◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

H. Fu

9 papers hereh-index 11443 citations37 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author4

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • eess.IV3
  • stat.AP2
same name
  • H. Fu — 8 papers, h 15
  • H. Fu — 7 papers, h 4
  • H. Fu — 3 papers, h 25
  • H. Fu — 3 papers, h 1
  • H. Fu — 3 papers, h 32
  • H. Fu — 1 paper, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.