3 papers
cs.CV2026
Learning What Matters: Prioritized Concept Learning via Relative Error-driven Sample Selection
Shivam Chandhok, Qian Yang, Oscar Manas +3
Instruction tuning has been central to the success of recent vision-language models (VLMs), but it remains expensive-requiring large-scale datasets, high-quality annotations, and l…
cs.CV2025
Assessing and Learning Alignment of Unimodal Vision and Language Models
Le Zhang, Qian Yang, Aishwarya Agrawal
How well are unimodal vision and language models aligned? Although prior work have approached answering this question, their assessment methods do not directly translate to how the…
cs.CV2024
Decompose and Compare Consistency: Measuring VLMs' Answer Reliability via Task-Decomposition Consistency Comparison
Qian Yang, Weixiang Yan, Aishwarya Agrawal
Despite tremendous advancements, current state-of-the-art Vision-Language Models (VLMs) are still far from perfect. They tend to hallucinate and may generate biased responses. In s…