Angular Margin-Mining Softmax Loss for Face Recognition
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3168310
Abstract: Face recognition methods have been significantly improved in recent years owing to the advances made in loss functions. Typically, loss functions are designed to enhance the separability power by concentrating on hard samples in mining-based… read more here.
Keywords: loss; softmax loss; margin; hard samples ... See more keywords
Mining Hard Samples Globally and Efficiently for Person Reidentification
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DOI: 10.1109/jiot.2020.2980549
Abstract: Person reidentification (ReID) is an important application of Internet of Things (IoT). ReID recognizes pedestrians across camera views at different locations and time, which is usually treated as a ranking task. An essential part of… read more here.
Keywords: mining; sample mining; person reidentification; hard samples ... See more keywords
OTFace: Hard Samples Guided Optimal Transport Loss for Deep Face Representation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Multimedia"
DOI: 10.1109/tmm.2022.3230331
Abstract: Face representation in the wild is extremely hard due to the large scale face variations. Some deep convolutional neural networks (CNNs) have been developed to learn discriminative feature by designing properly margin-based losses, which perform… read more here.
Keywords: samples guided; face; feature; face representation ... See more keywords