2 citations · 3 across the 11 of their papers we have counts for
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Technical Report for ICML 2024 TiFA Workshop MLLM Attack Challenge: Suffix Injection and Projected Gradient Descent Can Easily Fool An MLLM
Yangyang Guo, Ziwei Xu, Xilie Xu +3
This technical report introduces our top-ranked solution that employs two approaches, \ie suffix injection and projected gradient descent (PGD) , to address the TiFA workshop MLLM…
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…
Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning
Jifan Zhang, Lalit Jain, Yang Guo +9
We present a novel multimodal preference dataset for creative tasks, consisting of over 250 million human ratings on more than 2.2 million captions, collected through crowdsourcing…