1 citations · 2 across the 3 of their papers we have counts for
5 papers
Recover and Match: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Optimal Transport
Hao Tan, Zichang Tan, Jun Li +3
Identifying multiple novel classes in an image, known as open-vocabulary multi-label recognition, is a challenging task in computer vision. Recent studies explore the transfer of p…
SSPA: Split-and-Synthesize Prompting with Gated Alignments for Multi-Label Image Recognition
Hao Tan, Zichang Tan, Jun Li +3
Multi-label image recognition is a fundamental task in computer vision. Recently, Vision-Language Models (VLMs) have made notable advancements in this area. However, previous metho…
Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation
Zhigang Chen, Benjia Zhou, Jun Li +5
Previous Sign Language Translation (SLT) methods achieve superior performance by relying on gloss annotations. However, labeling high-quality glosses is a labor-intensive task, whi…
PVLR: Prompt-driven Visual-Linguistic Representation Learning for Multi-Label Image Recognition
Hao Tan, Zichang Tan, Jun Li +2
Multi-label image recognition is a fundamental task in computer vision. Recently, vision-language models have made notable advancements in this area. However, previous methods ofte…
Compound Text-Guided Prompt Tuning via Image-Adaptive Cues
Hao Tan, Jun Li, Yizhuang Zhou +3
Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable generalization capabilities to downstream tasks. However, existing prompt tuning based frameworks need to pa…