2 citations · 5 across the 23 of their papers we have counts for
5 papers · 2 filters
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…