activity
20192023
most citedSEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

52 citations · 85 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2023

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,…

cs.CV2023★ 5 cited

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…

cs.CV2023

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…

cs.CV2022

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…

cs.CV2022

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…

cs.CV2021★ 21 cited

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…