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Ranked List Loss for Deep Metric Learning
The objective of deep metric learning (DML) is to learn embeddings that ...
03/08/2019 ∙ by Xinshao Wang, et al. ∙
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Instance Cross Entropy for Deep Metric Learning
Loss functions play a crucial role in deep metric learning thus a variet...
11/22/2019 ∙ by Xinshao Wang, et al. ∙
20
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ID-aware Quality for Set-based Person Re-identification
Set-based person re-identification (SReID) is a matching problem that ai...
11/20/2019 ∙ by Xinshao Wang, et al. ∙
8
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ProSelfLC: Progressive Self Label Correction for Target Revising in Label Noise
In this work, we address robust deep learning under label noise (semi-su...
05/07/2020 ∙ by Xinshao Wang, et al. ∙
2
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Dictionary Integration using 3D Morphable Face Models for Pose-invariant Collaborative-representation-based Classification
The paper presents a dictionary integration algorithm using 3D morphable...
11/01/2016 ∙ by Xiaoning Song, et al. ∙
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Deep Metric Learning by Online Soft Mining and Class-Aware Attention
Deep metric learning aims to learn a deep embedding that can capture the...
11/04/2018 ∙ by Xinshao Wang, et al. ∙
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Filtering Point Targets via Online Learning of Motion Models
Filtering point targets in highly cluttered and noisy data frames can be...
02/20/2019 ∙ by Mehryar Emambakhsh, et al. ∙
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Anyvision Group
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New York
,
United States
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0
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AnyVision created a world-leading face recognition algorithm capable of handling large number of faces in real life scenarios. Among these are the following scenarios: Airports – Extract, analyze and identify people in a crowded airport environment. Safe Cities – Track suspect's route throughout multiple cameras in the city. Borders – Receive real-time alerts when known POI's approach or try to cross a border.
