69 citations · 382 across the 33 of their papers we have counts for
27 papers · 1 filter
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…
Effective Abstract Reasoning with Dual-Contrast Network
Tao Zhuo, Mohan Kankanhalli
As a step towards improving the abstract reasoning capability of machines, we aim to solve Raven's Progressive Matrices (RPM) with neural networks, since solving RPM puzzles is hig…
PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences
Hehe Fan, Xin Yu, Yuhang Ding +2
Point cloud sequences are irregular and unordered in the spatial dimension while exhibiting regularities and order in the temporal dimension. Therefore, existing grid based convolu…
Learning to Predict Trustworthiness with Steep Slope Loss
Yan Luo, Yongkang Wong, Mohan S. Kankanhalli +1
Understanding the trustworthiness of a prediction yielded by a classifier is critical for the safe and effective use of AI models. Prior efforts have been proven to be reliable on…