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Jiaxing Du

2 accepted papers

2026

Stop Mixing Things Up! BISCUIT Teaches Vision-Language Models to Learn New Concepts from Images on the Spot

AAAI 2026technical

Vision-Language Models (VLMs) have achieved impressive performance across various tasks, but often struggle to apply newly introduced visual concepts during inference. A common failure pattern is what we call Mixing Things Up: VLMs frequently confuse concept names, resulting in vague descriptions an

Cited by 0SourcePDFScholar
2026

Twin-T & TwintVQA: A Reliable Structure-Detail Separating VLM and a Comprehensive Benchmark for Chart and Table Tasks

CVPR 2026

With the rapid development of Vision-Language Models (VLMs), there is a growing demand for automatic analysis of structured visual data. Charts and tables carry quantitative information through regular layouts, explicit numbers, and chart-specific reading patterns, yet current VLMs still underuse th

Cited by 0SourcecodeScholar