9 papers
ReST-KV: Robust KV Cache Eviction with Layer-wise Output Reconstruction and Spatial-Temporal Smoothing
Yongqi An, Chang Lu, Kuan Zhu +5
Large language models (LLMs) face growing challenges in efficient generative inference due to the increasing memory demands of Key-Value (KV) caches, especially for long sequences.…
UniFGVC: Universal Training-Free Few-Shot Fine-Grained Vision Classification via Attribute-Aware Multimodal Retrieval
Hongyu Guo, Xiangzhao Hao, Jiarui Guo +3
Few-shot fine-grained visual classification (FGVC) aims to leverage limited data to enable models to discriminate subtly distinct categories. Recent works mostly finetuned the pre-…
Improving Generalization in LLM Structured Pruning via Function-Aware Neuron Grouping
Tao Yu, Yongqi An, Kuan Zhu +3
Large Language Models (LLMs) demonstrate impressive performance across natural language tasks but incur substantial computational and storage costs due to their scale. Post-trainin…
Referring Expression Instance Retrieval and A Strong End-to-End Baseline
Xiangzhao Hao, Kuan Zhu, Hongyu Guo +5
Using natural language to query visual information is a fundamental need in real-world applications. Text-Image Retrieval (TIR) retrieves a target image from a gallery based on an…
FOCUS: Fine-grained Optimization with Semantic Guided Understanding for Pedestrian Attributes Recognition
Hongyan An, Kuan Zhu, Xin He +4
Pedestrian attribute recognition (PAR) is a fundamental perception task in intelligent transportation and security. To tackle this fine-grained task, most existing methods focus on…
Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence
Jinghan He, Kuan Zhu, Haiyun Guo +6
Large vision-language models (LVLMs) have made substantial progress in integrating large language models (LLMs) with visual inputs, enabling advanced multimodal reasoning. Despite…