视觉Transformer（ViT）在各种视觉任务中取得了显著成功，但其部署常因高昂的计算成本而受阻。尽管结构化权重剪枝和令牌压缩已成为有前景的解决方案，但它们分别存在再训练时间长和层间依赖复杂优化的问题。我们提出ToaSt，一个解耦框架，将专门策略应用于不同的ViT组件。我们对多头自注意力模块应用耦合头结构化剪枝，利用注意力操作特性增强鲁棒性。对于前馈网络（占FLOPs的60%以上），我们引入令牌通道选择（TCS），一种在推理时过滤冗余噪声通道的免训练方法。在包括DeiT、ViT-MAE和Swin Transformer在内的九个不同模型上的广泛评估表明，ToaSt在精度和效率之间实现了优越的权衡，持续优于现有基线。在ViT-MAE-Huge上，ToaSt以39.4%的FLOPs缩减达到88.52%的准确率（+1.64%）。ToaSt还能有效迁移到多种下游任务（COCO检测、ADE20K分割、CIFAR-100分类），在COCO上达到52.2 vs 51.9 mAP。代码：this http URL
英文摘要：
Vision Transformers (ViTs) have achieved remarkable success across various vision tasks, yet their deployment is often hindered by prohibitive computational costs. While structured weight pruning and token compression have emerged as promising solutions, they suffer from prolonged retraining and inter-layer dependencies that complicate optimization, respectively. We propose ToaSt, a decoupled framework applying specialized strategies to distinct ViT components. We apply coupled head-wise structured pruning to Multi-Head Self-Attention modules, leveraging attention operation characteristics to enhance robustness. For Feed-Forward Networks (over 60% of FLOPs), we introduce Token Channel Selection (TCS), a training-free method that filters redundant noise channels at inference time. Extensive evaluations across nine diverse models, including DeiT, ViT-MAE, and Swin Transformer, demonstrate that ToaSt achieves superior trade-offs between accuracy and efficiency, consistently outperforming existing baselines. On ViT-MAE-Huge, ToaSt achieves 88.52% accuracy (+1.64%p) with 39.4% FLOPs reduction. ToaSt also transfers effectively to diverse downstream tasks (COCO detection, ADE20K segmentation, CIFAR-100 classification), achieving 52.2 versus 51.9 mAP on COCO. Code: github.com/SHANNonLab-HUFS/ToaSt
Block-based Symmetric Pruning and Fusion for Efficient Vision Transformers
College of Artificial Intelligence and Green Energy National Yang Ming Chiao Tung University（人工智能与绿色能源学院，阳明交通大学）；Computer Science Department University at Albany, SUNY（计算机科学系，阿尔巴尼大学）；Computer Science Information Engineering Department University at Albany, SUNY（计算机科学与信息工程系，阿尔巴尼大学）
Cut-ViT: Task-Specific Model Pruning via Gram Anchoring Subspace Consistency
Nanjing University of Science and Technology（南京理工大学）；State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery（先进工程机械智能制造国家重点实验室）；Zhejiang University（浙江大学）；Nanjing Normal University（南京师范大学）
Vision transformer (ViT) has achieved competitive accuracy on a variety of computer vision applications, but its computational cost impedes the deployment on resource-limited mobile devices. We explore the sparsity in ViT and observe that informative patches and heads are sufficient for accurate image recognition. In this paper, we propose a cascade pruning framework named CP-ViT by predicting sparsity in ViT models progressively and dynamically to reduce computational redundancy while minimizing the accuracy loss. Specifically, we define the cumulative score to reserve the informative patches…
NVIDIA（英伟达）；University of California, Berkeley（加州大学伯克利分校）；Duke University（杜克大学）
Transformers yield state-of-the-art results across many tasks. However, their heuristically designed architecture impose huge computational costs during inference. This work aims on challenging the common design philosophy of the Vision Transformer (ViT) model with uniform dimension across all the stacked blocks in a model stage, where we redistribute the parameters both across transformer blocks and between different structures within the block via the first systematic attempt on global structural pruning. Dealing with diverse ViT structural components, we derive a novel Hessian-based structu…
The recently proposed Vision transformers (ViTs) have shown very impressive empirical performance in various computer vision tasks, and they are viewed as an important type of foundation model. However, ViTs are typically constructed with large-scale sizes, which then severely hinder their potential deployment in many practical resources-constrained applications. To mitigate this challenging problem, structured pruning is a promising solution to compress model size and enable practical efficiency. However, unlike its current popularity for CNNs and RNNs, structured pruning for ViT models is li…
TAP-ViTs: Task-Adaptive Pruning for On-Device Deployment of Vision Transformers
State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；College of Computer Science and Technology（计算机科学与技术学院）；College of Computer Science and Electronic Engineering（计算机科学与电子工程学院）
While vision transformers (ViTs) have continuously achieved new milestones in the field of computer vision, their sophisticated network architectures with high computation and memory costs have impeded their deployment on resource-limited edge devices. In this paper, we propose a hardware-efficient image-adaptive token pruning framework called HeatViT for efficient yet accurate ViT acceleration on embedded FPGAs. By analyzing the inherent computational patterns in ViTs, we first design an effective attention-based multi-head token selector, which can be progressively inserted before transforme…
DORA: Dynamic Online Reinforcement Agent for Token Pruning in Vision Transformers
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Northeastern University（东北大学）；Clemson University（克莱姆森大学）；Peking University（北京大学）；College of William and Mary（威廉与玛丽学院）；ETH Zürich（苏黎世联邦理工学院）
Recently, Vision Transformer (ViT) has continuously established new milestones in the computer vision field, while the high computation and memory cost makes its propagation in industrial production difficult. Pruning, a traditional model compression paradigm for hardware efficiency, has been widely applied in various DNN structures. Nevertheless, it stays ambiguous on how to perform exclusive pruning on the ViT structure. Considering three key points: the structural characteristics, the internal data pattern of ViTs, and the related edge device deployment, we leverage the input token sparsity…
National Key Laboratory for Novel Software Technology（新型软件技术国家重点实验室）；Nanjing University（南京大学）；Nanjing（南京）；China（中国）
Channel pruning is an important family of methods to speed up deep model's inference. Previous filter pruning algorithms regard channel pruning and model fine-tuning as two independent steps. This paper argues that combining them into a single end-to-end trainable system will lead to better results. We propose an efficient channel selection layer, namely AutoPruner, to find less important filters automatically in a joint training manner. Our AutoPruner takes previous activation responses as an input and generates a true binary index code for pruning. Hence, all the filters corresponding to zer…
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Texas A&M University（德州农工大学）；Kwai Inc.（快手公司）
Vision transformers (ViTs) have gained popularity recently. Even without customized image operators such as convolutions, ViTs can yield competitive performance when properly trained on massive data. However, the computational overhead of ViTs remains prohibitive, due to stacking multi-head self-attention modules and else. Compared to the vast literature and prevailing success in compressing convolutional neural networks, the study of Vision Transformer compression has also just emerged, and existing works focused on one or two aspects of compression. This paper proposes a unified ViT compress…
Reinforcement Learning-based Token Pruning in Vision Transformers: A Markov Game Approach
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Data Intelligence Institute of Paris（巴黎数据智能研究院）；Université Paris Cité（巴黎西岱大学）；Shijiazhuang Tiedao University（石家庄铁道大学）
Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning
Xidian University（西安电子科技大学）；University of Michigan – Shanghai Jiao Tong University Joint Institute（密歇根大学-上海交通大学联合学院）；Shanghai Jiao Tong University（上海交通大学）；Norwegian University of Science and Technology（挪威科技大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks
Institute for Infocomm Research (IR), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局（A*STAR）信息通信研究院（IR））；Nanyang Technological University, Singapore（新加坡南洋理工大学）
Recently, vision transformer (ViT) and its variants have achieved promising performances in various computer vision tasks. Yet the high computational costs and training data requirements of ViTs limit their application in resource-constrained settings. Model compression is an effective method to speed up deep learning models, but the research of compressing ViTs has been less explored. Many previous works concentrate on reducing the number of tokens. However, this line of attack breaks down the spatial structure of ViTs and is hard to be generalized into downstream tasks. In this paper, we des…
Effective pruning of web-scale datasets based on complexity of concept clusters
University of Tübingen（蒂宾根大学）；Meta AI (FAIR)（Meta AI（FAIR））；ELLIS Institute Tübingen；Max-Planck Institute for Intelligent Systems Tübingen（蒂宾根马克斯·普朗克智能系统研究所）；University of California San Diego（加州大学圣迭戈分校）；DatologyAI
Motion-Aware Adaptive Pixel Pruning for Efficient Local Motion Deblurring
Harbin Institute of Technology \& City University of Hong Kong；School of Computer Science；Technology, Harbin Institute of Technology；Tianjin University \& Low-Altitude Intelligence Lab, Xiong'an National Innovation Center Technology Co., Ltd；College of Intelligence；Computing, Tianjin University
SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observation
Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Spain（智能技术研究中心（CiTIUS），圣地亚哥-德孔波斯特拉大学，西班牙）；Departmento de Electrónica e Computación, Universidade de Santiago de Compostela, Spain（电子与计算系，圣地亚哥-德孔波斯特拉大学，西班牙）；IBM Research Europe, Zurich, Switzerland（IBM欧洲研究院，瑞士苏黎世）
Pruning By Explaining Revisited: Optimizing Attribution Methods to Prune CNNs and Transformers
Fraunhofer Heinrich-Hertz-Institute（弗劳恩霍夫海因茨·赫兹研究所）；Technische Universität Berlin（柏林工业大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所（BIFOLD））
HiAP: A Multi-Granular Stochastic Auto-Pruning Framework for Vision Transformers
School of Natural and Computing Sciences, University of Aberdeen（阿伯丁大学自然与计算科学学院）；School of Computing Sciences, University of East Anglia（东安格利亚大学计算科学学院）；Department of Physics and Technology, UiT The Arctic University of Norway（挪威北极大学物理与技术系）
