10 papers · 1 filter
PAPT++: Risk-Aware Adversarial Tuning and Generation for Single Domain Generalization
Zhipeng Xu, De Cheng, Xinyang Jiang +5
Single domain generalization (SDG) aims to learn a model from one labeled source domain that generalizes to unseen target domains. A common strategy is to enrich the source distrib…
Symbiosis-Inspired Knowledge Distillation for Incremental Object Detection
Mingyue Zeng, De Cheng, Zhipeng Xu +3
Incremental object detection (IOD) aims to extend detectors to new categories while retaining previously acquired knowledge. Existing methods often adopt a class incremental learni…
Dual-Branch Cross-Projection Debiasing through Diffusion-based Disentanglement
Xiangqian Zhao, Xinyang Jiang, Zhipeng Xu +5
Foundation models trained on biased datasets often rely on spurious correlations between target labels and non-causal attributes, resulting in poor generalization on minority group…
Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification
Haonan Shi, Yubin Wang, De Cheng +3
Unsupervised visible-infrared person re-identification (USVI-ReID) aims to learn modality-invariant image features from unlabeled cross-modal person datasets by reducing the modali…
Harnessing Textual Semantic Priors for Knowledge Transfer and Refinement in CLIP-Driven Continual Learning
Lingfeng He, De Cheng, Di Xu +2
Continual learning (CL) aims to equip models with the ability to learn from a stream of tasks without forgetting previous knowledge. With the progress of vision-language models lik…
EKPC: Elastic Knowledge Preservation and Compensation for Class-Incremental Learning
Huaijie Wang, De Cheng, Lingfeng He +4
Class-Incremental Learning (CIL) aims to enable AI models to continuously learn from sequentially arriving data of different classes over time while retaining previously acquired k…