13 citations · 48 across the 24 of their papers we have counts for
9 papers · 1 filter
Spatial-Contextual Discrepancy Information Compensation for GAN Inversion
Ziqiang Zhang, Yan Yan, Jing-Hao Xue +1
Most existing GAN inversion methods either achieve accurate reconstruction but lack editability or offer strong editability at the cost of fidelity. Hence, how to balance the disto…
Federated Learning with Extremely Noisy Clients via Negative Distillation
Yang Lu, Lin Chen, Yonggang Zhang +4
Federated learning (FL) has shown remarkable success in cooperatively training deep models, while typically struggling with noisy labels. Advanced works propose to tackle label noi…
Towards Unseen Triples: Effective Text-Image-joint Learning for Scene Graph Generation
Qianji Di, Wenxi Ma, Zhongang Qi +3
Scene Graph Generation (SGG) aims to structurally and comprehensively represent objects and their connections in images, it can significantly benefit scene understanding and other…
PARFormer: Transformer-based Multi-Task Network for Pedestrian Attribute Recognition
Xinwen Fan, Yukang Zhang, Yang Lu +1
Pedestrian attribute recognition (PAR) has received increasing attention because of its wide application in video surveillance and pedestrian analysis. Extracting robust feature re…
Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation
Yan Jin, Mengke Li, Yang Lu +2
Deep neural networks have made huge progress in the last few decades. However, as the real-world data often exhibits a long-tailed distribution, vanilla deep models tend to be heav…
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentation
Yang Lu, Pinxin Qian, Gang Huang +1
Personalized Federated Learning (PFL) aims to learn personalized models for each client based on the knowledge across all clients in a privacy-preserving manner. Existing PFL metho…
MRCN: A Novel Modality Restitution and Compensation Network for Visible-Infrared Person Re-identification
Yukang Zhang, Yan Yan, Jie Li +1
Visible-infrared person re-identification (VI-ReID), which aims to search identities across different spectra, is a challenging task due to large cross-modality discrepancy between…
Diverse Embedding Expansion Network and Low-Light Cross-Modality Benchmark for Visible-Infrared Person Re-identification
Yukang Zhang, Hanzi Wang
For the visible-infrared person re-identification (VIReID) task, one of the major challenges is the modality gaps between visible (VIS) and infrared (IR) images. However, the train…
Federated Semi-Supervised Learning with Annotation Heterogeneity
Xinyi Shang, Gang Huang, Yang Lu +4
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data. Most of the existing FSSL work…