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Yu Weng

2 accepted papers

2025

Spotlighter: Revisiting Prompt Tuning from a Representative Mining View

EMNLP 2025

CLIP’s success has demonstrated that prompt tuning can achieve robust cross-modal semantic alignment for tasks ranging from open-domain recognition to fine-grained classification. However, redundant or weakly relevant feature components introduce noise and incur unnecessary computational costs. In t

Cited by 0SourcePDFScholar
2025

TVQACML: Benchmarking Text-Centric Visual Question Answering in Multilingual Chinese Minority Languages

EMNLP 2025

Text-Centric Visual Question Answering (TEC-VQA) is a critical research area that requires semantic interactions between objects and scene texts. However, most existing TEC-VQA benchmarks focus on high-resource languages like English and Chinese. Although few works expanding multilingual QA pairs in