1 citations · 1 across the 11 of their papers we have counts for
13 papers
StrixAE: An Intelligent Agent for Audio Enhancement under Complex Distortion Coupling in Real-World Scenarios
Chenglin Wu, Junjie Wu, Jinhang Chen +5
Audio enhancement in real-world scenarios involves complex distortion couplings and requires personalized enhancement. Existing solutions struggle to address both simultaneously. T…
DynamicVerse: A Physically-Aware Multimodal Framework for 4D World Modeling
Kairun Wen, Yuzhi Huang, Runyu Chen +16
Understanding the dynamic physical world, characterized by its evolving 3D structure, real-world motion, and semantic content with textual descriptions, is crucial for human-agent…
Dissecting Generalized Category Discovery: Multiplex Consensus under Self-Deconstruction
Luyao Tang, Kunze Huang, Chaoqi Chen +5
Human perceptual systems excel at inducing and recognizing objects across both known and novel categories, a capability far beyond current machine learning frameworks. While genera…
JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent
Yunlong Lin, Zixu Lin, Kunjie Lin +8
Photo retouching has become integral to contemporary visual storytelling, enabling users to capture aesthetics and express creativity. While professional tools such as Adobe Lightr…
Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline
Yuzhi Huang, Chenxin Li, Haitao Zhang +9
Video anomaly detection (VAD) is crucial in scenarios such as surveillance and autonomous driving, where timely detection of unexpected activities is essential. Although existing m…
PCMamba: Physics-Informed Cross-Modal State Space Model for Dual-Camera Compressive Hyperspectral Imaging
Ge Meng, Zhongnan Cai, Jingyan Tu +4
Panchromatic (PAN) -assisted Dual-Camera Compressive Hyperspectral Imaging (DCCHI) is a key technology in snapshot hyperspectral imaging. Existing research primarily focuses on exp…
FRN: Fractal-Based Recursive Spectral Reconstruction Network
Ge Meng, Zhongnan Cai, Ruizhe Chen +4
Generating hyperspectral images (HSIs) from RGB images through spectral reconstruction can significantly reduce the cost of HSI acquisition. In this paper, we propose a Fractal-Bas…
JarvisIR: Elevating Autonomous Driving Perception with Intelligent Image Restoration
Yunlong Lin, Zixu Lin, Haoyu Chen +6
Vision-centric perception systems struggle with unpredictable and coupled weather degradations in the wild. Current solutions are often limited, as they either depend on specific d…
Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables
Zhongnan Cai, Yingying Wang, Hui Zheng +10
Recently, deep learning-based pan-sharpening algorithms have achieved notable advancements over traditional methods. However, deep learning-based methods incur substantial computat…
Bridging Synthetic-to-Real Gaps: Frequency-Aware Perturbation and Selection for Single-shot Multi-Parametric Mapping Reconstruction
Linyu Fan, Che Wang, Ming Ye +7
Data-centric artificial intelligence (AI) has remarkably advanced medical imaging, with emerging methods using synthetic data to address data scarcity while introducing synthetic-t…
Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors
Yunlong Lin, Zhenqi Fu, Kairun Wen +7
Low-light image enhancement (LIE) aims at precisely and efficiently recovering an image degraded in poor illumination environments. Recent advanced LIE techniques are using deep ne…
Bootstrap Segmentation Foundation Model under Distribution Shift via Object-Centric Learning
Luyao Tang, Yuxuan Yuan, Chaoqi Chen +3
Foundation models have made incredible strides in achieving zero-shot or few-shot generalization, leveraging prompt engineering to mimic the problem-solving approach of human intel…
AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancement
Yunlong Lin, Tian Ye, Sixiang Chen +6
Existing low-light image enhancement (LIE) methods have achieved noteworthy success in solving synthetic distortions, yet they often fall short in practical applications. The limit…