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20152023
most citedLearning Scale-Free Networks by Dynamic Node-Specific Degree Prior

13 citations · 33 across the 11 of their papers we have counts for

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6 papers · 1 filter

cs.CV2023

Threshold-Consistent Margin Loss for Open-World Deep Metric Learning

Qin Zhang, Linghan Xu, Qingming Tang +4

Existing losses used in deep metric learning (DML) for image retrieval often lead to highly non-uniform intra-class and inter-class representation structures across test classes an…

cs.CV2023

Learning for Transductive Threshold Calibration in Open-World Recognition

Qin Zhang, Dongsheng An, Tianjun Xiao +6

In deep metric learning for visual recognition, the calibration of distance thresholds is crucial for achieving desired model performance in the true positive rates (TPR) or true n…

cs.CV20194 cited

Towards Disentangled Representations for Human Retargeting by Multi-view Learning

Chao Yang, Xiaofeng Liu, Qingming Tang +1

We study the problem of learning disentangled representations for data across multiple domains and its applications in human retargeting. Our goal is to map an input image to an id…

cs.CV2019

Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets

Xiaofeng Liu, B. V. K Vijaya Kumar, Chao Yang +2

This paper targets the problem of image set-based face verification and identification. Unlike traditional single media (an image or video) setting, we encounter a set of heterogen…

cs.CV2018

Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart

Chao Yang, Yuhang Song, Xiaofeng Liu +2

Recent advances in deep generative models have shown promising potential in image inpanting, which refers to the task of predicting missing pixel values of an incomplete image usin…

cs.CV20175 cited

Acoustic Feature Learning via Deep Variational Canonical Correlation Analysis

Qingming Tang, Weiran Wang, Karen Livescu

We study the problem of acoustic feature learning in the setting where we have access to another (non-acoustic) modality for feature learning but not at test time. We use deep vari…