1 citations · 1 across the 6 of their papers we have counts for
6 papers
Dual-domain Adaptation Networks for Realistic Image Super-resolution
Chaowei Fang, Bolin Fu, De Cheng +2
Realistic image super-resolution (SR) focuses on transforming real-world low-resolution (LR) images into high-resolution (HR) ones, handling more complex degradation patterns than…
Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning
Fangwen Wu, Lechao Cheng, Shengeng Tang +4
Class-incremental learning (CIL) seeks to enable a model to sequentially learn new classes while retaining knowledge of previously learned ones. Balancing flexibility and stability…
Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution Detection
Ying Yang, De Cheng, Chaowei Fang +4
Unsupervised out-of-distribution (OOD) detection aims to identify out-of-domain data by learning only from unlabeled In-Distribution (ID) training samples, which is crucial for dev…
Progressive Conservative Adaptation for Evolving Target Domains
Gangming Zhao, Chaoqi Chen, Wenhao He +5
Conventional domain adaptation typically transfers knowledge from a source domain to a stationary target domain. However, in many real-world cases, target data usually emerge seque…
Revisiting the Power of Prompt for Visual Tuning
Yuzhu Wang, Lechao Cheng, Chaowei Fang +3
Visual prompt tuning (VPT) is a promising solution incorporating learnable prompt tokens to customize pre-trained models for downstream tasks. However, VPT and its variants often e…
Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation
Jingxuan He, Lechao Cheng, Chaowei Fang +3
Compared to conventional semantic segmentation with pixel-level supervision, Weakly Supervised Semantic Segmentation (WSSS) with image-level labels poses the challenge that it alwa…