21 citations · 46 across the 9 of their papers we have counts for
9 papers
ViewSAM: Learning View-aware Cross-modal Semantics for Weakly Supervised Cross-view Referring Multi-Object Tracking
Jiawei Ge, Xintian Zhang, Jiuxin Cao +9
Cross-view Referring Multi-Object Tracking (CRMOT) aims to track multiple objects specified by natural language across multiple camera views, with globally consistent identities. D…
Deconstructing the Failure of Ideal Noise Correction: A Three-Pillar Diagnosis
Chen Feng, Zhuo Zhi, Zhao Huang +5
Statistically consistent methods based on the noise transition matrix () offer a theoretically grounded solution to Learning with Noisy Labels (LNL), with guarantees of converge…
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…
CLIPCleaner: Cleaning Noisy Labels with CLIP
Chen Feng, Georgios Tzimiropoulos, Ioannis Patras
Learning with Noisy labels (LNL) poses a significant challenge for the Machine Learning community. Some of the most widely used approaches that select as clean samples for which th…
LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition
Zhonglin Sun, Chen Feng, Ioannis Patras +1
In this work we focus on learning facial representations that can be adapted to train effective face recognition models, particularly in the absence of labels. Firstly, compared wi…
Self-Supervised Representation Learning with Cross-Context Learning between Global and Hypercolumn Features
Zheng Gao, Chen Feng, Ioannis Patras
Whilst contrastive learning yields powerful representations by matching different augmented views of the same instance, it lacks the ability to capture the similarities between dif…
MaskCon: Masked Contrastive Learning for Coarse-Labelled Dataset
Chen Feng, Ioannis Patras
Deep learning has achieved great success in recent years with the aid of advanced neural network structures and large-scale human-annotated datasets. However, it is often costly an…
Adaptive Soft Contrastive Learning
Chen Feng, Ioannis Patras
Self-supervised learning has recently achieved great success in representation learning without human annotations. The dominant method -- that is contrastive learning, is generally…
SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise
Chen Feng, Georgios Tzimiropoulos, Ioannis Patras
Despite the large progress in supervised learning with neural networks, there are significant challenges in obtaining high-quality, large-scale and accurately labelled datasets. In…