5 papers · 1 filter
SIGMA: Bridging Structural and Distributional Gaps for Vision Foundation Model Adaptation
Lingyu Xiong, Jinjin Shi, Xuran Xu +3
Vision Foundation Models (VFMs) have demonstrated impressive representational capabilities. However, adapting them to downstream tasks via full fine-tuning incurs prohibitive compu…
Pareto-Enhanced Portrait Generation: Vision-Aligned Text Supervision for Alignment, Realism, and Aesthetics
Yunlong Wang, Jinjin Shi, Wenbin Gao +3
Text-to-image diffusion models often face a severe trilemma in human portrait generation: text-image alignment, photorealism, and human-perceived aesthetics inherently inhibit one…
KVCapsule: Efficient Sequential KV Cache Compression for Vision-Language Models with Asymmetric Redundancy
Yingbing Huang, Tharun Adithya Srikrishnan, Steven K. Reinhardt +1
Vision-Language Models (VLMs) have emerged as a critical and fast-growing extension of Large Language Models (LLMs) that enable multimodal reasoning through both text and image inp…
AdaRing: Towards Ultra-Light Vision-Language Adaptation via Cross-Layer Tensor Ring Decomposition
Ying Huang, Yuanbin Man, Wenqi Jia +3
Adapter-based fine-tuning has gained remarkable attention in adapting large pre-trained vision language models (VLMs) for a wide range of downstream tasks efficiently. In this para…
AdaCM: On Understanding Extremely Long-Term Video with Adaptive Cross-Modality Memory Reduction
Yuanbin Man, Ying Huang, Chengming Zhang +3
The advancements in large language models (LLMs) have propelled the improvement of video understanding tasks by incorporating LLMs with visual models. However, most existing LLM-ba…