Search Results for author: Yuhan Zhang

Found 6 papers, 3 papers with code

Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales

1 code implementation CVPR 2021 Yifan Sun, Yuke Zhu, Yuhan Zhang, Pengkun Zheng, Xi Qiu, Chi Zhang, Yichen Wei

%We argue that such flexibility is also important for deep metric learning, because different visual concepts indeed correspond to different semantic scales.

Metric Learning

IPN-V2 and OCTA-500: Methodology and Dataset for Retinal Image Segmentation

1 code implementation14 Dec 2020 Mingchao Li, Yuhan Zhang, Zexuan Ji, Keren Xie, Songtao Yuan, Qinghuai Liu, Qiang Chen

In this work, we propose image projection network V2 (IPN-V2), extending IPN by adding a plane perceptron to enhance the perceptron ability in the horizontal direction.

Semantic Segmentation

Meta-Learning for Neural Relation Classification with Distant Supervision

no code implementations26 Oct 2020 Zhenzhen Li, Jian-Yun Nie, Benyou Wang, Pan Du, Yuhan Zhang, Lixin Zou, Dongsheng Li

Distant supervision provides a means to create a large number of weakly labeled data at low cost for relation classification.

Classification General Classification +2

Modeling the US-China trade conflict: a utility theory approach

no code implementations23 Oct 2020 Yuhan Zhang, Cheng Chang

This paper models the US-China trade conflict and attempts to analyze the (optimal) strategic choices.

A Real-time Contribution Measurement Method for Participants in Federated Learning

no code implementations28 Sep 2020 Bingjie Yan, Yize Zhou, Boyi Liu, Jun Wang, Yuhan Zhang, Li Liu, Xiaolan Nie, Zhiwei Fan, Zhixuan Liang

However, there is a lack of a sufficiently reasonable contribution measurement mechanism to distribute the reward for each agent.

Federated Learning

Circle Loss: A Unified Perspective of Pair Similarity Optimization

10 code implementations CVPR 2020 Yifan Sun, Changmao Cheng, Yuhan Zhang, Chi Zhang, Liang Zheng, Zhongdao Wang, Yichen Wei

This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.

 Ranked #1 on Face Verification on IJB-C (training dataset metric)

Face Recognition Face Verification +3

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