2 citations · 6 across the 12 of their papers we have counts for
13 papers
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…
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…
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…
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,…
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…
PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning
Xinyu Zhang, Yuxuan Dong, Yanrui Wu +6
Large language models demonstrate remarkable capabilities across various domains, especially mathematics and logic reasoning. However, current evaluations overlook physics-based re…
Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios
Shantanu Jaiswal, Debaditya Roy, Basura Fernando +1
Complex visual reasoning and question answering (VQA) is a challenging task that requires compositional multi-step processing and higher-level reasoning capabilities beyond the imm…
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…
FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data
Binqian Xu, Xiangbo Shu, Haiyang Mei +3
Multimodal Large Language Models (MLLMs) have made significant advancements, demonstrating powerful capabilities in processing and understanding multimodal data. Fine-tuning MLLMs…
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…
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…
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…
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…