2 citations · 2 across the 5 of their papers we have counts for
5 papers
H-GRPO: Permutation-Invariant Reinforcement Learning for Improving Spatial Grounded Reasoning
Eric Peh, Debaditya Roy, Chen Li +1
Vision-language models (VLMs) have achieved strong performance on visual reasoning tasks, yet a correct final answer does not necessarily imply that the model followed a correct or…
HMR3D: Hierarchical Multimodal Representation for 3D Scene Understanding with Large Vision-Language Model
Chen Li, Eric Peh, Basura Fernando
Recent advances in large vision-language models (VLMs) have shown significant promise for 3D scene understanding. Existing VLM-based approaches typically align 3D scene features wi…
ChainReaction: Causal Chain-Guided Reasoning for Modular and Explainable Causal-Why Video Question Answering
Paritosh Parmar, Eric Peh, Basura Fernando
Existing Causal-Why Video Question Answering (VideoQA) models often struggle with higher-order reasoning, relying on opaque, monolithic pipelines that entangle video understanding,…
CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes
Paritosh Parmar, Eric Peh, Ruirui Chen +4
Causal video question answering (QA) has garnered increasing interest, yet existing datasets often lack depth in causal reasoning. To address this gap, we capitalize on the unique…
Learning to Visually Connect Actions and their Effects
Paritosh Parmar, Eric Peh, Basura Fernando
We introduce the novel concept of visually Connecting Actions and Their Effects (CATE) in video understanding. CATE can have applications in areas like task planning and learning f…