5 papers
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…
ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition
Mengqi Xue, Qihan Huang, Haofei Zhang +4
Prototypical part network (ProtoPNet) has drawn wide attention and boosted many follow-up studies due to its self-explanatory property for explainable artificial intelligence (XAI)…
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…
Calibrating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation
Lechao Cheng, Zerun Liu, Jingxuan He +3
Weakly supervised semantic segmentation (WSSS) has recently attracted considerable attention because it requires fewer annotations than fully supervised approaches, making it espec…
A Comprehensive Study of Structural Pruning for Vision Models
Changhao Li, Haoling Li, Mengqi Xue +6
Structural pruning has emerged as a promising approach for producing more efficient models. Nevertheless, the community suffers from a lack of standardized benchmarks and metrics,…