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Zeyue Xue

7 papers hereh-index 7480 citations9 works total

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

author position
  • first author3
  • middle author4

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.AI1
  • cs.GR1
  • eess.IV1
same name
  • Zeyue Xue — 24 papers, h 6
  • Zeyue Xue — 1 paper

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
20202024
most citedRAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths

41 citations · 49 across the 7 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024

Rethinking the Spatial Inconsistency in Classifier-Free Diffusion Guidance

Dazhong Shen, Guanglu Song, Zeyue Xue +2

Classifier-Free Guidance (CFG) has been widely used in text-to-image diffusion models, where the CFG scale is introduced to control the strength of text guidance on the whole image…

cs.CV2023★ 41 cited

RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths

Zeyue Xue, Guanglu Song, Qiushan Guo +4

Text-to-image generation has recently witnessed remarkable achievements. We introduce a text-conditional image diffusion model, termed RAPHAEL, to generate highly artistic images,…

cs.CV2023★ 1 cited

Temporal Enhanced Training of Multi-view 3D Object Detector via Historical Object Prediction

Zhuofan Zong, Dongzhi Jiang, Guanglu Song +4

In this paper, we propose a new paradigm, named Historical Object Prediction (HoP) for multi-view 3D detection to leverage temporal information more effectively. The HoP approach i…

cs.CV2022★ 2 cited

Large-batch Optimization for Dense Visual Predictions

Zeyue Xue, Jianming Liang, Guanglu Song +4

Training a large-scale deep neural network in a large-scale dataset is challenging and time-consuming. The recent breakthrough of large-batch optimization is a promising way to tac…

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