🔥🔥🔥 A paper list of some recent works about Token Compress for Vit and VLM.
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FlashVLM: Text-Guided Visual Token Selection for Large Multimodal Models. [FlashVLM;]
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Focus: A Streaming Concentration Architecture for Efficient Vision-Language Models. [Focus; GitHub]
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EchoingPixels: Cross-Modal Adaptive Token Reduction for Efficient Audio-Visual LLMs. [EchoingPixels; Audio-Video]
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HybridToken-VLM: Hybrid Token Compression for Vision-Language Models. [HTC-VLM;]
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Less Is More, but Where? Dynamic Token Compression via LLM-Guided Keyframe Prior. [DyToK; GitHub]
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LLaVA-UHD v3: Progressive Visual Compression for Efficient Native-Resolution Encoding in MLLMs. [LLaVA-UHD v3;]
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FloC: FACILITY LOCATION-BASED EFFICIENT VI- SUAL TOKEN COMPRESSION FOR LONG VIDEO UN- DERSTANDING. [FloC;]
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VisionSelector: End-to-End Learnable Visual Token Compression for Efficient Multimodal LLMs. [VisionSelector; GitHub]
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AutoPrune: Each Complexity Deserves a Pruning Policy. [AutoPrune;NIPS2025; GitHub]
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LightVLM: Acceleraing Large Multimodal Models with Pyramid Token Merging and KV Cache Compression. [LightVLM;]
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MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs. [MMG-Vid;]
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VISA: Group-wise Visual Token Selection and Aggregation via Graph Summarization for Efficient MLLMs Inference. [VISA; GitHub]
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HiPrune: Training-Free Visual Token Pruning via Hierarchical Attention in Vision-Language Models. [HiPrune; GitHub]
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A Glimpse to Compress: Dynamic Visual Token Pruning for Large Vision-Language Models. [GlimpsePrune; GitHub]
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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers. [Short-LVLM; GitHub]
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When Tokens Talk Too Much: A Survey of Multimodal Long-Context Token Compression across Images, Videos, and Audios. [Survey ;GitHub]
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VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning. [VisionThink; GitHub]
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Multi-Granular Spatio-Temporal Token Merging for Training-Free Acceleration of Video LLMs. [STTM;ICCV; GitHub]
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Beyond Token Pruning: Operation Pruning in Vision-Language Models. [GSOP; GitHub]
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LaCo: Efficient Layer-wise Compression of Visual Tokens for Multimodal Large Language Models. [LaCo;]
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LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs. [LLaVA-Scissor;Video; GitHub]
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GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models. [GreedyPrune;]
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Learning Compact Vision Tokens for Efficient Large Multimodal Models. [LLaVA-STF;VQA; GitHub]
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DynTok: Dynamic Compression of Visual Tokens for Efficient and Effective Video Understanding. [DynTok; Video;]
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HoliTom: Holistic Token Merging for Fast Video Large Language Models. [HoliTom; Video; GitHub]
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AdaTP: Attention-Debiased Token Pruning for Video Large Language Models. [AdaTP; Video;]
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CrossLMM: Decoupling Long Video Sequences from LMMs via Dual Cross-Attention Mechanisms. [CrossLMM; Video; GitHub]
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Clapper: Compact Learning and Video Representation in VLMs. [Clapper; Video]
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Video Compression Commander:Plug-and-Play Inference Acceleration for Video Large Language Models. [VidCom2; Video; GitHub]
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Top-Down Compression: Revisit Efficient Vision Token Projection for Visual Instruction Tuning. [LLaVA-Meteor]
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Static or Dynamic: Towards Query-Adaptive Token Selection for Video Question Answering. [Explore-Then-Select; Video; EMNLP 2025; GitHub]
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FiLA-Video: Spatio-Temporal Compression for Fine-Grained Long Video Understanding. [FiLA-Video;Video]
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VCM: Vision Concept Modeling with Adaptive Vision Token Compression via Instruction Fine-Tuning. [VCM]
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TimeChat-Online: 80% Visual Tokens are Naturally Redundant in Streaming Videos. [TimeChat-Online; Video; GitHub]
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DYMU: Dynamic Merging and Virtual Unmerging for Efficient VLMs. [DYMU;GitHub]
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Quicksviewer: An LMM for Efficient Video Understanding via Reinforced Compression of Video Cubes. [Quicksviewer;Video;GitHub]
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PACT: Pruning and Clustering-Based Token Reduction for Faster Visual Language Models. [PACT;CVPR 2025;GitHub]
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QG-VTC: Question-Guided Visual Token Compression in MLLMs for Efficient VQA. [QG-VTC;VQA]
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TopV: Compatible Token Pruning with Inference Time Optimization for Fast and Low-Memory Multimodal Vision Language Model. [TopV; CVPR 2025]
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InternVL-X: Advancing and Accelerating InternVL Series with Efficient Visual Token Compression. [InternVL-X;GitHub]
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Token Dynamics: Towards Efficient and Dynamic Video Token Representation for Video Large Language Model. [Token Dynamics;Video]
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HICom:Hybrid-Level Instruction Injection for Video Token Compression in Multi-modal Large Language Models. [HICom;2025 CVPR;Video;GitHub]
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TwigVLM: Growing a Twig to Accelerate Large Vision-Language Models. [TwigVLM; ICCV 2025;GitHub]
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FastVID: Dynamic Density Pruning for Fast Video Large Language Models. [FastVID;GitHub]
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SAINT：Similarity-Aware Token Pruning: Your VLM but Faster. [SAINT;GitHub]
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STORM:Token-Efficient Long Video Understanding for Multimodal LLMs. [STORM;Video;NVIDIA]
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OpenReview Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification. [Dynamic-LLaVA; ICLR2025; GitHub]
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DivPrune: Diversity-based Visual Token Pruning for Large Multimodal Models. [DivPrune;GitHub]
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FCoT-VL:Advancing Text-oriented Large Vision-Language Models with Efficient Visual Token Compression. [FCoT-VL;]
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Beyond Token Compression: A Training-Free Reduction Framework for Efficient Visual Processing in MLLMs. [Beyond Token Compression;GitHub]
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FALCON: Resolving Visual Redundancy and Fragmentation in High-resolution Multimodal Large Language Models via Visual Registers. [Falcon; ICCV 2025; GitHub]
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DyRate:Dynamic Token Reduction during Generation for Vision Language Models. [DyRate]
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AdaFV: Accelerating VLMs with Self-Adaptive Cross-Modality Attention Mixture. [AdaFV]
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LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token. [LLAVA-MINI;GitHub]
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FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Visual Language Models. [FrameFusion;Video;GitHub]
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VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling. [VideoChat-Flash;Video;GitHub]
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RETAKE: Reducing Temporal and Knowledge Redundancy for Long Video Understanding. [RETAKE;Video;GitHub]
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FastVLM: Efficient Vision Encoding for Vision Language Models. [FastVLM;Apple;]
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PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models. [PVC;Video;GitHub]
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Dynamic-VLM: Simple Dynamic Visual Token Compression for VideoLLM. [Dynamic-VLM;Video;]
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VisionZip: Longer is Better but Not Necessary in Vision Language Models. [VisionZip;Video;GitHub]
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p-MoD: Building Mixture-of-Depths MLLMs via Progressive Ratio Decay. [p-MoD: GitHub]
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[CLS] Attention is All You Need for Training-Free Visual Token Pruning: Make VLM Inference Faster. [FasterVLM: GitHub]
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ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models. [ATP-LLaVA]
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OpenReview LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token. [LLaVA-Mini]
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Rethinking Token Reduction in MLLMs: Towards a Unified Paradigm for Training-Free Acceleration .[FiCoCo;]
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OpenReview LVP: Language-guide Visual Projector for Efficient Multimodal LLM.[LVP]
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OpenReview Efficient Multi-modal Large Language Models via Visual Token Grouping .[VisToG]
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DyCoke:Dynamic Compression of Tokens for Fast Video Large Language Models .[DyCoke;Video;Github]
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FocusLLaVA: A Coarse-to-Fine Approach for Efficient and Effective Visual Token Compression .[FocusLLaVA;]
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MustDrop:Multi-Stage Vision Token Dropping: Towards Efficient Multimodal Large Language Model .[MustDrop;Github]
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Don't Look Twice: Faster Video Transformers with Run-Length Tokenization .[RLT;Video;NeurIPS 2024;Github]
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Inference Optimal VLMs Need Only One Visual Token but Larger Models .[QueCC;Github]
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Video Token Merging for Long-form Video Understandin .[Learnable VTM;Video]
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LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding .[LongVU;Video;Github]
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PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction .[PyramidDrop;Github]
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Efficient Vision-Language Models by Summarizing Visual Tokens into Compact Registers .[Victor;]
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VidCompress: Memory-Enhanced Temporal Compression for Video Understanding in Large Language Models .[VidCompress;]
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Retrieval Replace Reduction:An effective visual token reduction method via semantic match .[TRSM;]
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AVG-LLaVA: A Large Multimodal Model with Adaptive Visual Granularity .[AVG-LLaVA;Github]
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Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs .[TRIM]
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TC-LLaVA: Rethinking the Transfer from Image to Video Understanding with Temporal Consideration .[TC-LLaVA;Video;]
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TG-LLaVA: Text Guided LLaVA via Learnable Latent Embeddings .[TG-LLaVA]
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mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding . [mPLUG-DocOwl2;Github]
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TempMe: Video Temporal Token Merging for Efficient Text-Video Retrieval . [TempMe;Video;ICLR 2025;Github]
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Recoverable Compression: A Multimodal Vision Token Recovery Mechanism Guided by Text Information . [Recoverable Compression]
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HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language Models in Resource-Constrained Environments . [HiRED;Github]
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mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models. [mPLUG-Owl3;Github]
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Token-level Correlation-guided Compression for Efficient Multimodal Document Understanding . [Token-level;Github]
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HiRes-LLaVA: Restoring Fragmentation Input in High-Resolution Large Vision-Language Models . [HiRes-LLaVA;]
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TokenPacker: Efficient Visual Projector for Multimodal LLM . [TokenPacker;Github]
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VoCo-LLaMA: Towards Vision Compression with Large Language Models . [VoCo-LLaMA;Github]
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DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language Models . [DeCo;Github]
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Matryoshka Query Transformer for Large Vision-Language Models . [MQT-LLaVA; NeurIPS 2024]Github]
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Matryoshka Multimodal Models . [Matryoshka;M3]Github]
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How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites . [InternVL;Pixel-Shuffle;Github]
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CATP: Cross-Attention Token Pruning for Accuracy Preserved Multimodal Model Inference . [CATP;]
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LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models . [LLaVA-PruMerge;Github]
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An Image is Worth 1/2 Tokens After Layer 2: Plug-and-PLay Acceleration for VLLM Inference . [FastV;ECCV 2024;Github]
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MobileVLM V2: Faster and Stronger Baseline for Vision Language Model . [LDP-v2;Github]
Honeybee: Locality-enhanced Projector for Multimodal LLM . [C-Abstractor;CVPR 2024;Github ]
LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models . [LLaMA-VID;ECCV 2024;Github ]
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond . [Resampler;Github]
CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers . [CrossGET; ICML 2024;Github]
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models . [Q-former;Github]
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Lossless Token Merging Even Without Fine-Tuning in Vision Transformers . [ATM]
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Prune and Merge: Efficient Token Compression For Vision Transformer With Spatial Information Preserved . [Prune and Merge;Github]
Token Cropr: Faster ViTs for Quite a Few Tasks.[Token Cropr;]
Vote&Mix: Plug-and-Play Token Reduction for Efficient Vision Transformer . [Vote&Mix;]
Token Compensator: Altering Inference Cost of Vision Transformer without Re-Tuning . [Token Compensator;ToCom;Github]
Dynamic and Compressive Adaptation of Transformers From Images to Videos . [InTI;]
LookupViT: Compressing visual information to a limited number of tokens . [LookupViT;DeepMind]
PYRA: Parallel Yielding Re-Activation for Training-Inference Efficient Task Adaptation . [PYRA;ECCV 2024;Github]
PPT: Token Pruning and Pooling for Efficient Vision Transformers . [PPT;Github]
DiffRate : Differentiable Compression Rate for Efficient Vision Transformers . [DiffRate;ICCV 2023;Github]
Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers . [TPS;CVPR 2023;Github]
TOKEN MERGING: YOUR VIT BUT FASTER . [ToMe;Token Merging; ICLR 2023]
Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention . [Adaptive Sparse ViT]
EViT: Expediting Vision Transformers via Token Reorganizations . [EViT;ICLR 2022;Github]
Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space . [ViT-Slim;CVPR 2022;Github]
A-ViT: Adaptive Tokens for Efficient Vision Transformer . [A-Vit;]
ATS: Adaptive Token Sampling For Efficient Vision Transformers . [ATS;ECCV 2022;Github]
Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer . [Evo-ViT;AAAI 2022;Github]
Patch Slimming for Efficient Vision Transformers . [Patch Slimming;]
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsificationr . [DynamicViT;NeurIPS 2021;Github]
