2 citations · 3 across the 8 of their papers we have counts for
8 papers · 1 filter
Learning to Restore More: Continual Capability Expansion for Pretrained Image Restoration Models
Hu Gao, Yulong Chen, Lizhuang Ma
Image restoration models are typically trained with a fixed set of capabilities. When new restoration requirements emerge, existing solutions usually train additional models or joi…
Causal-AgentIR: Self-Evolving Causal Memory for Adaptive Image Restoration Agents
Hu Gao, Yulong Chen, Lizhuang Ma
Image restoration agents have recently emerged as a flexible paradigm for handling diverse and unpredictable degradations in real-world scenarios. Existing agents typically formula…
Learning Adaptive Dynamical Features via Multi- Liquid-Mamba for All-in-one Image Restoration
Hu Gao, Changshuo Wang, Yulong Chen +1
Image restoration aims to recover high-quality images from degraded observations. Recent Mamba-based image restoration models have demonstrated strong potential in modeling long-ra…
Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection
Wenbing Zhu, Lidong Wang, Ziqing Zhou +14
The increasing complexity of industrial anomaly detection (IAD) has positioned multimodal detection methods as a focal area of machine vision research. However, dedicated multimoda…
Reconstructing In-the-Wild Open-Vocabulary Human-Object Interactions
Boran Wen, Dingbang Huang, Zichen Zhang +6
Reconstructing human-object interactions (HOI) from single images is fundamental in computer vision. Existing methods are primarily trained and tested on indoor scenes due to the l…
One-for-More: Continual Diffusion Model for Anomaly Detection
Xiaofan Li, Xin Tan, Zhuo Chen +8
With the rise of generative models, there is a growing interest in unifying all tasks within a generative framework. Anomaly detection methods also fall into this scope and utilize…