2 citations · 3 across the 2 of their papers we have counts for
8 papers · 1 filter
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…
ELIP: Efficient Discriminative Language-Image Pre-training with Fewer Vision Tokens
Yangyang Guo, Haoyu Zhang, Yongkang Wong +2
Learning a versatile language-image model is computationally prohibitive under a limited computing budget. This paper delves into the \emph{efficient language-image pre-training},…
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…
Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models
Yu-Wei Zhan, Fan Liu, Xin Luo +3
Human-Object Interaction (HOI) detection aims at detecting human-object pairs and predicting their interactions. However, conventional HOI detection methods often struggle to fully…
UNK-VQA: A Dataset and a Probe into the Abstention Ability of Multi-modal Large Models
Yangyang Guo, Fangkai Jiao, Zhiqi Shen +2
Teaching Visual Question Answering (VQA) models to refrain from answering unanswerable questions is necessary for building a trustworthy AI system. Existing studies, though have ex…