activity
20242026
collaborators

7 papers

cs.CV2026

Beyond the Global Scores: Fine-Grained Token Grounding as a Robust Detector of LVLM Hallucinations

Tuan Dung Nguyen, Minh Khoi Ho, Qi Chen +8

Large vision-language models (LVLMs) achieve strong performance on visual reasoning tasks but remain highly susceptible to hallucination. Existing detection methods predominantly r…

cs.CV2025

ChartAB: A Benchmark for Chart Grounding & Dense Alignment

Aniruddh Bansal, Davit Soselia, Dang Nguyen +1

Charts play an important role in visualization, reasoning, data analysis, and the exchange of ideas among humans. However, existing vision-language models (VLMs) still lack accurat…

cs.CV2025

FaSTA: Fast-Slow Toolpath Agent with Subroutine Mining for Efficient Multi-turn Image Editing

Advait Gupta, Rishie Raj, Dang Nguyen +1

We develop a cost-efficient neurosymbolic agent to address challenging multi-turn image editing tasks such as ``Detect the bench in the image while recoloring it to pink. Also, rem…

cs.CV2025

ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness

Yijun Liang, Ming Li, Chenrui Fan +7

Color plays an important role in human perception and usually provides critical clues in visual reasoning. However, it is unclear whether and how vision-language models (VLMs) can…

cs.CV2025

CoSTA: Cost-Sensitive Toolpath Agent for Multi-turn Image Editing

Advait Gupta, NandaKiran Velaga, Dang Nguyen +1

Text-to-image models like stable diffusion and DALLE-3 still struggle with multi-turn image editing. We decompose such a task as an agentic workflow (path) of tool use that address…

cs.CL2024

Multi-Objective Linguistic Control of Large Language Models

Dang Nguyen, Jiuhai Chen, Tianyi Zhou

Large language models (LLMs), despite their breakthroughs on many challenging benchmark tasks, lean to generate verbose responses and lack the controllability of output complexity,…

cs.CL2024

RuleR: Improving LLM Controllability by Rule-based Data Recycling

Ming Li, Han Chen, Chenguang Wang +3

Large language models (LLMs) still lack delicate controllability over their responses, which is critical to enhancing their performance and the user experience. However, curating s…