5 papers
RestoreKV: Recovering Full-Cache Behavior Under Aggressive Query-Agnostic KV Cache Eviction
Changwoo Baek, Seungjun Shin, Kyeongbo Kong
Query-agnostic KV cache eviction compresses a context once and reuses the resulting cache for arbitrary future queries, but performance can collapse under tight budgets. Existing m…
3DZip: Spatial-Aware Feature Diversity-Guided Token Compression for 3D Question Answering
Changwoo Baek, Kyeongbo Kong
Recent 3D vision-language models (3D VLMs) construct geometry aware tokens by projecting 2D visual features into world coordinates, enabling spatial reasoning for tasks such as 3D…
Structured Redundancy Modeling for Efficient Visual Token Pruning in High-Resolution MLLMs
Jouwon Song, Woohyeong Kim, Kyeongbo Kong
Recent high-resolution Multimodal Large Language Models (MLLMs) generate thousands of visual tokens per input, leading to a visual token explosion that introduces severe latency bo…
Focus Matters: Phase-Aware Suppression for Hallucination in Vision-Language Models
Sohyeon Kim, Sang Yeon Yoon, Kyeongbo Kong
Large Vision-Language Models (LVLMs) have achieved impressive progress in multimodal reasoning, yet they remain prone to object hallucinations, generating descriptions of objects t…
AgilePruner: An Empirical Study of Attention and Diversity for Adaptive Visual Token Pruning in Large Vision-Language Models
Changwoo Baek, Jouwon Song, Sohyeon Kim +1
Large Vision-Language Models (LVLMs) have adopted visual token pruning strategies to mitigate substantial computational overhead incurred by extensive visual token sequences. While…