28 citations · 49 across the 18 of their papers we have counts for
5 papers · 1 filter
SDGIC: A Semantic Disambiguation-Guided Generative Image Compression Method for Ultra-Low Bitrates
Kaile Wang, Lijun He, Haisheng Fu +2
Generative image compression has recently shown impressive perceptual quality, but often suffers from semantic inconsistency at ultra-low bitrates (bpp < 0.05), limiting its reliab…
LSTC-MDA: A Unified Framework for Long-Short Term Temporal Convolution and Mixed Data Augmentation in Skeleton-Based Action Recognition
Feng Ding, Haisheng Fu, Soroush Oraki +1
Skeleton-based action recognition faces two longstanding challenges: the scarcity of labeled training samples and difficulty modeling short- and long-range temporal dependencies. T…
3DM-WeConvene: Learned Image Compression with 3D Multi-Level Wavelet-Domain Convolution and Entropy Model
Haisheng Fu, Jie Liang, Feng Liang +3
Learned image compression (LIC) has recently made significant progress, surpassing traditional methods. However, most LIC approaches operate mainly in the spatial domain and lack m…
SELIC: Semantic-Enhanced Learned Image Compression via High-Level Textual Guidance
Haisheng Fu, Jie Liang, Zhenman Fang +1
Learned image compression (LIC) techniques have achieved remarkable progress; however, effectively integrating high-level semantic information remains challenging. In this work, we…
FEDS: Feature and Entropy-Based Distillation Strategy for Efficient Learned Image Compression
Haisheng Fu, Jie Liang, Zhenman Fang +1
Learned image compression (LIC) methods have recently outperformed traditional codecs such as VVC in rate-distortion performance. However, their large models and high computational…