2 citations · 2 across the 8 of their papers we have counts for
9 papers
Video Compression with Hierarchical Temporal Neural Representation
Jun Zhu, Xinfeng Zhang, Lv Tang +3
Video compression has recently benefited from implicit neural representations (INRs), which model videos as continuous functions. INRs offer compact storage and flexible reconstruc…
Frequency-aware Neural Representation for Videos
Jun Zhu, Xinfeng Zhang, Lv Tang +3
Implicit Neural Representations (INRs) have emerged as a promising paradigm for video compression. However, existing INR-based frameworks typically suffer from inherent spectral bi…
SANR: Scene-Aware Neural Representation for Light Field Image Compression with Rate-Distortion Optimization
Gai Zhang, Xinfeng Zhang, Lv Tang +3
Light field images capture multi-view scene information and play a crucial role in 3D scene reconstruction. However, their high-dimensional nature results in enormous data volumes,…
MSNeRV: Neural Video Representation with Multi-Scale Feature Fusion
Jun Zhu, Xinfeng Zhang, Lv Tang +1
Implicit Neural representations (INRs) have emerged as a promising approach for video compression, and have achieved comparable performance to the state-of-the-art codecs such as H…
UAR-NVC: A Unified AutoRegressive Framework for Memory-Efficient Neural Video Compression
Jia Wang, Xinfeng Zhang, Gai Zhang +3
Implicit Neural Representations (INRs) have demonstrated significant potential in video compression by representing videos as neural networks. However, as the number of frames incr…
CANeRV: Content Adaptive Neural Representation for Video Compression
Lv Tang, Jun Zhu, Xinfeng Zhang +3
Recent advances in video compression introduce implicit neural representation (INR) based methods, which effectively capture global dependencies and characteristics of entire video…
Releasing the Parameter Latency of Neural Representation for High-Efficiency Video Compression
Gai Zhang, Xinfeng Zhang, Lv Tang +3
For decades, video compression technology has been a prominent research area. Traditional hybrid video compression framework and end-to-end frameworks continue to explore various i…
Chain of Visual Perception: Harnessing Multimodal Large Language Models for Zero-shot Camouflaged Object Detection
Lv Tang, Peng-Tao Jiang, Zhihao Shen +3
In this paper, we introduce a novel multimodal camo-perceptive framework (MMCPF) aimed at handling zero-shot Camouflaged Object Detection (COD) by leveraging the powerful capabilit…
Scene Matters: Model-based Deep Video Compression
Lv Tang, Xinfeng Zhang, Gai Zhang +1
Video compression has always been a popular research area, where many traditional and deep video compression methods have been proposed. These methods typically rely on signal pred…