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Token Reduction

Papers

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Showing 51–75 of 78 papers

TitleStatusHype
Layton: Latent Consistency Tokenizer for 1024-pixel Image Reconstruction and Generation by 256 TokensCode0
Does Acceleration Cause Hidden Instability in Vision Language Models? Uncovering Instance-Level Divergence Through a Large-Scale Empirical Study—0
BatchGEMBA: Token-Efficient Machine Translation Evaluation with Batched Prompting and Prompt CompressionCode0
Knowing When to Stop: Dynamic Context Cutoff for Large Language Models—0
MINT: Mitigating Hallucinations in Large Vision-Language Models via Token Reduction—0
Learning Free Token Reduction for Multi-Modal Large Language Models—0
Dynamic Token Reduction during Generation for Vision Language Models—0
AdaFV: Rethinking of Visual-Language alignment for VLM acceleration—0
Cached Adaptive Token Merging: Dynamic Token Reduction and Redundant Computation Elimination in Diffusion ModelCode0
Rethinking Token Reduction with Parameter-Efficient Fine-Tuning in ViT for Pixel-Level TasksCode0
Cross-Layer Cache Aggregation for Token Reduction in Ultra-Fine-Grained Image RecognitionCode0
ImagePiece: Content-aware Re-tokenization for Efficient Image Recognition—0
Deploying Foundation Model Powered Agent Services: A Survey—0
AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration—0
Learning to Merge Tokens via Decoupled Embedding for Efficient Vision TransformersCode0
TRIM: Token Reduction and Inference Modeling for Cost-Effective Language Generation—0
Accelerating Multimodal Large Language Models by Searching Optimal Vision Token Reduction—0
Rethinking Token Reduction in MLLMs: Towards a Unified Paradigm for Training-Free Acceleration—0
Efficient Multi-modal Large Language Models via Visual Token Grouping—0
freePruner: A Training-free Approach for Large Multimodal Model Acceleration—0
PAR: Prompt-Aware Token Reduction Method for Efficient Large Multimodal Models—0
Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems—0
Vote&Mix: Plug-and-Play Token Reduction for Efficient Vision Transformer—0
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction—0
HaltingVT: Adaptive Token Halting Transformer for Efficient Video RecognitionCode0
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