Quantformer: Learning Extremely Low-Precision Vision Transformers
Abstract
In this article, we propose extremely low-precision vision transformers called Quantformer for efficient inference. Conventional network quantization methods directly quantize weights and activations of fully-connected layers without considering properties of transformer architectures. Quantization sizably deviates the self-attention compared with full-precision counterparts, and the shared quantization strategy for diversely distributed patch features causes severe quantization errors. To address these issues, we enforce the self-attention rank in quantized transformers to mimic that in full-precision counterparts with capacity-aware distribution for information retention, and quantize patch features with group-wise discretization strategy for quantization error minimization. Specifically, we efficiently preserve the self-attention rank consistency by minimizing the distance between the self-attention in quantized and real-valued transformers with adaptive concentration degree, where the optimal concentration degree is selected according to the self-attention entropy for model capacity adaptation. Moreover, we partition patch features in different dimensions with differentiable group assignment, so that features in different groups leverage various discretization strategies with minimal rounding and clipping errors. Experimental results show that our Quantformer outperforms the state-of-the-art network quantization methods by a sizable margin across various vision transformer architectures in image classification and object detection. We also integrate our Quantformer with mixed-precision quantization to further enhance the performance of the vanilla models.
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?
Quantformer leverages entropy information as a capacity-aware metric to maintain consistency of self-attention ranks by minimizing discrepancy between quantized and full-precision self-attention layers.
“Quantformer [60] leverages entropy information as a capacityaware metric to maintain the consistency of self-attention ranks by minimizing the discrepancy between quantized and full-precision self-attention layers.”
From Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey (2024) · cites this paper as [60]
Cites
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
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · 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
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
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
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
End-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
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · see citing works
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_32 · Cited by 4,408 · see citing works
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H. S., et al. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00681 · Cited by 4,008 · cited 1× · see citing works
“Inspired by the great success, vision transformers were widely studied to achieve promising performance in image classification [15], [35], [46], object detection [4], [12], semantic segmentation [38], [63] and many others.”
BackgroundSelf-citationDARTS: Differentiable architecture search
Liu, H., Simonyan, K., & Yang, Y. (2018) · arXiv · doi:10.48550/arxiv.1806.09055 · Cited by 3,248 · see citing works
Channel Pruning for Accelerating Very Deep Neural Networks
He, Y., Zhang, X., & Sun, J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.155 · Cited by 2,647 · 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
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., & He, K. (2017) · arXiv · doi:10.48550/arxiv.1706.02677 · 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
Binarized Neural NetworksPreprint
Hubara, I., Soudry, D., & El-Yaniv, R. (2016) · arXiv.org · doi:10.48550/arxiv.1602.02505 · Cited by 2,241 · 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
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
HAQ: Hardware-Aware Automated Quantization With Mixed Precision
Wang, K., Liu, Z., Lin, Y., Lin, J., & Han, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00881 · Cited by 978 · 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
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
Zhang, D., Yang, J., Ye, D., & Hua, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_23 · Cited by 714 · see citing works
PACT: Parameterized Clipping Activation for Quantized Neural Networks
Choi, J., Wang, Z., Venkataramani, S., Chuang, P. I.-J., Srinivasan, V., & Gopalakrishnan, K. (2018) · arXiv · doi:10.48550/arxiv.1805.06085 · Cited by 703 · see citing works
Self-citationLiu, Z., Wu, B., Luo, W., Yang, X. Y., Liu, W., & Cheng, K.-T. T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01267-0_44 · Cited by 568 · see citing works
UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
Dai, Z., Cai, B., Lin, Y., & Chen, 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/cvpr46437.2021.00165 · Cited by 547 · see citing works
Data-Free Quantization Through Weight Equalization and Bias Correction
Nagel, M., Baalen, M. V., Blankevoort, T., & Welling, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00141 · Cited by 500 · see citing works
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks
Gong, R., Liu, X., Jiang, S., Li, T., Hu, P., Lin, J., Yu, F., & Yan, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00495 · Cited by 485 · see citing works
DynamicViT: 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
Self-citationZafrir, O., Boudoukh, G., Izsak, P., & Wasserblat, M. (2019) · 2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS Edition (EMC2-NIPS) · doi:10.1109/emc2-nips53020.2019.00016 · Cited by 442 · see citing works
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
Shen, S., Dong, Z., Ye, J., Ma, L., Yao, Z., Gholami, A., Mahoney, M. W., & Keutzer, K. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6409 · Cited by 437 · see citing works
Learned Step Size Quantization
Esser, S. K., McKinstry, J. L., Bablani, D., Appuswamy, R., & Modha, D. S. (2019) · arXiv · doi:10.48550/arxiv.1902.08153 · Cited by 415 · see citing works
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search
Chu, X., Zhou, T., Zhang, B., & Li, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_28 · Cited by 338 · see citing works
DARTS+: Improved Differentiable Architecture Search with Early StoppingPreprint
Liang, H., Zhang, S., Sun, J., He, X., Huang, W., Zhuang, K., & Li, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.06035 · Cited by 303 · see citing works
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Zhao, R., Hu, Y., Dotzel, J., De Sa, C., & Zhang, Z. (2019) · arXiv · doi:10.48550/arxiv.1901.09504 · Cited by 231 · see citing works
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer
Lu, Z., He, S., Zhu, X., Zhang, L., Song, Y.-Z., & Xiang, T. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00862 · Cited by 219 · see citing works
Fully Quantized Network for Object Detection
Li, R., Wang, Y., Liang, F., Qin, H., Yan, J., & Fan, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00292 · Cited by 214 · see citing works
Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer
Xu, Y., Zhang, Z., Zhang, M., Sheng, K., Li, K., Dong, W., Zhang, L., Xu, C., & Sun, X. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20202 · Cited by 202 · see citing works
I-BERT: Integer-only BERT Quantization
Kim, S., Gholami, A., Yao, Z., Mahoney, M. W., & Keutzer, K. (2021) · arXiv · doi:10.48550/arxiv.2101.01321 · Cited by 186 · 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
Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer
Idelbayev, Y., & Carreira-Perpiñán, M. Á. (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/cvpr42600.2020.00807 · Cited by 132 · see citing works
Rethinking Differentiable Search for Mixed-Precision Neural Networks
Cai, Z., & Vasconcelos, N. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00242 · Cited by 119 · see citing works
Efficient Neural Network Compression
Kim, H., Khan, M. U. K., & Kyung, C. (2019) · 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.2019.01285 · Cited by 115 · see citing works
Manifold Regularized Dynamic Network Pruning
Tang, Y.-H., Wang, Y.-H., Xu, Y., Deng, Y., Xu, C., Tao, D., & Xu, C. (2021) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr46437.2021.00498 · Cited by 115 · see citing works
Network Quantization with Element-wise Gradient Scaling
Lee, J., Kim, D., & Ham, B. (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/cvpr46437.2021.00638 · Cited by 114 · see citing works
GLiT: Neural Architecture Search for Global and Local Image Transformer
Chen, B., Li, P., Li, C., Li, B., Bai, L., Chen, L., Sun, M., Yan, J., & Ouyang, W. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00008 · Cited by 100 · see citing works
Differentiable Joint Pruning and Quantization for Hardware Efficiency
Wang, Y., Lu, Y., & Blankevoort, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58526-6_16 · Cited by 76 · see citing works
Extreme Network Compression via Filter Group Approximation
Peng, B., Tan, W., Li, Z., Zhang, S., Xie, D., & Pu, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_19 · Cited by 68 · see citing works
Shapley-NAS: Discovering Operation Contribution for Neural Architecture Search
Xiao, H., Wang, Z., Zhu, Z., Zhou, J., & Lu, J. (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/cvpr52688.2022.01159 · Cited by 67 · see citing works
Self-citationDifferentiable Feature Aggregation Search for Knowledge Distillation
Guan, Y., Zhao, P., Wang, B., Zhang, Y., Yao, C., Bian, K., & Tang, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58520-4_28 · Cited by 42 · see citing works
Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision
Liu, X., Ye, M., Zhou, D., & Liu, Q. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17054 · Cited by 40 · see citing works
GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization
Guo, Y., Yuan, H., Tan, J., Wang, Z., Yang, S., & Liu, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00519 · Cited by 40 · see citing works
Self-citationLearning Efficient Binarized Object Detectors With Information Compression
Wang, Z., Lu, J., Wu, Z., & Zhou, J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3050464 · Cited by 38 · see citing works
Self-citationLearning Channel-Wise Interactions for Binary Convolutional Neural Networks
Wang, Z., Lu, J., & Zhou, J. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2988262 · Cited by 37 · see citing works
Self-citationSearching the Search Space of Vision TransformerPreprint
Chen, M., Wu, K., Ni, B., Peng, H., Liu, B., Fu, J., Chao, H., & Ling, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.14725 · Cited by 32 · see citing works
PSViT: Better Vision Transformer via Token Pooling and Attention SharingPreprint
Chen, B., Li, P., Li, B., Li, C., Bai, L., Chen, L., Sun, M., Yan, J., & Ouyang, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.03428 · Cited by 30 · see citing works
Generalizable Mixed-Precision Quantization via Attribution Rank Preservation
Wang, Z., Xiao, H., Lu, J., & Zhou, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00524 · Cited by 27 · see citing works
Self-citationLearning Deep Binary Descriptors via Bitwise Interaction Mining
Wang, Z., Han, X. G., Duan, Y., Zhou, J., & Lu, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3161600 · Cited by 23 · see citing works
Self-citationFixed-point Quantization for Vision Transformer
Li, Z., Wang, P., Wang, Z., & Cheng, J. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9728246 · Cited by 6 · see citing works
Self-citationTerViT: An Efficient Ternary Vision Transformer
Xu, S., Li, Y., Ma, T., Zeng, B., Zhang, B., Gao, P., & Lv, J. (2022) · arXiv · doi:10.48550/arxiv.2201.08050 · Cited by 5 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
6 of the 68 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Vision Transformers for Image Classification: A Comparative Survey
Wang, Y., Deng, Y., Zheng, Y.-J., Chattopadhyay, P., & Wang, L.-P. (2025) · Technologies · doi:10.3390/technologies13010032 · Cited by 202 · see citing works
Efficient multimodal large language models: a survey
Jin, Y., Li, J., Gu, T., Liu, Y., Zhao, B., Lai, J., Gan, Z., Wang, Y., Wang, C., Tan, X., et al. (2025) · Visual Intelligence · doi:10.1007/s44267-025-00099-6 · Cited by 108 · see citing works
Self-citationSaha, S., & Xu, L. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130417 · Cited by 66 · see citing works
Instance-Aware Group Quantization for Vision Transformers
Moon, J., Kim, D., Cheon, J., & Ham, B. (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/cvpr52733.2024.01527 · Cited by 24 · see citing works
Fawole, O. A., & Rawat, D. B. (2025) · ACM Computing Surveys · doi:10.1145/3729167 · Cited by 18 · see citing works
GradQ-ViT: Robust and Efficient Gradient Quantization for Vision Transformers
Choi, D., & Kim, H. K. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i15.33759 · Cited by 18 · see citing works
Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis
Xu, X., Wang, Z., Zhou, J., & Lu, 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/cvpr52729.2023.00514 · Cited by 13 · see citing works
Self-citationMarkov-PQ: Joint Pruning-Quantization via Learnable Markov Chain
Li, Y., Zhang, X., Xie, W., Zhang, J., Fang, L., & Du, J. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3437182 · Cited by 8 · see citing works
Ding, R., Liang, Y. Q., Zhao, S., Nie, J., Chen, L., Liu, H., & Zhou, X. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107558 · Cited by 6 · see citing works
Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey
Du, D., Gong, G., & Chu, X. (2024) · arXiv · doi:10.48550/arxiv.2405.00314 · Cited by 4 · see citing works
“Furthermore, [60] introduces a differentiable search mechanism aimed at optimally grouping patch feature dimensions.”
BackgroundKatare, D., Leroux, S., Janssen, M., & Ding, A. Y. (2025) · Computing · doi:10.1007/s00607-025-01427-w · Cited by 4 · see citing works
Learning Accurate Performance Predictors for Ultrafast Automated Model Compression
Wang, Z., Lu, J., Xiao, H., Liu, S., & Zhou, J. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01783-0 · Cited by 3 · see citing works
Self-citationTA-ASF: Attention-Sensitive Token Sampling and Fusing for Visual Transformer Models on the Edge
Chen, J., Zhang, X., Zhou, W., & Shi, W. (2024) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec62691.2024.00018 · Cited by 1 · see citing works
Interactive shape estimation for densely cluttered objects
Ran, J., & Yan, H. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.02.026 · Cited by 1 · see citing works
Excision of Prostatic Utricle: When? How?
Karakurt, G., Keskin, H., Doğan, H. S., & Tekgül, S. (2025) · Journal of Urological Surgery · doi:10.4274/jus.galenos.2025.2024-11-7 · Cited by 1 · see citing works
Ding, R., Zhao, S., Shi, J., Nie, J., Chen, L., Liu, H., & Zhou, X. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116114 · Cited by 1 · see citing works
Learning Extensible Series-Parallel Lookup Tables for Efficient Image Super-Resolution
Zhang, J., Wang, Z., Ma, C., Zhou, J., & Lu, J. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02516-1
Self-citationORPTQ: An Improved Large Model Quantization Method Based on Optimal Quantization Range
Tian, S., & Huang, K. (2025) · Information Technology And Control · doi:10.5755/j01.itc.54.2.40573
Zhao, Z., Li, H., Liu, F., Lu, Y., Wang, Z., Yang, T., Jiang, L., & Guan, H. (2025) · doi:10.1109/iccad66269.2025.11240736
Self-citationLB-PTQ: Effective Low-Bit Post-Training Quantization for Vision Transformers
Wang, Z., Xu, K., Geng, X., Lin, J., Aly, M. M. S., Wu, M., Li, X., & Lin, W. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3685111
Self-citation陶晓焦, Feng, Q., Li, Y., Hao, W., & Zhang, Y. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00828-x
Tanveer, H., Ahmadilivani, M. H., Jenihhin, M., & Daneshtalab, M. (2026) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2026.104001
CASQ: Configuration-Aware Sensitivity for Mixed-Precision Quantization of Vision Transformers
Kričković, L., & Kakani, V. (2026) · Electronics · doi:10.3390/electronics15194573
- Authors
- Wang, Z., Wang, C., Xu, X., Zhou, J., & Lu, J.
- Affiliations
- Beijing National Research Center for Information Science and Technology (CN)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2023-07-01
- Volume
- 45
- Issue
- 7
- Pages
- 8813-8826
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W4312453883
- Cited by
- 22
- References
- 68
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 3 · 2023: 2
- FWCI
- 1.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Key Research and Development Program of China (2017YFA0700802)
- National Natural Science Foundation of China (62125603, 61822603)
- Beijing Academy of Artificial Intelligence
- 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 › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
- Keywords
- vision transformer, neural network quantization, low-precision inference, attention entropy, mixed-precision quantization, inference efficiency, image classification, object detection
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
