3 citations · 4 across the 6 of their papers we have counts for
7 papers
Webly Supervised Multi-Label Recognition: Evaluation Benchmark and Dual-Branch Multi-Label Contrastive Learning
Zhihua Xu, Zhijing Yang, Yufeng Yang +1
Training deep learning models with freely available web images can reduce their dependence on costly manual annotations. Although webly supervised learning has been widely studied…
Learning Semantic-Aware Threshold for Multi-Label Image Recognition with Partial Labels
Haoxian Ruan, Zhihua Xu, Zhijing Yang +4
Multi-label image recognition with partial labels (MLR-PL) is designed to train models using a mix of known and unknown labels. Traditional methods rely on semantic or feature corr…
Contrastive Decoupled Representation Learning and Regularization for Speech-Preserving Facial Expression Manipulation
Tianshui Chen, Jianman Lin, Zhijing Yang +3
Speech-preserving facial expression manipulation (SPFEM) aims to modify a talking head to display a specific reference emotion while preserving the mouth animation of source spoken…
Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models
Lei Tang, Jinghui Qin, Wenxuan Ye +2
Recently, Large language models (LLMs) with in-context learning have demonstrated remarkable potential in handling neural machine translation. However, existing evidence shows that…
Learning Semantic-Aware Representation in Visual-Language Models for Multi-Label Recognition with Partial Labels
Haoxian Ruan, Zhihua Xu, Zhijing Yang +3
Multi-label recognition with partial labels (MLR-PL), in which only some labels are known while others are unknown for each image, is a practical task in computer vision, since col…
Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration
Tianshui Chen, Weihang Wang, Tao Pu +4
Modern visual recognition models often display overconfidence due to their reliance on complex deep neural networks and one-hot target supervision, resulting in unreliable confiden…
Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
Tao Pu, Tianshui Chen, Hefeng Wu +3
Despite achieving impressive progress, current multi-label image recognition (MLR) algorithms heavily depend on large-scale datasets with complete labels, making collecting large-s…