AdaViT: Adaptive Vision Transformers for Efficient Image Recognition
Abstract
Built on top of self-attention mechanisms, vision transformers have demonstrated remarkable performance on a variety of tasks recently. While achieving excellent performance, they still require relatively intensive computational cost that scales up drastically as the numbers of patches, self-attention heads and transformer blocks increase. In this paper, we argue that due to the large variations among images, their need for modeling long-range dependencies between patches differ. To this end, we introduce AdaViT, an adaptive computation framework that learns to derive usage policies on which patches, self-attention heads and transformer blocks to use throughout the backbone on a per-input basis, aiming to improve inference efficiency of vision transformers with a minimal drop of accuracy for image recognition. Optimized jointly with a transformer backbone in an end-to-end manner, a light-weight decision network is attached to the backbone to produce decisions on-the-fly. Extensive experiments on ImageNet demonstrate that our method obtains more than 2 × improvement on efficiency compared to state-of-the-art vision transformers with only 0.8% drop of accuracy, achieving good efficiency/accuracy trade-offs conditioned on different computational budgets. We further conduct quantitative and qualitative analysis on learned usage polices and provide more insights on the redundancy in vision transformers. Code is available at ht tps: / / gi thub. com/MengLcool/AdaVi T.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Token pruning's information loss limits how many tokens can reasonably be reduced.
“Yet, token pruning has several disadvantages: the information loss from pruning limits how many tokens you can reasonably reduce;”
From Token Merging: Your ViT But Faster (2022) · cites this paper as Meng et al., 2022
Current token pruning methods require re-training the model to be effective, some with extra parameters.
“current methods require re-training the model to be effective (some with extra parameters);”
From Token Merging: Your ViT But Faster (2022) · cites this paper as Meng et al., 2022
Several token pruning methods prune different numbers of tokens depending on the input content, making batched inference infeasible.
“and several prune different numbers of tokens depending on the input content, making batched inference infeasible.”
From Token Merging: Your ViT But Faster (2022) · cites this paper as Meng et al., 2022
Most token pruning methods cannot be applied to speed up training.
“most cannot be applied to speed up training;”
From Token Merging: Your ViT But Faster (2022) · cites this paper as Meng et al., 2022
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: 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
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
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · 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-citationEnd-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285 · see citing works
EfficientNet: 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
Decoupled Weight Decay Regularization
Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522 · see citing works
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00716 · Cited by 9,982 · see citing works
Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., Pang, R., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00140 · Cited by 9,708 · see citing works
Self-citationShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ma, N., Zhang, X., Zheng, H.-T., & Sun, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_8 · Cited by 7,356 · see citing works
Pyramid 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 4× · see citing works
“In practice these different head selection methods provide more flexible options to suit different computational budgets.”
BackgroundMethodInfluentialSegFormer: 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
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
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Sun, C., Shrivastava, A., Singh, S., & Gupta, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.97 · Cited by 2,484 · see citing works
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z., Tay, F. E. H., Feng, J., & Yan, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00060 · Cited by 2,415 · see citing works
Vision Transformers for Dense Prediction
Ranftl, R., Bochkovskiy, A., & Koltun, V. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01196 · Cited by 2,373 · cited 1× · see citing works
“Given an input image, AdaViT learns to adaptively derive policies on which patches , self-attention heads and transformer blocks to use or activate in the transformer backbone conditioned on the input image, encouraging us-ing less computation while maintaining the classification accuracy.”
MethodLiu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., & Hu, H. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00320 · Cited by 2,294 · see citing works
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification
Chen, C.-F. R., Fan, Q., & Panda, R. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00041 · Cited by 2,242 · see citing works
The Concrete Distribution: A Continuous Relaxation of Discrete Random VariablesPreprint
Maddison, C. J., Mnih, A., & Teh, Y. W. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.00712 · Cited by 1,771 · see citing works
Multiscale vision transformers
Fan, H., Xiong, B., Mangalam, K., Li, Y., Yan, Z., Malik, J., & Feichtenhofer, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00675 · Cited by 1,407 · 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
“While obtaining superior performance, the computational cost of vision transformers is still intensive and scales up quickly as the numbers of patches, self-attention heads and transformer blocks increase.”
BackgroundTransReID: Transformer-based Object Re-Identification
He, S., Luo, H., Wang, P., Wang, F., Li, H., & Jiang, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01474 · Cited by 1,244 · see citing works
Self-citationLeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference
Graham, B., El-Nouby, A., Touvron, H., Stock, P., Joulin, A., Jegou, H., & Douze, M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01204 · Cited by 935 · see citing works
Twins: Revisiting the Design of Spatial Attention in Vision Transformers
Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., & Shen, C. (2021) · arXiv · doi:10.48550/arxiv.2104.13840 · Cited by 817 · see citing works
End-to-End Video Instance Segmentation with Transformers
Wang, Y., Xu, Z., Wang, X., Shen, C., Cheng, B., Shen, H., & Xia, H. (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/cvpr46437.2021.00863 · Cited by 706 · see citing works
SkipNet: Learning Dynamic Routing in Convolutional Networks
Wang, X., Yu, F., Dou, Z.-Y., Darrell, T., & Gonzalez, J. E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_25 · Cited by 643 · see citing works
Multi-Scale Dense Networks for Resource Efficient Image ClassificationPreprint
Huang, G. M., Chen, D., Li, T., Wu, F., van der Maaten, L., & Weinberger, K. Q. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.09844 · Cited by 638 · see citing works
Early Convolutions Help Transformers See BetterPreprint
Xiao, T., Singh, M., Mintun, E., Darrell, T., Dollár, P., & Girshick, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.14881 · Cited by 565 · see citing works
Voxel Transformer for 3D Object Detection
Mao, J., Xue, Y., Niu, M., Bai, H., Feng, J., Liang, X., Xu, H., & Xu, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00315 · Cited by 520 · see citing works
3D Object Detection with Pointformer
Pan, X., Xia, Z., Song, S., Li, L. E., & Huang, G. M. (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/cvpr46437.2021.00738 · Cited by 493 · see citing works
BlockDrop: Dynamic Inference Paths in Residual Networks
Wu, Z., Nagarajan, T., Kumar, A., Rennie, S., Davis, L. S., Grauman, K., & Feris, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00919 · Cited by 465 · see citing works
Self-citationDynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Rao, Y., Zhao, W., Liu, B., Lu, J., Zhou, J., & Hsieh, C.-J. (2021) · arXiv · doi:10.48550/arxiv.2106.02034 · Cited by 448 · see citing works
Lin, J., Rao, Y., Lu, J., & Zhou, J. (2017) · neural information processing systems · Cited by 394 · see citing works
Convolutional Networks with Adaptive Inference Graphs
Veit, A., & Belongie, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_1 · Cited by 374 · see citing works
Spatially Adaptive Computation Time for Residual Networks
Figurnov, M., Collins, M. D., Zhu, Y., Li, Z., Huang, J., Vetrov, D., & Salakhutdinov, R. (2017) · 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/cvpr.2017.194 · Cited by 340 · see citing works
ObjectFormer for Image Manipulation Detection and Localization
Wang, J., Wu, Z., Chen, J., Han, X., Shrivastava, A., Lim, S.-N., & Jiang, Y. (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/cvpr52688.2022.00240 · Cited by 255 · see citing works
Self-citationResolution Adaptive Networks for Efficient Inference
Yang, L., Han, Y., Chen, X., Song, S., Dai, J., & Huang, G. M. (2020) · 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/cvpr42600.2020.00244 · Cited by 250 · see citing works
BEVT: BERT Pretraining of Video Transformers
Wang, R., Chen, D., Wu, Z., Chen, Y., Dai, X., Liu, M., Jiang, Y.-G., Zhou, L., & Yuan, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01432 · Cited by 234 · see citing works
Self-citationVidTr: Video Transformer Without Convolutions
Zhang, Y., Li, X., Liu, C., Shuai, B., Zhu, Y., Brattoli, B., Chen, H., Marsic, I., & Tighe, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01332 · Cited by 222 · 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
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
Meng, Y., Lin, C.-C., Panda, R., Sattigeri, P., Karlinsky, L., Oliva, A., Saenko, K., & Feris, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58571-6_6 · Cited by 156 · see citing works
AutoFocus: Efficient Multi-Scale Inference
Najibi, M., Singh, B., & Davis, L. S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00984 · Cited by 150 · see citing works
Improved Techniques for Training Adaptive Deep Networks
Li, H., Zhang, H., Qi, X., Yang, R., & Huang, G. M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00198 · Cited by 141 · see citing works
Self-citationTraining Vision Transformers for Image Retrieval
El-Nouby, A., Neverova, N., Laptev, I., & Jégou, H. (2021) · arXiv · doi:10.48550/arxiv.2102.05644 · Cited by 127 · see citing works
Label Independent Memory for Semi-Supervised Few-shot Video Classification
Zhu, L., & Yang, Y. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3007511 · Cited by 126 · see citing works
Symbiotic Attention for Egocentric Action Recognition With Object-Centric Alignment
Wang, X., Zhu, L., Wu, Y., & Yang, Y. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3015894 · Cited by 104 · see citing works
Learning When and Where to Zoom With Deep Reinforcement Learning
Uzkent, B., & Ermon, S. (2020) · 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/cvpr42600.2020.01236 · Cited by 69 · see citing works
Batch-Shaping for Learning Conditional Channel Gated NetworksPreprint
Bejnordi, B. E., Blankevoort, T., & Welling, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.06627 · Cited by 58 · see citing works
A Dynamic Frame Selection Framework for Fast Video Recognition
Wu, Z., Li, H., Xiong, C., Jiang, Y., & Davis, L. S. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3029425 · Cited by 57 · see citing works
Self-citationA Coarse-to-Fine Framework for Resource Efficient Video Recognition
Wu, Z., Li, H., Zheng, Y., Xiong, C., Jiang, Y., & Davis, L. S. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01508-1 · Cited by 43 · see citing works
Self-citation2D or not 2D? Adaptive 3D Convolution Selection for Efficient Video Recognition
Li, H., Wu, Z., Shrivastava, A., & Davis, L. S. (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/cvpr46437.2021.00609 · Cited by 31 · see citing works
Self-citationAdaptive Neural Networks for Fast Test-Time Prediction.Preprint
Bolukbasi, T., Wang, J., Dekel, O., & Saligrama, V. (2017) · arXiv (Cornell University) · Cited by 29 · see citing works
Flagged first, then by citations
4 of the 59 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
ObjectFormer for Image Manipulation Detection and Localization
Wang, J., Wu, Z., Chen, J., Han, X., Shrivastava, A., Lim, S.-N., & Jiang, Y. (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/cvpr52688.2022.00240 · Cited by 255 · see citing works
Self-citationBEVT: BERT Pretraining of Video Transformers
Wang, R., Chen, D., Wu, Z., Chen, Y., Dai, X., Liu, M., Jiang, Y.-G., Zhou, L., & Yuan, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01432 · Cited by 234 · see citing works
Self-citationScale-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
Self-citationA Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends
Younesi, A., Ansari, M., Fazli, M., Ejlali, A., Shafique, M., & Henkel, J. (2024) · IEEE Access · doi:10.1109/access.2024.3376441 · Cited by 228 · see citing works
Prototypical Residual Networks for Anomaly Detection and Localization
Zhang, H., Wu, Z., Wang, Z., Chen, Z., & Jiang, Y. (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/cvpr52729.2023.01562 · Cited by 126 · see citing works
Self-citationSelective Structured State-Spaces for Long-Form Video Understanding
Wang, J., Zhu, W., Wang, P., Yu, X., Liu, L. L., Omar, M., & Hamid, R. (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/cvpr52729.2023.00618 · Cited by 121 · see citing works
Token Merging for Fast Stable Diffusion
Bolya, D., & Hoffman, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00484 · Cited by 115 · see citing works
ProPainter: Improving Propagation and Transformer for Video Inpainting
Zhou, S., Li, C., Chan, K. C. K., & Change, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00961 · Cited by 109 · see citing works
PPT: Token-Pruned Pose Transformer for Monocular and Multi-view Human Pose Estimation
Ma, H., Wang, Z., Chen, Y., Kong, D., Chen, L., Liu, X., Yan, X., Tang, H., & Xie, X. S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20065-6_25 · Cited by 84 · see citing works
Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers
Wei, S., Ye, T., Zhang, S., Tang, Y., & Liang, J. (2023) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52729.2023.00208 · Cited by 152 · see citing works
Token Fusion: Bridging the Gap between Token Pruning and Token Merging
Kim, M., Gao, S., Hsu, Y.-C., Shen, Y., & Jin, H. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00141 · Cited by 76 · see citing works
CFAT: Unleashing Triangular Windows for Image Super-resolution
Ray, A., Kumar, G., & Kolekar, M. 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/cvpr52733.2024.02468 · Cited by 72 · see citing works
Katkuri, A. V. R., Madan, H., Khatri, N., Abdul-Qawy, A. S. H., & Patnaik, K. S. (2024) · Array · doi:10.1016/j.array.2024.100361 · Cited by 66 · see citing works
Dynamic Spatial Sparsification for Efficient Vision Transformers and Convolutional Neural Networks
Rao, Y., Liu, Z.-Y., Zhao, W.-L., Zhou, J., & Lu, J.-W. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3263826 · Cited by 60 · see citing works
Token Merging: Your ViT But Faster
Bolya, D., Fu, C.-Y., Dai, X., Zhang, P., Feichtenhofer, C., & Hoffman, J. (2022) · arXiv · doi:10.48550/arxiv.2210.09461 · Cited by 140 · see citing works
“A promising subfield of ViTs have recently emerged where, due to the input-agnostic nature of transformers, tokens can be pruned at runtime to enable a faster model (Rao et al., 2021; Yin et al., 2022; Meng et al., 2022; Liang et al., 2022; Kong et al., 2022).”
BackgroundInfluentialA Historical Survey of Advances in Transformer Architectures
Sajun, A. R., Zualkernan, I. A., & Sankalpa, D. (2024) · Applied Sciences · doi:10.3390/app14104316 · Cited by 70 · see citing works
ViTs as backbones: Leveraging vision transformers for feature extraction
Elharrouss, O., Himeur, Y., Mahmood, Y., Alrabaee, S., Ouamane, A., Bensaali, F., Bechqito, Y., & Chouchane, A. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.102951 · Cited by 59 · see citing works
Exploring Cognitive and Aesthetic Causality for Multimodal Aspect-Based Sentiment Analysis
Xiao, L., Mao, R., Zhao, S., Lin, Q., Jia, Y., He, L., & Cambria, E. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3565506 · Cited by 57 · see citing works
Yao, S., Sun, H., Xiang, T.-Z., Wang, X., & Cao, X. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3475219 · Cited by 54 · see citing works
Which Tokens to Use? Investigating Token Reduction in Vision Transformers
Haurum, J. B., Escalera, S., Taylor, G. W., & Moeslund, T. B. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00085 · Cited by 47 · see citing works
TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer
Dou, Z., Wu, Q., Lin, C. W., Cao, Z., Wu, Q., Wan, W., Komura, T., & Wang, W. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01390 · Cited by 47 · see citing works
Long, S., Zhao, Z., Pi, J., Wang, S., & Wang, J.-D. (2022) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52729.2023.00996 · Cited by 79 · see citing works
Linguistic-Aware Patch Slimming Framework for Fine-Grained Cross-Modal Alignment
Fu, Z., Zhang, L., Hou, X., & Mao, 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/cvpr52733.2024.02485 · Cited by 46 · see citing works
Resource-efficient Algorithms and Systems of Foundation Models: A Survey
Xu, M., Cai, D., Yin, W., Wang, S., Jin, X., & Liu, X. (2024) · ACM Computing Surveys · doi:10.1145/3706418 · Cited by 152 · see citing works
Making Vision Transformers Efficient from A Token Sparsification View
Chang, S., Wang, P., Lin, M., Wang, F., Zhang, D. J., Jin, R., & Shou, M. Z. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00600 · Cited by 43 · see citing works
MHST: Multiscale Head Selection Transformer for Hyperspectral and LiDAR Classification
Ni, K., Wang, D., Zheng, Z., & Wang, P. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3366614 · Cited by 43 · see citing works
Occluded Person Re-identification via Saliency-Guided Patch Transfer
Tan, L., Xia, J., Liu, W., Dai, P., Wu, Y., & Cao, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28312 · Cited by 43 · see citing works
Hyperspectral and LiDAR Classification via Frequency Domain-Based Network
Ni, K., Wang, D., Zhao, G., Zheng, Z., & Wang, P. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3435793 · Cited by 40 · see citing works
RIFormer: Keep Your Vision Backbone Effective But Removing Token Mixer
Wang, J., Zhang, S., Liu, Y., Wu, T., Yang, Y., Liu, X., Chen, K., Luo, P., & Lin, D. (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/cvpr52729.2023.01388 · Cited by 38 · see citing works
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision Transformers
Lu, C., de Geus, D., & Dubbelman, G. (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/cvpr52729.2023.02263 · Cited by 36 · see citing works
STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition
Li, M., Xu, X., Fan, H., Zhou, P., Liu, J., Liu, J., Li, J., Keppo, J., Shou, M. Z., & Yan, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00471 · Cited by 36 · see citing works
ResFormer: Scaling ViTs with Multi-Resolution Training
Tian, R., Wu, Z., Dai, Q., Hu, H., Qiao, Y., & Jiang, Y. (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/cvpr52729.2023.02176 · Cited by 34 · see citing works
Self-citationRevisiting Token Pruning for Object Detection and Instance Segmentation
Liu, Y., Gehrig, M., Messikommer, N., Cannici, M., & Scaramuzza, D. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00264 · Cited by 40 · see citing works
WLSD-YOLO: A Model for Detecting Surface Defects in Wood Lumber
Zhang, Q., Liu, L., Yang, Z., Jingtao, Y., & Jing, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3395623 · Cited by 33 · see citing works
vid-TLDR: Training Free Token merging for Light-Weight Video Transformer
Choi, J., Lee, S., Chu, J., Choi, M., & Kim, H. J. (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/cvpr52733.2024.01776 · Cited by 35 · see citing works
Xiong, B., Hong, R., Liu, R., Wang, J., Zhang, J., Li, W., Lv, S., & Ge, D. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109190 · Cited by 30 · see citing works
Towards Language-Driven Video Inpainting via Multimodal Large Language Models
Wu, J., Li, X., Si, C., Zhou, S., Yang, J., Zhang, J., Li, Y., Chen, K., Tong, Y., Liu, Z., 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/cvpr52733.2024.01188 · Cited by 30 · see citing works
Latency-Aware Unified Dynamic Networks for Efficient Image Recognition
Han, Y., Liu, Z., Yuan, Z., Pu, Y., Wang, C., Song, S., & Huang, G. M. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3393530 · Cited by 27 · see citing works
Zhang, Z., Zhao, L., Wang, J., & Wang, C. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3384238 · Cited by 25 · see citing works
Chen, Y., Wen, C., Liu, W., & He, W. P. (2023) · Scientific Reports · doi:10.1038/s41598-023-46693-w · Cited by 26 · see citing works
TFormer: A Transmission-Friendly ViT Model for IoT Devices
Lu, Z., Ding, C., Juefei-Xu, F., Boddeti, V. N., Wang, S., & Yang, Y. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3222765 · Cited by 24 · see citing works
Vision Transformers with Mixed-Resolution Tokenization
Ronen, T., Levy, O., & Golbert, A. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00486 · Cited by 25 · see citing works
Ruiping, Y., Liu, K., Xu, S., Jian, Y., & Zhang, Z. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01359-6 · Cited by 23 · see citing works
Cao, J., Ye, P., Li, S., Yu, C., Tang, Y., Lu, J., & Chen, T. (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/cvpr52733.2024.01487 · Cited by 29 · see citing works
Vision Transformers are Good Mask Auto-Labelers
Lan, S., Yang, X., Yu, Z., Wu, Z., Álvarez, J. M., & Anandkumar, A. (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/cvpr52729.2023.02274 · Cited by 23 · see citing works
Self-citationNorouzi, N., Orlova, S., de Geus, D., & Dubbelman, 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/cvpr52733.2024.01493 · Cited by 22 · see citing works
MaskGaussian: Adaptive 3D Gaussian Representation from Probabilistic Masks
Liu, Y., Zhong, Z., Zhan, Y., Xu, S., & Sun, 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/cvpr52734.2025.00072 · Cited by 21 · see citing works
Cross-Modality Interaction Network for Pan-Sharpening
Wang, Y., He, X., Dong, Y., Lin, Y., Huang, Y., & Ding, X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3412683 · Cited by 21 · see citing works
Metal Surface Defect Detection Based on a Transformer with Multi-Scale Mask Feature Fusion
Zhao, L., Zheng, Y., Peng, T., & Zheng, E. (2023) · Sensors · doi:10.3390/s23239381 · Cited by 21 · see citing works
How can objects help action recognition?
Zhou, X., Arnab, A., Sun, C., & Schmid, C. (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/cvpr52729.2023.00233 · Cited by 20 · see citing works
SViTT: Temporal Learning of Sparse Video-Text Transformers
Li, Y., Min, K., Tripathi, S., & Vasconcelos, N. M. (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/cvpr52729.2023.01814 · Cited by 20 · see citing works
Dynamic Token-Pass Transformers for Semantic Segmentation
Liu, Y., Zhou, Q., Wang, J., Wang, Z., Wang, F., Wang, J., & Zhang, W. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00184 · Cited by 20 · see citing works
Automatic Estuarine Fish Species Classification System Based on Deep Learning Techniques
Tejaswini, H., Pai, M. M. M., & Pai, R. M. (2024) · IEEE Access · doi:10.1109/access.2024.3468438 · Cited by 20 · see citing works
LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition
Hu, Y., Cheng, Y., Lu, A., Cao, Z., Wei, D., Liu, J., & Li, Z. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.28001 · Cited by 19 · see citing works
TCFormer: Visual Recognition via Token Clustering Transformer
Zeng, W., Jin, S., Xu, L., Liu, W.-T., Qian, C., Ouyang, W.-L., Luo, P., & Wang, X. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3425768 · Cited by 28 · see citing works
Ullah, N., Mahmood, T., Kim, S. G., Nam, S. H., Sultan, H., & Park, K. R. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106451 · Cited by 19 · see citing works
Data-efficient multi-scale fusion vision transformer
Tang, H., Liu, D., & Shen, C. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111305 · Cited by 18 · see citing works
Prune Spatio-temporal Tokens by Semantic-aware Temporal Accumulation
Ding, S., Zhao, P., Zhang, X., Qian, R., Xiong, H., & Tian, Q. C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01554 · Cited by 18 · see citing works
GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation
Xu, X., Wang, S., Chen, Y., Zheng, Y., Wei, Z., & Liu, J. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00016 · Cited by 18 · see citing works
SMB-YOLOv5: A Lightweight Airport Flying Bird Detection Algorithm Based on Deep Neural Networks
Liang, H., Zhang, X., Kong, J., Zhao, Z., & Ma, K. (2024) · IEEE Access · doi:10.1109/access.2024.3415385 · Cited by 18 · see citing works
Multi-Criteria Token Fusion with One-Step-Ahead Attention for Efficient Vision Transformers
Lee, S., Choi, J., & Kim, H. J. (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/cvpr52733.2024.01490 · Cited by 19 · see citing works
MSViT: Dynamic Mixed-scale Tokenization for Vision Transformers
Havtorn, J. D., Royer, A., Blankevoort, T., & Bejnordi, B. E. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00091 · Cited by 17 · see citing works
Yang, B., Liu, F., Zou, Y., Wu, X., Wang, Y., & Clifton, D. A. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3371376 · Cited by 20 · see citing works
Bourriez, N., Bendidi, I., Cohen, E., Watkinson, G., Sanchez, M., Bollot, G., & Genovesio, A. (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/cvpr52733.2024.01098 · Cited by 16 · see citing works
An Efficient Window-Based Vision Transformer Accelerator via Mixed-Granularity Sparsity
Dong, Q., Zhang, S., & Wang, Z. (2025) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2025.3527541 · Cited by 16 · see citing works
Niu, Y., Song, Z., Luo, Q., Chen, G., Ma, M., & Li, F. (2025) · Remote Sensing · doi:10.3390/rs17040660 · Cited by 16 · see citing works
K-centered Patch Sampling for Efficient Video Recognition
Park, S. H., Tack, J., Heo, B., Ha, J.-W., & Shin, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19833-5_10 · Cited by 16 · see citing works
Alzamzami, F., & Saddik, A. E. (2023) · IEEE Access · doi:10.1109/access.2023.3274744 · Cited by 17 · see citing works
AutoFocusFormer: Image Segmentation off the Grid
Chen, Z., Patnaik, K., Zhai, S., Wan, A., Ren, Z., Schwing, A. G., Colburn, A., & Li, F. (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/cvpr52729.2023.01748 · Cited by 16 · see citing works
Eventful Transformers: Leveraging Temporal Redundancy in Vision Transformers
Dutson, M., Li, Y., & Gupta, M. (2023) · IEEE International Conference on Computer Vision · doi:10.1109/iccv51070.2023.01551 · Cited by 28 · see citing works
Adapting Neural Networks at Runtime: Current Trends in At-Runtime Optimizations for Deep Learning
Sponner, M., Waschneck, B., & Kumar, A. S. (2024) · ACM Computing Surveys · doi:10.1145/3657283 · Cited by 15 · see citing works
Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language Embedding
Meng, L., Dai, X., Chen, Y., Zhang, P., Chen, D., Liu, M., Wang, J., Wu, Z., Yuan, L., & Jiang, Y. (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/cvpr52729.2023.01097 · Cited by 14 · see citing works
Self-citationTwo-Phase Flow Pattern Identification in Vertical Pipes Using Transformer Neural Networks
Ruiz-Díaz, C. M., Perilla-Plata, E. E., & González‐Estrada, O. A. (2024) · Inventions · doi:10.3390/inventions9010015 · Cited by 14 · see citing works
Efficient Vision Transformers with Partial Attention
Vo, X.-T., Nguyen, D.-L., Priadana, A., & Jo, K.-H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73010-8_18 · Cited by 14 · see citing works
AdaFormer: Efficient Transformer with Adaptive Token Sparsification for Image Super-resolution
Luo, X., Ai, Z., Liang, Q., Liu, D., Xie, Y., Qu, Y., & Fu, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28194 · Cited by 13 · see citing works
Dual-path multi-branch feature residual network for salient object detection
Chen, Z., Lu, Y., Long, S., & Bai, J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108530 · Cited by 13 · see citing works
Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic Segmentation
Kienzle, D., Kantonis, M., Schön, R., & Lienhart, R. W. (2024) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr62202.2024.00019 · Cited by 13 · see citing works
Dong, J., Jiang, J., Jiang, K., Li, J., & Zhang, 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/cvpr52734.2025.02870 · Cited by 13 · see citing works
Conditional computation in neural networks: Principles and research trends
Scardapane, S., Baiocchi, A., Devoto, A., Marsocci, V., Minervini, P., & Pomponi, J. (2024) · Intelligenza Artificiale · doi:10.3233/ia-240035 · Cited by 12 · see citing works
Shen, G., Ouyang, Y., Lu, J., Yang, Y., & Sánchez, V. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3512369 · Cited by 12 · see citing works
Scale-Aware Pruning Framework for Remote Sensing Object Detection via Multifeature Representation
Hu, Z., Gong, M., Zhao, Y., Zhang, M., Lu, Y., Li, J., Pu, Y., & Wang, Z. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3545416 · Cited by 12 · see citing works
A fusion‐attention swin transformer for cardiac MRI image segmentation
Yang, R., Liu, K., & Liang, Y. (2023) · IET Image Processing · doi:10.1049/ipr2.12936 · Cited by 11 · see citing works
Efficient Transformer-based 3D Object Detection with Dynamic Token Halting
Ye, M., Meyer, G. P., Chai, Y., & Liu, Q. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00775 · Cited by 11 · see citing works
Model and Method for Providing Resilience to Resource-Constrained AI-System
Москаленко, В. В., Kharchenko, V. S., & Semenov, S. (2024) · Sensors · doi:10.3390/s24185951 · Cited by 11 · see citing works
SAT-HMR: Real-Time Multi-Person 3D Mesh Estimation via Scale-Adaptive Tokens
Su, C., Ma, X., Su, J., & Wang, 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/cvpr52734.2025.01565 · Cited by 11 · see citing works
Survey on Efficient Large Language Models: Principles, Algorithms, Applications, and Open Issues
Cheng, J., Kang, H., Shao, Y., Li, N., Chen, P., Wang, R., Long, S., Yang, X., & Ma, L. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3628671 · Cited by 11 · see citing works
Multi-Class Urinary Sediment Particles Detection Based on YOLOv7 With Attention Modules
Komori, T., Nishikawa, H., Sasaki, K., Taniguchi, I., & Onoye, T. (2024) · IEEE Access · doi:10.1109/access.2024.3448262 · Cited by 10 · see citing works
MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning
Farina, M., Mancini, M., Cunegatti, E., Cunegatti, E., Iacca, G., & Ricci, E. (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/cvpr52733.2024.01532 · Cited by 10 · see citing works
Huang, Y., Li, Q., Hu, C., Zhou, X., Han, Q., Lu, H., Cui, N., & Cao, Q. (2025) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2025.101672 · Cited by 10 · see citing works
Yin, M., Uzkent, B., Shen, Y., Jin, H., & Yuan, B. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26298 · Cited by 10 · see citing works
Deep Neural Networks in Video Human Action Recognition: A reviewPreprint
Wang, Z., Zheng, Y., Yang, Y., & Li, Y. (2023) · doi:10.36227/techrxiv.22146914 · Cited by 10 · see citing works
Expediting Large-Scale Vision Transformer for Dense Prediction Without Fine-Tuning
Yuan, Y., Liang, W., Ding, H., Liang, Z., Zhang, C., & Hu, H. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3327511 · Cited by 14 · see citing works
Rolling bearing fault diagnosis method based on PE-DCM and ViT
Hui, Y., Xu, K., Chen, P., & Zhao, X. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad5eab · Cited by 9 · see citing works
Agglomerative Token Clustering
Haurum, J. B., Escalera, S., Taylor, G. W., & Moeslund, T. B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72998-0_12 · Cited by 9 · see citing works
Adaptive patch selection to improve Vision Transformers through Reinforcement Learning
Cauteruccio, F., Marchetti, M., Traini, D., Domenico Ursino, A., & Virgili, L. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06516-z · Cited by 9 · see citing works
Su, S., Liu, J., Gao, L., & Song, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28300 · Cited by 9 · see citing works
AdapTiV: Sign-Similarity Based Image-Adaptive Token Merging for Vision Transformer Acceleration
Yoo, S.-J., Kim, H., & Kim, J. (2024) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro61859.2024.00015 · Cited by 9 · see citing works
Martinez, F., Romaine, J. B., Manzano, J. M., Ierardi, C., & Millán, P. (2024) · IEEE Access · doi:10.1109/access.2024.3453993 · Cited by 8 · see citing works
Attention Map Is All We Need for Lightweight Fingerprint Liveness Detection
Yook, H. J., Hong, P. M., Kang, S. H., Jhun, G. S., Seo, J. E., & Lee, Y. K. (2024) · IEEE Access · doi:10.1109/access.2024.3458908 · Cited by 8 · see citing works
Zhu, L., Chen, T., Ji, D., Ye, J., & Liu, J. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3531299 · Cited by 8 · see citing works
- Authors
- Meng, L., Li, H., Chen, B.-C., Lan, S., Wu, Z., Jiang, Y.-G., & Lim, S.-N.
- Affiliations
- Fudan University (CN)
- University of Maryland, College Park (US)
- Meta (United States) (US)
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 12299-12308
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4226174225
- Cited by
- 262
- References
- 59
- Citations by year
- 2026: 35 · 2025: 68 · 2024: 106 · 2023: 47 · 2022: 6
- FWCI
- 20.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Funders
- National Key R&D Program of China (2021ZDOI12805)
- 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
- vision transformer, adaptive computation, self-attention, inference efficiency, image recognition, influence diagrams, long-range dependencies, accuracy-efficiency trade-off
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
