52 citations · 85 across the 8 of their papers we have counts for
8 papers
SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Bohao Li, Rui Wang, Guangzhi Wang +3
Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable…
Mining Conditional Part Semantics with Occluded Extrapolation for Human-Object Interaction Detection
Guangzhi Wang, Yangyang Guo, Mohan Kankanhalli
Human-Object Interaction Detection is a crucial aspect of human-centric scene understanding, with important applications in various domains. Despite recent progress in this field,…
What Makes for Good Visual Tokenizers for Large Language Models?
Guangzhi Wang, Yixiao Ge, Xiaohan Ding +2
We empirically investigate proper pre-training methods to build good visual tokenizers, making Large Language Models (LLMs) powerful Multimodal Large Language Models (MLLMs). In ou…
Text to Point Cloud Localization with Relation-Enhanced Transformer
Guangzhi Wang, Hehe Fan, Mohan Kankanhalli
Automatically localizing a position based on a few natural language instructions is essential for future robots to communicate and collaborate with humans. To approach this goal, w…
Chairs Can be Stood on: Overcoming Object Bias in Human-Object Interaction Detection
Guangzhi Wang, Yangyang Guo, Yongkang Wong +1
Detecting Human-Object Interaction (HOI) in images is an important step towards high-level visual comprehension. Existing work often shed light on improving either human and object…
Distance Matters in Human-Object Interaction Detection
Guangzhi Wang, Yangyang Guo, Yongkang Wong +1
Human-Object Interaction (HOI) detection has received considerable attention in the context of scene understanding. Despite the growing progress on benchmarks, we realize that exis…
Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognition
Ziwei Xu, Guangzhi Wang, Yongkang Wong +1
This paper proposes a novel model for recognizing images with composite attribute-object concepts, notably for composite concepts that are unseen during model training. We aim to e…
Multi-source Distilling Domain Adaptation
Sicheng Zhao, Guangzhi Wang, Shanghang Zhang +7
Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain…