5 papers · 1 filter
Make Your LVLM KV Cache More Lightweight
Xihao Chen, Yangyang Guo, Roger Zimmermann
Key-Value (KV) cache has become a de facto component of modern Large Vision-Language Models (LVLMs) for inference. While it enhances decoding efficiency in Large Language Models (L…
VidLBEval: Benchmarking and Mitigating Language Bias in Video-Involved LVLMs
Yiming Yang, Yangyang Guo, Hui Lu +1
Recently, Large Vision-Language Models (LVLMs) have made significant strides across diverse multimodal tasks and benchmarks. This paper reveals a largely under-explored problem fro…
Joint Vision-Language Social Bias Removal for CLIP
Haoyu Zhang, Yangyang Guo, Mohan Kankanhalli
Vision-Language (V-L) pre-trained models such as CLIP show prominent capabilities in various downstream tasks. Despite this promise, V-L models are notoriously limited by their inh…
SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency
Yangyang Guo, Mohan Kankanhalli
While contrastive pre-training is widely employed, its data efficiency problem has remained relatively under-explored thus far. Existing methods often rely on static coreset select…
VidHal: Benchmarking Temporal Hallucinations in Vision LLMs
Wey Yeh Choong, Yangyang Guo, Mohan Kankanhalli
Vision Large Language Models (VLLMs) are widely acknowledged to be prone to hallucinations. Existing research addressing this problem has primarily been confined to image inputs, w…