{"total":106,"rows":[{"id":"2210.09461","title":"Token Merging: Your ViT But Faster","created":"2022-10-17","cited_by_count":1021,"area":"inference"},{"id":"2108.01390","title":"Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer","created":"2021-08-03","cited_by_count":177,"area":"inference"},{"id":"2112.13890","title":"SPViT: Enabling Faster Vision Transformers via Soft Token Pruning","created":"2021-12-27","cited_by_count":167,"area":"inference"},{"id":"2312.01026","title":"Token Fusion: Bridging the Gap between Token Pruning and Token Merging","created":"2023-12-02","cited_by_count":125,"area":"inference"},{"id":"2304.10716","title":"Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers","created":"2023-04-21","cited_by_count":81,"area":"inference"},{"id":"2305.17997","title":"DiffRate : Differentiable Compression Rate for Efficient Vision Transformers","created":"2023-05-29","cited_by_count":77,"area":"inference"},{"id":"2412.16117","title":"PruneVid: Visual Token Pruning for Efficient Video Large Language Models","created":"2024-12-20","cited_by_count":67,"area":"inference"},{"id":"2305.17328","title":"Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers","created":"2023-05-27","cited_by_count":62,"area":"inference"},{"id":"2308.01045","title":"Dynamic Token Pruning in Plain Vision Transformers for Semantic Segmentation","created":"2023-08-02","cited_by_count":62,"area":"inference"},{"id":"2308.04657","title":"Which Tokens to Use? Investigating Token Reduction in Vision Transformers","created":"2023-08-09","cited_by_count":60,"area":"inference"},{"id":"2403.02991","title":"MADTP: Multimodal Alignment-Guided Dynamic Token Pruning for Accelerating Vision-Language Transformer","created":"2024-03-05","cited_by_count":54,"area":"inference"},{"id":"2306.07050","title":"Revisiting Token Pruning for Object Detection and Instance Segmentation","created":"2023-06-12","cited_by_count":50,"area":"inference"},{"id":"2505.18227","title":"Token Reduction Should Go Beyond Efficiency in Generative Models -- From Vision, Language to Multimodality","created":"2025-05-23","cited_by_count":50,"area":"inference"},{"id":"2211.08110","title":"HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers","created":"2022-11-15","cited_by_count":46,"area":"inference"},{"id":"2409.01156","title":"TempMe: Video Temporal Token Merging for Efficient Text-Video Retrieval","created":"2024-09-02","cited_by_count":40,"area":"inference"},{"id":"2310.01812","title":"PPT: Token Pruning and Pooling for Efficient Vision Transformers","created":"2023-10-03","cited_by_count":38,"area":"inference"},{"id":"2209.13802","title":"Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention","created":"2022-09-28","cited_by_count":35,"area":"inference"},{"id":"2405.16148","title":"Accelerating Transformers with Spectrum-Preserving Token Merging","created":"2024-05-25","cited_by_count":35,"area":"inference"},{"id":"2406.09936","title":"ALGM: Adaptive Local-then-Global Token Merging for Efficient Semantic Segmentation with Plain Vision Transformers","created":"2024-06-14","cited_by_count":28,"area":"inference"},{"id":"2409.18962","title":"Exploring Token Pruning in Vision State Space Models","created":"2024-09-27","cited_by_count":28,"area":"inference"},{"id":"2410.23782","title":"Video Token Merging for Long-form Video Understanding","created":"2024-10-31","cited_by_count":23,"area":"inference"},{"id":"2307.10780","title":"Learned Thresholds Token Merging and Pruning for Vision Transformers","created":"2023-07-20","cited_by_count":21,"area":"inference"},{"id":"2412.10569","title":"Learning to Merge Tokens via Decoupled Embedding for Efficient Vision Transformers","created":"2024-12-13","cited_by_count":21,"area":"inference"},{"id":"2311.03035","title":"GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation","created":"2023-11-06","cited_by_count":19,"area":"inference"},{"id":"2412.00965","title":"Token Cropr: Faster ViTs for Quite a Few Tasks","created":"2024-12-01","cited_by_count":19,"area":"inference"},{"id":"2303.08010","title":"Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models","created":"2023-03-14","cited_by_count":16,"area":"inference"},{"id":"2210.05832","title":"SaiT: Sparse Vision Transformers through Adaptive Token Pruning","created":"2022-10-11","cited_by_count":15,"area":"inference"},{"id":"2509.17650","title":"Evict3R: Training-Free Token Eviction for Memory-Bounded Streaming Visual Geometry Transformers","created":"2025-09-22","cited_by_count":14,"area":"inference"},{"id":"2409.08464","title":"VLTP: Vision-Language Guided Token Pruning for Task-Oriented Segmentation","created":"2024-09-13","cited_by_count":11,"area":"inference"},{"id":"2506.20066","title":"ToSA: Token Merging with Spatial Awareness","created":"2025-06-24","cited_by_count":11,"area":"inference"},{"id":"2511.14751","title":"Co-Me: Confidence-Guided Token Merging for Visual Geometric Transformers","created":"2025-11-18","cited_by_count":10,"area":"inference"},{"id":"2309.15755","title":"CAIT: Triple-Win Compression towards High Accuracy, Fast Inference, and Favorable Transferability For ViTs","created":"2023-09-27","cited_by_count":9,"area":"inference"},{"id":"2405.14467","title":"Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic Segmentation","created":"2024-05-23","cited_by_count":9,"area":"inference"},{"id":"2407.05941","title":"Pruning One More Token is Enough: Leveraging Latency-Workload Non-Linearities for Vision Transformers on the Edge","created":"2024-07-01","cited_by_count":9,"area":"inference"},{"id":"2409.00633","title":"Make Your ViT-based Multi-view 3D Detectors Faster via Token Compression","created":"2024-09-01","cited_by_count":9,"area":"inference"},{"id":"2410.11786","title":"Selection-p: Self-Supervised Task-Agnostic Prompt Compression for Faithfulness and Transferability","created":"2024-10-15","cited_by_count":9,"area":"inference"},{"id":"2506.05709","title":"Token Transforming: A Unified and Training-Free Token Compression Framework for Vision Transformer Acceleration","created":"2025-06-06","cited_by_count":9,"area":"inference"},{"id":"2406.01283","title":"Focus on the Core: Efficient Attention via Pruned Token Compression for Document Classification","created":"2024-06-03","cited_by_count":7,"area":"inference"},{"id":"2407.15219","title":"Efficient Visual Transformer by Learnable Token Merging","created":"2024-07-21","cited_by_count":7,"area":"inference"},{"id":"2306.14393","title":"Constraint-aware and Ranking-distilled Token Pruning for Efficient Transformer Inference","created":"2023-06-26","cited_by_count":6,"area":"inference"},{"id":"2408.06798","title":"Token Compensator: Altering Inference Cost of Vision Transformer without Re-Tuning","created":"2024-08-13","cited_by_count":6,"area":"inference"},{"id":"2306.04897","title":"Multi-Scale And Token Mergence: Make Your ViT More Efficient","created":"2023-06-08","cited_by_count":5,"area":"inference"},{"id":"2403.14047","title":"Accelerating ViT Inference on FPGA through Static and Dynamic Pruning","created":"2024-03-21","cited_by_count":5,"area":"inference"},{"id":"2504.04514","title":"Saliency-driven Dynamic Token Pruning for Large Language Models","created":"2025-04-06","cited_by_count":5,"area":"inference"},{"id":"2508.00367","title":"Representation Shift: Unifying Token Compression with FlashAttention","created":"2025-08-01","cited_by_count":5,"area":"inference"},{"id":"2303.06522","title":"Token Sparsification for Faster Medical Image Segmentation","created":"2023-03-11","cited_by_count":4,"area":"inference"},{"id":"2408.17062","title":"Vote&Mix: Plug-and-Play Token Reduction for Efficient Vision Transformer","created":"2024-08-30","cited_by_count":4,"area":"inference"},{"id":"2503.20282","title":"Faster Parameter-Efficient Tuning with Token Redundancy Reduction","created":"2025-03-26","cited_by_count":4,"area":"inference"},{"id":"2503.23455","title":"Efficient Token Compression for Vision Transformer with Spatial Information Preserved","created":"2025-03-30","cited_by_count":4,"area":"inference"},{"id":"2603.00527","title":"TP-Spikformer: Token Pruned Spiking Transformer","created":"2026-02-28","cited_by_count":4,"area":"inference"},{"id":"2303.02331","title":"Training-Free Acceleration of ViTs with Delayed Spatial Merging","created":"2023-03-04","cited_by_count":3,"area":"inference"},{"id":"2503.23459","title":"Reinforcement Learning-based Token Pruning in Vision Transformers: A Markov Game Approach","created":"2025-03-30","cited_by_count":3,"area":"inference"},{"id":"2505.15160","title":"Lossless Token Merging Even Without Fine-Tuning in Vision Transformers","created":"2025-05-21","cited_by_count":3,"area":"inference"},{"id":"2505.20100","title":"AdaTP: Attention-Debiased Token Pruning for Video Large Language Models","created":"2025-05-26","cited_by_count":3,"area":"inference"},{"id":"2507.09702","title":"Token Compression Meets Compact Vision Transformers: A Survey and Comparative Evaluation for Edge AI","created":"2025-07-13","cited_by_count":3,"area":"inference"},{"id":"2511.21477","title":"Frequency-Aware Token Reduction for Efficient Vision Transformer","created":"2025-11-26","cited_by_count":3,"area":"inference"},{"id":"2512.14661","title":"Focus: A Streaming Concentration Architecture for Efficient Vision-Language Models","created":"2025-12-16","cited_by_count":3,"area":"inference"},{"id":"2512.20120","title":"HEART-VIT: Hessian-Guided Efficient Dynamic Attention and Token Pruning in Vision Transformer","created":"2025-12-23","cited_by_count":3,"area":"inference"},{"id":"2606.10533","title":"Audio-Visual Exchange-Aware Token Pruning for Efficient Audio-Visual Captioning","created":"2026-06-09","cited_by_count":3,"area":"inference"},{"id":"2604.05718","title":"MPM: Mutual Pair Merging for Efficient Vision Transformers","created":"2026-04-07","cited_by_count":2,"area":"inference"},{"id":"2605.15852","title":"GHOST: Geometry-Hierarchical Online Streaming Token Eviction for Efficient 3D Reconstruction","created":"2026-05-15","cited_by_count":2,"area":"inference"},{"id":"2607.02612","title":"Fusion: A Framework for Unified Sequential Token AdaptatIon in VisiOn TraNsformers","created":"2026-07-01","cited_by_count":2,"area":"inference"},{"id":"2607.04605","title":"Do All Visual Tokens Matter Equally? Object-Evidence Preserving Token Merging for Vision-Language Retrieval","created":"2026-07-06","cited_by_count":2,"area":"inference"},{"id":"2502.13251","title":"Neural Attention Search","created":"2025-02-18","cited_by_count":1,"area":"inference"},{"id":"2504.17996","title":"Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning","created":"2025-04-25","cited_by_count":1,"area":"inference"},{"id":"2508.13204","title":"QuickMerge++: Fast Token Merging with Autoregressive Prior","created":"2025-08-16","cited_by_count":1,"area":"inference"},{"id":"2604.14563","title":"Revisiting Token Compression for Accelerating ViT-based Sparse Multi-View 3D Object Detectors","created":"2026-04-16","cited_by_count":1,"area":"inference"},{"id":"2604.15408","title":"Dispatch-Aware Ragged Attention for Pruned Vision Transformers","created":"2026-04-16","cited_by_count":1,"area":"inference"},{"id":"2605.27465","title":"AdaMerge: Salience-Aware Adaptive Token Merging for Training-Free Acceleration of Vision Transformers","created":"2026-05-26","cited_by_count":1,"area":"inference"},{"id":"2310.17177","title":"Bridging The Gaps Between Token Pruning and Full Pre-training via Masked Fine-tuning","created":"2023-10-26","cited_by_count":0,"area":"inference"},{"id":"2404.09245","title":"Arena: A Patch-of-Interest ViT Inference Acceleration System for Edge-Assisted Video Analytics","created":"2024-04-14","cited_by_count":0,"area":"inference"},{"id":"2411.18092","title":"Training Noise Token Pruning","created":"2024-11-27","cited_by_count":0,"area":"inference"},{"id":"2507.09514","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","created":"2025-07-13","cited_by_count":0,"area":"inference"},{"id":"2508.13599","title":"Towards Efficient Vision State Space Models via Token Merging","created":"2025-08-19","cited_by_count":0,"area":"inference"},{"id":"2509.14165","title":"Where Do Tokens Go? Understanding Pruning Behaviors in STEP at High Resolutions","created":"2025-09-17","cited_by_count":0,"area":"inference"},{"id":"2509.21764","title":"CubistMerge: Spatial-Preserving Token Merging For Diverse ViT Backbones","created":"2025-09-26","cited_by_count":0,"area":"inference"},{"id":"2511.19718","title":"Rethinking Vision Transformer Depth via Structural Reparameterization","created":"2025-11-24","cited_by_count":0,"area":"inference"},{"id":"2512.22760","title":"Neighbor-Aware Token Reduction via Hilbert Curve for Vision Transformers","created":"2025-12-28","cited_by_count":0,"area":"inference"},{"id":"2602.02163","title":"Reg4Pru: Regularisation Through Random Token Routing for Token Pruning","created":"2026-02-02","cited_by_count":0,"area":"inference"},{"id":"2602.03043","title":"SAFE-KD: Risk-Controlled Early-Exit Distillation for Vision Backbones","created":"2026-02-03","cited_by_count":0,"area":"inference"},{"id":"2603.22867","title":"TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI","created":"2026-03-24","cited_by_count":0,"area":"inference"},{"id":"2603.27900","title":"R\\'enyi Entropy: A New Token Pruning Metric for Vision Transformers","created":"2026-03-29","cited_by_count":0,"area":"inference"},{"id":"2604.00827","title":"Video Patch Pruning: Efficient Video Instance Segmentation via Early Token Reduction","created":"2026-04-01","cited_by_count":0,"area":"inference"},{"id":"2604.14789","title":"A Comparative Study of CNN Optimization Methods for Edge AI: Exploring the Role of Early Exits","created":"2026-04-16","cited_by_count":0,"area":"inference"},{"id":"2604.16745","title":"Why Training-Free Token Reduction Collapses: The Inherent Instability of Pairwise Scoring Signals","created":"2026-04-17","cited_by_count":0,"area":"inference"},{"id":"2605.08371","title":"PrePARE: Pre-AA Token Pruning for Frozen Multi-View Geometry Transformers","created":"2026-05-08","cited_by_count":0,"area":"inference"},{"id":"2605.09276","title":"Uncertainty-Aware Token Importance Estimation in Spiking Transformers","created":"2026-05-10","cited_by_count":0,"area":"inference"},{"id":"2605.11683","title":"DORA: Dynamic Online Reinforcement Agent for Token Merging in Vision Transformers","created":"2026-05-12","cited_by_count":0,"area":"inference"},{"id":"2605.14913","title":"Representative Attention For Vision Transformers","created":"2026-05-14","cited_by_count":0,"area":"inference"},{"id":"2606.16633","title":"DCP-Prune: Ultra-Low Token Pruning with Distribution Consistency Preservation","created":"2026-06-15","cited_by_count":0,"area":"inference"},{"id":"2607.15563","title":"Are All Tokens Necessary for Visual Place Recognition? An Empirical Study of Token Reduction for Efficient Inference","created":"2026-07-17","cited_by_count":0,"area":"inference"},{"id":"2607.20467","title":"DC-Leap: Training-Free Acceleration of dLLMs via Draft-Guided Contiguous Leaping Decoding","created":"2026-05-19","cited_by_count":0,"area":"inference"},{"id":"2608.00077","title":"Beyond Accuracy: Auditing Spatial Provenance in Visual Token Pruning for OCR-Critical MLLM Inference","created":"2026-07-29","cited_by_count":0,"area":"inference"},{"id":"2608.03681","title":"Keep the Needle, Prune the Haystack: Defect-Preserving Token Pruning for Efficient Zero-Shot Anomaly Detection","created":"2026-08-04","cited_by_count":0,"area":"inference"},{"id":"2608.10989","title":"Putting Registers to Work: Task Registers for Token Pruning in Vision Transformers","created":"2026-08-11","cited_by_count":0,"area":"inference"},{"id":"2608.13141","title":"MergeOver: Post-Training Token Merging for Recursive Vision Transformers","created":"2026-08-13","cited_by_count":0,"area":"inference"},{"id":"2608.22526","title":"RS$^3$-Prune: Read-Sparse, Store-Sparse Token Pruning for Video Object Segmentation","created":"2026-08-23","cited_by_count":0,"area":"inference"},{"id":"2608.28911","title":"SemKV: Semantic Mixed-Precision KV Cache Quantization Guided by the Quality Cliff for Long-Context LLM Inference","created":"2026-08-28","cited_by_count":0,"area":"inference"},{"id":"2609.01232","title":"Position Matters: Feature Inversion Attacks in ViT Split Inference with Token Reduction and Shuffling","created":"2026-09-01","cited_by_count":0,"area":"inference"},{"id":"2609.03318","title":"Iapetus: Content-Aware Hierarchical Scheduling for Collaborative ViT Inference in LEO Satellite Networks","created":"2026-09-03","cited_by_count":0,"area":"inference"}],"next_cursor":"100"}