activity
20212026
most citedTrustable Co-label Learning from Multiple Noisy Annotators

28 citations · 59 across the 13 of their papers we have counts for

collaborators
Showing cs.CVShow all

5 papers · 1 filter

cs.CV2025

TopoTTA: Topology-Enhanced Test-Time Adaptation for Tubular Structure Segmentation

Jiale Zhou, Wenhan Wang, Shikun Li +6

Tubular structure segmentation (TSS) is important for various applications, such as hemodynamic analysis and route navigation. Despite significant progress in TSS, domain shifts re…

cs.CV2024★ 1 cited

Learning Natural Consistency Representation for Face Forgery Video Detection

Daichi Zhang, Zihao Xiao, Shikun Li +3

Face Forgery videos have elicited critical social public concerns and various detectors have been proposed. However, fully-supervised detectors may lead to easily overfitting to sp…

cs.CV2024

DANCE: Dual-View Distribution Alignment for Dataset Condensation

Hansong Zhang, Shikun Li, Fanzhao Lin +3

Dataset condensation addresses the problem of data burden by learning a small synthetic training set that preserves essential knowledge from the larger real training set. To date,…

cs.CV2023★ 1 cited

M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy

Hansong Zhang, Shikun Li, Pengju Wang +2

Training state-of-the-art (SOTA) deep models often requires extensive data, resulting in substantial training and storage costs. To address these challenges, dataset condensation h…

cs.CV2022★ 23 cited

Selective-Supervised Contrastive Learning with Noisy Labels

Shikun Li, Xiaobo Xia, Shiming Ge +1

Deep networks have strong capacities of embedding data into latent representations and finishing following tasks. However, the capacities largely come from high-quality annotated l…