activity
20192026
most citedSelf-PU: Self Boosted and Calibrated Positive-Unlabeled Training

29 citations · 71 across the 10 of their papers we have counts for

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7 papers · 1 filter

cs.CV2024★ 2 cited

AGG: Amortized Generative 3D Gaussians for Single Image to 3D

Dejia Xu, Ye Yuan, Morteza Mardani +4

Given the growing need for automatic 3D content creation pipelines, various 3D representations have been studied to generate 3D objects from a single image. Due to its superior ren…

cs.CV2021

Font Completion and Manipulation by Cycling Between Multi-Modality Representations

Ye Yuan, Wuyang Chen, Zhaowen Wang +4

Generating font glyphs of consistent style from one or a few reference glyphs, i.e., font completion, is an important task in topographical design. As the problem is more well-defi…

cs.CV2020★ 14 cited

AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation

Xinyu Gong, Wuyang Chen, Yifan Jiang +5

We present AutoPose, a novel neural architecture search(NAS) framework that is capable of automatically discovering multiple parallel branches of cross-scale connections towards ac…

cs.CV2019★ 9 cited

In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation

Ye Yuan, Wuyang Chen, Yang Yang +1

The comparative losses (typically, triplet loss) are appealing choices for learning person re-identification (ReID) features. However, the triplet loss is computationally much more…

cs.CV2019★ 1 cited

Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification

Ye Yuan, Wuyang Chen, Tianlong Chen +4

Many real-world applications, such as city-scale traffic monitoring and control, requires large-scale re-identification. However, previous ReID methods often failed to address two…

cs.CV2019

ABD-Net: Attentive but Diverse Person Re-Identification

Tianlong Chen, Shaojin Ding, Jingyi Xie +5

Attention mechanism has been shown to be effective for person re-identification (Re-ID). However, the learned attentive feature embeddings which are often not naturally diverse nor…