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20242026
most citedCausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes

2 citations · 6 across the 12 of their papers we have counts for

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cs.CV2026

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers

Ridma Jayasundara, Shaheer Mohamed, Tharindu Fernando +6

Adversarial vulnerabilities remain a major concern for the safe deployment of neural networks, particularly in object detection, a core task embedded in many safety-critical system…

cs.CV2026

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…

cs.CV2025

CoFFT: Chain of Foresight-Focus Thought for Visual Language Models

Xinyu Zhang, Yuxuan Dong, Lingling Zhang +5

Despite significant advances in Vision Language Models (VLMs), they remain constrained by the complexity and redundancy of visual input. When images contain large amounts of irrele…

cs.CV2025

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,…

cs.CV2025★ 1 cited

Learning to Generate Long-term Future Narrations Describing Activities of Daily Living

Ramanathan Rajendiran, Debaditya Roy, Basura Fernando

Anticipating future events is crucial for various application domains such as healthcare, smart home technology, and surveillance. Narrative event descriptions provide context-rich…

cs.CV2024

Diagram-Driven Course Questions Generation

Xinyu Zhang, Lingling Zhang, Yanrui Wu +6

Visual Question Generation (VQG) research focuses predominantly on natural images while neglecting the diagram, which is a critical component in educational materials. To meet the…

cs.CV2024★ 1 cited

Effectively Leveraging CLIP for Generating Situational Summaries of Images and Videos

Dhruv Verma, Debaditya Roy, Basura Fernando

Situation recognition refers to the ability of an agent to identify and understand various situations or contexts based on available information and sensory inputs. It involves the…

cs.CV2024★ 2 cited

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…

cs.CV2024

Training-Free Action Recognition and Goal Inference with Dynamic Frame Selection

Ee Yeo Keat, Zhang Hao, Alexander Matyasko +1

We introduce VidTFS, a Training-free, open-vocabulary video goal and action inference framework that combines the frozen vision foundational model (VFM) and large language model (L…

cs.CV2024

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…