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researcher

Di Hu

5 papers here

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

author position
  • first author1
  • middle author1
  • last author3

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.SD1
ORCID 0000-0002-7118-6733
same name
  • Di Hu — 14 papers, h 15
  • Di Hu — 9 papers, h 4
  • Di Hu — 9 papers, h 6
  • Di Hu — 9 papers, h 5
  • Di Hu — 8 papers, h 6
  • Di Hu — 7 papers, h 12

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
20212023
most citedLearning in Audio-visual Context: A Review, Analysis, and New Perspective

32 citations · 43 across the 5 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2023★ 5 cited

Revisiting Pre-training in Audio-Visual Learning

Ruoxuan Feng, Wenke Xia, Di Hu

Pre-training technique has gained tremendous success in enhancing model performance on various tasks, but found to perform worse than training from scratch in some uni-modal situat…

cs.CV2022★ 32 cited

Learning in Audio-visual Context: A Review, Analysis, and New Perspective

Yake Wei, Di Hu, Yapeng Tian +1

Sight and hearing are two senses that play a vital role in human communication and scene understanding. To mimic human perception ability, audio-visual learning, aimed at developin…

cs.CV2022★ 1 cited

Dual Domain-Adversarial Learning for Audio-Visual Saliency Prediction

Yingzi Fan, Longfei Han, Yue Zhang +3

Both visual and auditory information are valuable to determine the salient regions in videos. Deep convolution neural networks (CNN) showcase strong capacity in coping with the aud…

cs.CV2021★ 3 cited

Class-aware Sounding Objects Localization via Audiovisual Correspondence

Di Hu, Yake Wei, Rui Qian +3

Audiovisual scenes are pervasive in our daily life. It is commonplace for humans to discriminatively localize different sounding objects but quite challenging for machines to achie…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.