q., Sun, L., Cheng, M., Liu, J., & Wang, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.04263 · Cited by 137 · see citing works
Towards All-in-One Pre-Training via Maximizing Multi-Modal Mutual Information
Su, W., Zhu, X., Tao, C., Lu, L., Li, B., Huang, G. M., Qiao, Y., Wang, X., Zhou, J., & Dai, J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01525 · Cited by 52 · see citing works
Self-citation
Flagged first, then by citations
7 of the 85 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
DINOv2: Learning Robust Visual Features without Supervision
Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al. (2023) · arXiv · doi:10.48550/arxiv.2304.07193 · Cited by 1,078 · see citing works
Intern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
Chen, Z., Wu, J., Wang, W., Su, W., Chen, G., Xing, S., Zhong, M., Zhang, Q., Zhu, X., Lu, L., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02283 · Cited by 734 · see citing works
Self-citationReproducible Scaling Laws for Contrastive Language-Image Learning
Cherti, M., Beaumont, R., Wightman, R., Wortsman, M., Ilharco, G., Gordon, C., Schuhmann, C., Schmidt, L., & Jitsev, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00276 · Cited by 673 · see citing works
DETRs with Collaborative Hybrid Assignments Training
Zong, Z., Song, G., & Liu, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00621 · Cited by 650 · see citing works
A survey of the vision transformers and their CNN-transformer based variants
Khan, A., Rauf, Z., Sohail, A., Khan, A. R., Asif, H., Asif, A., & Farooq, U. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10595-0 · Cited by 496 · see citing works
InceptionNeXt: When Inception Meets ConvNeXt
Yu, W., Zhou, P., Yan, S., & Wang, X. (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/cvpr52733.2024.00542 · Cited by 475 · see citing works
Self-citationYang, C., Chen, Y., Tian, H., Tao, C., Zhu, X., Zhang, Z., Huang, G. M., Li, H., Qiao, Y., Lu, L., et al. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01710 · Cited by 339 · see citing works
Self-citationRemote Sensing Object Detection in the Deep Learning Era—A Review
Gui, S., Song, S., Qin, R., & Tang, Y. (2024) · Remote Sensing · doi:10.3390/rs16020327 · Cited by 331 · see citing works
EVA-02: A visual representation for neon genesis
Fang, Y., Sun, Q., Wang, X., Huang, T., Wang, X., & Cao, Y. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105171 · Cited by 312 · see citing works
Self-citationOn the challenges and perspectives of foundation models for medical image analysis
Zhang, S., & Metaxas, D. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102996 · Cited by 288 · see citing works
MambaOut: Do We Really Need Mamba for Vision?
Yu, W., & Wang, X. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00423 · Cited by 288 · see citing works
Self-citationEfficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications
Xiong, Y., Li, Z., Chen, Y., Wang, F., Zhu, X., Luo, J., Wang, W., Lü, T., Li, H., Qiao, 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/cvpr52733.2024.00540 · Cited by 284 · see citing works
Self-citationRMT: Retentive Networks Meet Vision Transformers
Fan, Q., Huang, H., Chen, M., Liu, H., & He, R. (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/cvpr52733.2024.00539 · Cited by 278 · see citing works
Large AI Models in Health Informatics: Applications, Challenges, and the Future
Qiu, J., Li, L., Sun, J., Peng, J., Shi, P., Zhang, R., Dong, Y., Lam, K., Lo, F. P.-W., Xiao, B. J., et al. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3316750 · Cited by 246 · see citing works
ELA: efficient location attention for deep convolution neural networks
Xu, W., Wan, Y., & Zhao, W. (2025) · Journal of Real-Time Image Processing · doi:10.1007/s11554-025-01719-6 · Cited by 237 · see citing works
Scale-Aware Modulation Meet Transformer
Lin, W., Wu, Z., Chen, J., Huang, J., & Jin, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00553 · Cited by 229 · see citing works
HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model
Wang, D., Hu, M., Jin, Y., Miao, Y., Yang, J., Xu, Y., Qin, X., Ma, J., Sun, L., Li, C., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3557581 · Cited by 229 · see citing works
Deep-Learning-Based Semantic Segmentation of Remote Sensing Images: A Survey
Huang, L., Jiang, B., Lv, S., Liu, Y., & Fu, Y. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3335891 · Cited by 175 · see citing works
DDP: Diffusion Model for Dense Visual Prediction
Ji, Y., Chen, Z., Xie, E., Hong, L., Liu, X., Liu, Z., Lü, T., Li, Z., & Luo, P. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01987 · Cited by 171 · see citing works
Self-citationSparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videos
Liu, H., Teng, Y., Lu, T., Wang, H., & Wang, L. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01703 · Cited by 154 · see citing works
Self-citationXia, C., Wang, X., Lv, F., Hao, X., & Shi, Y. (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/cvpr52733.2024.00525 · Cited by 147 · see citing works
Self-citationLou, M., Zhang, S., Zhou, H.-Y., Yang, S., Wu, C., & Yu, Y. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3550979 · Cited by 142 · see citing works
ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation
Zhao, X., Wu, X., Chen, W., Chen, P. C. Y., Xu, Q., & Li, Z. G. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3271000 · Cited by 137 · see citing works
Self-citationStudent Behavior Detection in the Classroom Based on Improved YOLOv8
Chen, H., Zhou, G., & Jiang, H. (2023) · Sensors · doi:10.3390/s23208385 · Cited by 136 · see citing works
MTP: Advancing Remote Sensing Foundation Model via Multitask Pretraining
Wang, D., Zhang, J., Xu, M., Liu, L., Wang, D., Gao, E., Han, C., Guo, H., Du, B., Tao, D., et al. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3408154 · Cited by 136 · see citing works
Contextual Object Detection with Multimodal Large Language Models
Zang, Y., Li, W., Han, J., Zhou, K., & Loy, C. C. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02214-4 · Cited by 130 · see citing works
A New Learning Paradigm for Foundation Model-Based Remote-Sensing Change Detection
Li, K., Cao, X., & Meng, D. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3365825 · Cited by 125 · see citing works
DSW-YOLO: A detection method for ground-planted strawberry fruits under different occlusion levels
Du, X., Cheng, H., Ma, Z., Lu, W., Wang, M., Meng, Z., Jiang, C., & Hong, F. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108304 · Cited by 124 · see citing works
Few-Shot Segmentation via Divide-and-Conquer Proxies
Lang, C., Cheng, G., Tu, B., & Han, J. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01886-8 · Cited by 121 · see citing works
Frequency Dynamic Convolution for Dense Image Prediction
Chen, L., Gu, L., Li, L., Yan, C., & Fu, Y. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02809 · Cited by 113 · see citing works
Dynamic YOLO for small underwater object detection
Chen, J., & Er, M. J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10788-1 · Cited by 110 · see citing works
MGSFA-Net: Multiscale Global Scattering Feature Association Network for SAR Ship Target Recognition
Zhang, X., Feng, S., Zhao, C., Sun, Z., Zhang, S., & Ji, K. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3357171 · Cited by 110 · see citing works
DBW-YOLO: A High-Precision SAR Ship Detection Method for Complex Environments
Tang, X., Zhang, J., Xia, Y., & Xiao, H. L. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3376558 · Cited by 91 · see citing works
Advances in the Neural Network Quantization: A Comprehensive Review
Lu, W., Ma, Z., Yang, C., & Yao, Q. (2024) · Applied Sciences · doi:10.3390/app14177445 · Cited by 90 · see citing works
PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation
Wang, Y., Chen, Y., Liao, X., Fan, L., & Zhang, Z. (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/cvpr52733.2024.01624 · Cited by 89 · see citing works
Fu, Y., Lou, M., & Yu, Y. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01777 · Cited by 87 · see citing works
5%>100%: Breaking Performance Shackles of Full Fine-Tuning on Visual Recognition Tasks
Yin, D., Hu, L., Li, B., Zhang, Y., & Yang, X. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01869 · Cited by 87 · see citing works
Harnessing large vision and language models in agriculture: a review
Zhu, H., Qin, S., Su, M., Lin, C., Li, A., & Gao, J. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1579355 · Cited by 83 · see citing works
OmniVec: Learning robust representations with cross modal sharing
Srivastava, S., & Sharma, G. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00127 · Cited by 80 · see citing works
Exploring Recurrent Long-Term Temporal Fusion for Multi-View 3D Perception
Han, C., Yang, J., Sun, J., Ge, Z., Dong, R., Zhou, H., Mao, W., Peng, Y., & Zhang, X. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3401172 · Cited by 79 · see citing works
Banerjee, T., Singh, D. P., Swain, D., Mahajan, S., Kadry, S., & Kim, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-12602-6 · Cited by 76 · see citing works
A Lightweight Transformer Network for Hyperspectral Image Classification
Zhang, X., Su, Y., Gao, L., Bruzzone, L., Gu, X., & Tian, Q. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3297858 · Cited by 75 · see citing works
Development and challenges of object detection: A survey
Li, Z., Dong, Y., Shen, L., Liu, Y., Pei, Y., Yang, H., Zheng, L., & Ma, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128102 · Cited by 74 · see citing works
Self-citationRoad defect detection based on improved YOLOv8s model
Wang, J., Meng, R., Huang, Y., Zhou, L., Huo, L., Qiao, Z., & Niu, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-67953-3 · Cited by 73 · see citing works
DBS-YOLO: A High-Precision Object Detection Algorithm for Hazardous Waste Images
Xiao, Z., Yang, G., Wang, X., Yu, G., Wang, X., Yang, B., & Zhang, D. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3476526 · Cited by 70 · see citing works
Self-citationM3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dataset
Chaney, K. P., Cladera, F., Wang, Z., Bisulco, A., Hsieh, M. A., Korpela, C., Kumar, V., Taylor, C. J., & Daniilidis, K. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00419 · Cited by 69 · see citing works
Real-time detection and counting of wheat ears based on improved YOLOv7
Li, Z., Zhu, Y., Sui, S., Zhao, Y., Li, X., & Li, X. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108670 · Cited by 69 · see citing works
Self-citationFrequency-Guided Spatial Adaptation for Camouflaged Object Detection
Zhang, S., Kong, D., Xing, Y., Lu, Y., Ran, L., Liang, G., Wang, H., & Zhang, Y. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3521681 · Cited by 69 · see citing works
Foundation models meet visualizations: Challenges and opportunities
Yang, W., Liu, M., Wang, Z., & Liu, S. (2024) · Computational Visual Media · doi:10.1007/s41095-023-0393-x · Cited by 67 · see citing works
Scaling Spike-Driven Transformer With Efficient Spike Firing Approximation Training
Yao, M., Qiu, X., Hu, T., Hu, J., Chou, Y., Tian, K., Liao, J., Leng, L., Xu, B., & Li, G. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3530246 · Cited by 67 · see citing works
BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection
Li, Z., Lan, S., Álvarez, J. M., & Wu, Z. (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/cvpr52733.2024.01901 · Cited by 64 · see citing works
Self-citationSTRAW-YOLO: A detection method for strawberry fruits targets and key points
Ma, Z., Dong, N., Gu, J., Cheng, H., Meng, Z., & Du, X. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109853 · Cited by 64 · see citing works
An open flame and smoke detection dataset for deep learning in remote sensing based fire detection
Wang, M., Yue, P., Jiang, L., Yu, D., Tuo, T., & Li, J. (2024) · Geo-spatial Information Science · doi:10.1080/10095020.2024.2347922 · Cited by 62 · see citing works
The role of large language models in agriculture: harvesting the future with LLM intelligence
Shaikh, T. A., Rasool, T., Veningston, K., & Yaseen, S. M. (2024) · Progress in Artificial Intelligence · doi:10.1007/s13748-024-00359-4 · Cited by 61 · see citing works
Merging Context Clustering With Visual State Space Models for Medical Image Segmentation
Zhu, Y., Zhang, D., Lin, Y., Feng, Y., & Tang, J. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3525673 · Cited by 60 · see citing works
MultiSenseSeg: A Cost-Effective Unified Multimodal Semantic Segmentation Model for Remote Sensing
Wang, Q., Chen, W., Huang, Z., Tang, H., & Yang, L. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3390750 · Cited by 58 · see citing works
Self-citationTemporal Enhanced Training of Multi-view 3D Object Detector via Historical Object Prediction
Zong, Z., Jiang, D., Song, G., Xue, Z., Su, J., Li, H., & Liu, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00350 · Cited by 57 · see citing works
Self-citationAncilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things
Pazho, A. D., Neff, C., Noghre, G. A., Ardabili, B. R., Yao, S., Baharani, M., & Tabkhi, H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3263725 · Cited by 56 · see citing works
Self-Attention and Transformers: Driving the Evolution of Large Language Models
Luo, Q., Zeng, W., Chen, M., Peng, G., Yuan, X., & Yin, Q. (2023) · IEEE International Conference on Electronic Information and Communication Technology (ICEICT) · doi:10.1109/iceict57916.2023.10245906 · Cited by 56 · see citing works
Li, X., Cai, M., Tan, X., Yin, C., Chen, W., Liu, Z., Wen, J., & Han, Y. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109160 · Cited by 56 · see citing works
Visual Point Cloud Forecasting Enables Scalable Autonomous Driving
Yang, Z., Chen, L., Sun, Y., & Li, H. (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/cvpr52733.2024.01390 · Cited by 56 · see citing works
Self-citationUrban attractiveness according to ChatGPT: Contrasting AI and human insights
Malekzadeh, M., Willberg, E. S., Torkko, J., & Toivonen, T. (2024) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2024.102243 · Cited by 56 · see citing works
Towards All-in-One Pre-Training via Maximizing Multi-Modal Mutual Information
Su, W., Zhu, X., Tao, C., Lu, L., Li, B., Huang, G. M., Qiao, Y., Wang, X., Zhou, J., & Dai, J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01525 · Cited by 52 · see citing works
Self-citationChen, G., Hou, Y., Cui, T., Li, H., Shangguan, F., & Cao, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-65293-w · Cited by 52 · see citing works
Self-citationKang, F., Huang, B., & Wan, G. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.105971 · Cited by 52 · see citing works
López, L., Suárez–Ramírez, J., Alemán-Flores, M., & Monzón, N. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106231 · Cited by 52 · see citing works
Wang, S., Anastasiu, D. C., Tang, Z., Chang, M., Yao, Y., Zheng, L., Rahman, M. S., Arya, M. S., Sharma, A., Chakraborty, P., et al. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00722 · Cited by 51 · see citing works
Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey
Shi, K., He, S., Shi, Z., An-jun, C., Xiong, Z., Chen, J., & Luo, J. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3599596 · Cited by 51 · see citing works
Zhang, R., Liu, D., Bai, Q., Fu, L., Hu, J., & Song, J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108045 · Cited by 49 · see citing works
Contextrast: Contextual Contrastive Learning for Semantic Segmentation
Sung, C., Kim, W.-H., An, J., Lee, W., Lim, H., & Myung, H. (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/cvpr52733.2024.00358 · Cited by 48 · see citing works
CEDNet: A cascade encoder–decoder network for dense prediction
Zhang, G., Li, Z., Tang, C., Li, J., & Hu, X. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111072 · Cited by 47 · see citing works
Self-citationBerardini, D., Migliorelli, L., Galdelli, A., Frontoni, E., Mancini, A., & Moccia, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16231-x · Cited by 46 · see citing works
Semantic Hierarchy-Aware Segmentation
Li, L., Wang, W., Zhou, T., Quan, R., & Yang, Y. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3332435 · Cited by 46 · see citing works
Self-citationText-Image Alignment for Diffusion-Based Perception
Kondapaneni, N., Marks, M., Knott, M., Guimarães, R., & Perona, P. (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/cvpr52733.2024.01317 · Cited by 46 · see citing works
Brain-Inspired Remote Sensing Foundation Models and Open Problems: A Comprehensive Survey
Jiao, L., Huang, Z., Lu, X., Liu, X., Yang, Y., Zhao, J., Zhang, J., Hou, B., Yang, S., Liu, F., et al. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3316302 · Cited by 45 · see citing works
Self-citationWavelet Transform Feature Enhancement for Semantic Segmentation of Remote Sensing Images
Li, Y., Liu, Z., Yang, J., & Zhang, H. (2023) · Remote Sensing · doi:10.3390/rs15245644 · Cited by 45 · see citing works
On-site soil analysis: A novel approach combining NIR spectroscopy, remote sensing and deep learning
Kok, M., Sarjant, S., Verweij, S. E., Vaessen, S. F. C., & Ros, G. H. (2024) · Geoderma · doi:10.1016/j.geoderma.2024.116903 · Cited by 45 · see citing works
When Geoscience Meets Foundation Models: Toward a general geoscience artificial intelligence system
Zhang, H., Xu, J., Cui, H., Li, L., Yang, Y., Tang, C., & Boers, N. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3496478 · Cited by 45 · see citing works
Yao, F., Lu, W., Yang, H., Xu, L., Liu, C., Hu, L., Yu, H., Liu, N., Deng, C., Tang, D., et al. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3316166 · Cited by 44 · see citing works
Zhang, Y., Li, B., Shang, J., Huang, X., Zhai, P., & Geng, C. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3331418 · Cited by 42 · see citing works
OmniVec2 - A Novel Transformer Based Network for Large Scale Multimodal and Multitask Learning
Srivastava, S., & Sharma, G. (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/cvpr52733.2024.02588 · Cited by 42 · see citing works
Zheng, T., Qin, H., Cui, Y., Wang, R., Zhao, W., Zhang, S., Geng, S., & Zhao, L. (2023) · BMC Medical Imaging · doi:10.1186/s12880-023-01011-8 · Cited by 41 · see citing works
A small sample nonstandard gear surface defect detection method
Zhou, F., Chao, Y., Wang, C., Zhang, X., Li, H., & Song, X. (2023) · Measurement · doi:10.1016/j.measurement.2023.113472 · Cited by 41 · see citing works
Self-citationLCFNets: Compensation Strategy for Real-Time Semantic Segmentation of Autonomous Driving
Yang, L., Bai, Y., Ren, F., Bi, C., & Zhang, R. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3363830 · Cited by 41 · see citing works
MetaSeg: MetaFormer-based Global Contexts-aware Network for Efficient Semantic Segmentation
Kang, B., Moon, S., Cho, Y., Yu, H., & Kang, S. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00050 · Cited by 41 · see citing works
A new lightweight network for efficient UAV object detection
Hua, W., Chen, Q., & Chen, W. (2024) · Scientific Reports · doi:10.1038/s41598-024-64232-z · Cited by 41 · see citing works
DCEA: DETR With Concentrated Deformable Attention for End-to-End Ship Detection in SAR Images
Lin, H., Liu, J., Li, X., Wei, L., Liu, Y., Han, B., & Wu, Z. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3461723 · Cited by 41 · see citing works
Spatial Frequency Modulation for Semantic Segmentation
Chen, L., Fu, Y., Gu, L., Zheng, D., & Dai, J. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3592621 · Cited by 41 · see citing works
Self-citationSemi-supervised crack detection using segment anything model and deep transfer learning
Li, J., Yuan, C., Wang, X., Chen, G., & Ma, G. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105899 · Cited by 40 · see citing works
Self-citationObject Detection and X-Ray Security Imaging: A Survey
Wu, J., Xu, X., & Yang, J. (2023) · IEEE Access · doi:10.1109/access.2023.3273736 · Cited by 39 · see citing works
Tu, Z., Yang, X., He, X. G., Yan, J., & Xu, T. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3359095 · Cited by 39 · see citing works
Berardini, D., Migliorelli, L., Galdelli, A., & Marín-Jiménez, M. J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109684 · Cited by 39 · see citing works
DehazeDCT: Towards Effective Non-Homogeneous Dehazing via Deformable Convolutional Transformer
Dong, W., Zhou, H., Wang, R., Liu, X., Zhai, G., & Chen, J. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00642 · Cited by 38 · see citing works
Huang, J., Zhang, X., Jia, L., & Zhou, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110214 · Cited by 38 · see citing works
Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review
Shi, Y., Jiang, K., Li, J., Qian, Z., Wen, J., Yang, M., Wang, K., & Yang, D. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3495045 · Cited by 38 · see citing works
3L-YOLO: A Lightweight Low-Light Object Detection Algorithm
Han, Z., Yue, Z., & Liu, L. (2024) · Applied Sciences · doi:10.3390/app15010090 · Cited by 37 · see citing works
Explainability and vision foundation models: A survey
Kazmierczak, R., Berthier, É., Frehse, G., & Franchi, G. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103184 · Cited by 37 · see citing works
MonoOcc: Digging into Monocular Semantic Occupancy Prediction
Zheng, Y., Li, X., Li, P., Zheng, Y., Jin, B., Zhong, C., Long, X., Zhao, H., & Zhang, Q. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611261 · Cited by 36 · see citing works
Review of Deep Learning-Based Image Inpainting Techniques
Yang, J., & Ruhaiyem, N. I. R. (2024) · IEEE Access · doi:10.1109/access.2024.3461782 · Cited by 36 · see citing works
Flexible and Fully Quantized Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems
Moosmann, J., Müller, H., Zimmerman, N., Rutishauser, G., Benini, L., & Magno, M. (2024) · IEEE Access · doi:10.1109/access.2024.3404878 · Cited by 35 · see citing works
- Authors
- Wang, W., Dai, J., Chen, Z., Huang, Z., Li, Z., Zhu, X., Hu, X., Lu, T., Lu, L., Li, H., Wang, X., & Qiao, Y.
- Affiliations
- Shanghai Artificial Intelligence Laboratory (CN)
- Sense Time Research
- Nanjing University
- Chinese University of Hong Kong (HK)
- ORCID
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 14408-14419
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386076222
- Cited by
- 1,080
- References
- 85
- Citations by year
- 2026: 197 · 2025: 382 · 2024: 362 · 2023: 138 · 2022: 1
- FWCI
- 115.39
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Natural Science Foundation of China (61672273,61832008)
- 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 › Visual Attention and Saliency Detection
- Keywords
- deformable convolution, receptive fields, inductive bias, object detection, semantic segmentation, image classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
