7 papers
Rethinking Multi-Label Image Classification With Deep Learning: Taxonomy, Challenge, and Outlook
Xuelin Zhu, Xiu-Shen Wei, Jiawei Ge +2
Multi-label image classification (MLIC), a fundamental task in computer vision, focuses on identifying multiple objects or concepts within an image, underpinning numerous read-worl…
Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing for Weakly-Supervised Camouflaged Object Detection with Scribble Annotations
Jiawei Ge, Jiuxin Cao, Xinyi Li +5
Weakly-Supervised Camouflaged Object Detection (WSCOD) aims to locate and segment objects that are visually concealed within their surrounding scenes, relying solely on sparse supe…
Denoise-then-Retrieve: Text-Conditioned Video Denoising for Video Moment Retrieval
Weijia Liu, Jiuxin Cao, Bo Miao +6
Current text-driven Video Moment Retrieval (VMR) methods encode all video clips, including irrelevant ones, disrupting multimodal alignment and hindering optimization. To this end,…
Context-Enhanced Video Moment Retrieval with Large Language Models
Weijia Liu, Bo Miao, Jiuxin Cao +4
Current methods for Video Moment Retrieval (VMR) struggle to align complex situations involving specific environmental details, character descriptions, and action narratives. To ta…
Query-Based Knowledge Sharing for Open-Vocabulary Multi-Label Classification
Xuelin Zhu, Jian Liu, Dongqi Tang +4
Identifying labels that did not appear during training, known as multi-label zero-shot learning, is a non-trivial task in computer vision. To this end, recent studies have attempte…
Text as Image: Learning Transferable Adapter for Multi-Label Classification
Xuelin Zhu, Jiuxin Cao, Jian liu +7
Pre-trained vision-language models have notably accelerated progress of open-world concept recognition. Their impressive zero-shot ability has recently been transferred to multi-la…