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Zhijie Deng

Shanghai Jiao Tong University

24 papers hereh-index 161.2k citations36 works total

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

author position
  • first author14
  • middle author7
  • last author3

Across the 24 of 24 papers where every author was matched, so the position is known.

fields
  • cs.LG18
  • cs.CV4
  • cs.CR2
affiliations
  • Shanghai Jiao Tong University
HomepageORCID 0000-0002-0932-1631
same name
  • Zhijie Deng — 19 papers, h 13
  • Zhijie Deng — 11 papers, h 4
  • Zhijie Deng — 9 papers, h 4
  • Zhijie Deng — 9 papers, h 3
  • Zhijie Deng — 6 papers, h 4
  • Zhijie Deng — 5 papers, h 3

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
20172026
most citedBatch Virtual Adversarial Training for Graph Convolutional Networks

48 citations · 181 across the 24 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

Think-Then-Generate: Reasoning-Aware Text-to-Image Diffusion with LLM Encoders

Siqi Kou, Jiachun Jin, Zetong Zhou +8

Recent progress in text-to-image (T2I) diffusion models (DMs) has enabled high-quality visual synthesis from diverse textual prompts. Yet, most existing T2I DMs, even those equippe…

cs.CV2024

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models

Peng Cui, Guande He, Dan Zhang +3

Datasets collected from the open world unavoidably suffer from various forms of randomness or noiseness, leading to the ubiquity of aleatoric (data) uncertainty. Quantifying such u…

cs.CV2023

Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation

Zhijie Deng, Yucen Luo

Unsupervised semantic segmentation is a long-standing challenge in computer vision with great significance. Spectral clustering is a theoretically grounded solution to it where the…

cs.CV2019★ 21 cited

Cluster Alignment with a Teacher for Unsupervised Domain Adaptation

Zhijie Deng, Yucen Luo, Jun Zhu

Deep learning methods have shown promise in unsupervised domain adaptation, which aims to leverage a labeled source domain to learn a classifier for the unlabeled target domain wit…

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