3 citations · 9 across the 24 of their papers we have counts for
11 papers · 1 filter
MemLife: Curating and Reasoning over Long-Term Egocentric Video Memories
Guangzhi Xiong, Xinyuan Zhang, Xiao Yang +14
Long-term egocentric video enables personalized AI assistants to reason about daily life. However, as video histories grow to hundreds of hours spanning months or years, reprocessi…
Monitorable Chart Reasoning Agents via Verifiable Process Rewards
Sanchit Sinha, Oana Frunza, Kashif Rasul +1
Chart reasoning agents are increasingly used to extract actionable insights in critical domains, achieving state-of-the-art performance on multiple benchmarks. Yet, high benchmark…
Towards Robustness against Typographic Attack with Training-free Concept Localization
Bohan Liu, Wenqian Ye, Guangzhi Xiong +3
Models trained via Contrastive Language-Image Pretraining (CLIP) serve as the foundational vision encoders for most modern Large Vision Language Models (LVLMs). Despite their wides…
Attention-guided Fine-tuning of Multimodal Large Language Models Improves Chain-of-Thought Reasoning
Sanchit Sinha, Guangzhi Xiong, Bohan Liu +2
The effectiveness of Chain-of-Thought (CoT) prompting in Multimodal Large Language Models (MLLMs) remains uncertain: across several visual reasoning benchmarks, CoT prompting often…
Rethinking Visual Attribution for Chest X-ray Reasoning in Large Vision Language Models
Guangzhi Xiong, Qiao Jin, Sanchit Sinha +2
Large Vision Language Models (LVLMs) show promise in medical applications, but their inability to faithfully ground responses in visual evidence raises serious concerns about clini…
Retrieving Counterfactuals Improves Visual In-Context Learning
Guangzhi Xiong, Sanchit Sinha, Zhenghao He +1
Vision-language models (VLMs) have achieved impressive performance across a wide range of multimodal reasoning tasks, but they often struggle to disentangle fine-grained visual att…
Concept-RuleNet: Grounded Multi-Agent Neurosymbolic Reasoning in Vision Language Models
Sanchit Sinha, Guangzhi Xiong, Zhenghao He +1
Modern vision-language models (VLMs) deliver impressive predictive accuracy yet offer little insight into 'why' a decision is reached, frequently hallucinating facts, particularly…
COCO-Tree: Compositional Hierarchical Concept Trees for Enhanced Reasoning in Vision Language Models
Sanchit Sinha, Guangzhi Xiong, Aidong Zhang
Compositional reasoning remains a persistent weakness of modern vision language models (VLMs): they often falter when a task hinges on understanding how multiple objects, attribute…
Chart-RVR: Reinforcement Learning with Verifiable Rewards for Explainable Chart Reasoning
Sanchit Sinha, Oana Frunza, Kashif Rasul +2
The capabilities of Large Vision-Language Models (LVLMs) have reached state-of-the-art on many visual reasoning tasks, including chart reasoning, yet they still falter on out-of-di…
GCAV: A Global Concept Activation Vector Framework for Cross-Layer Consistency in Interpretability
Zhenghao He, Sanchit Sinha, Guangzhi Xiong +1
Concept Activation Vectors (CAVs) provide a powerful approach for interpreting deep neural networks by quantifying their sensitivity to human-defined concepts. However, when comput…
ASCENT-ViT: Attention-based Scale-aware Concept Learning Framework for Enhanced Alignment in Vision Transformers
Sanchit Sinha, Guangzhi Xiong, Aidong Zhang
As Vision Transformers (ViTs) are increasingly adopted in sensitive vision applications, there is a growing demand for improved interpretability. This has led to efforts to forward…