18 citations · 30 across the 15 of their papers we have counts for
7 papers · 1 filter
GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction
Tao Hu, Weiyu Zhou, Yanjie Tu +4
Pre-trained Latent Diffusion Models (LDMs) have recently shown strong perceptual priors for low-level vision tasks, making them a promising direction for multi-exposure High Dynami…
NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results
Xiaoning Liu, Zongwei Wu, Florin-Alexandru Vasluianu +102
This paper presents a comprehensive review of the NTIRE 2025 Low-Light Image Enhancement (LLIE) Challenge, highlighting the proposed solutions and final outcomes. The objective of…
Text-Visual Semantic Constrained AI-Generated Image Quality Assessment
Qiang Li, Qingsen Yan, Haojian Huang +3
With the rapid advancements in Artificial Intelligence Generated Image (AGI) technology, the accurate assessment of their quality has become an increasingly vital requirement. Prev…
AVadCLIP: Audio-Visual Collaboration for Robust Video Anomaly Detection
Peng Wu, Wanshun Su, Guansong Pang +4
With the increasing adoption of video anomaly detection in intelligent surveillance domains, conventional visual-based detection approaches often struggle with information insuffic…
NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results
Sangmin Lee, Eunpil Park, Angel Canelo +33
This paper reviews the NTIRE 2025 Efficient Burst HDR and Restoration Challenge, which aims to advance efficient multi-frame high dynamic range (HDR) and restoration techniques. Th…
FusionNet: Multi-model Linear Fusion Framework for Low-light Image Enhancement
Kangbiao Shi, Yixu Feng, Tao Hu +5
The advent of Deep Neural Networks (DNNs) has driven remarkable progress in low-light image enhancement (LLIE), with diverse architectures (e.g., CNNs and Transformers) and color s…
SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model
Zongcan Ding, Haodong Zhang, Peng Wu +4
Video anomaly detection (VAD) aims to identify unexpected events in videos and has wide applications in safety-critical domains. While semi-supervised methods trained on only norma…