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2020 AAAI AAAI 2020

Video Person Re-ID: Fantastic Techniques and Where to Find Them (Student Abstract)

Abstract

Abstract The ability to identify the same person from multiple camera views without the explicit use of facial recognition is receiving commercial and academic interest. The current status-quo solutions are based on attention neural models. In this paper, we propose Attention and CL loss, which is a hybrid of center and Online Soft Mining (OSM) loss added to the attention loss on top of a temporal attention-based neural network. The proposed loss function applied with bag-of-tricks for training surpasses the state of the art on the common person Re-ID datasets, MARS and PRID 2011. Our source code is publicly available on github1.

🌉 Interdisciplinary Bridge — Computer Vision and Deep Learning and Machine Learning
🐣 Hot Topic Early Bird — video recognition
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Priyank Pathak , Amir Erfan Eshratifar , Michael Gormish

Topics

Machine Learning > Core Methods > Classification Machine Learning > Learning Types > Contrastive Learning Computer Vision > Analysis > Person Re-Identification Deep Learning > Learning Types > Metric Learning Deep Learning > Techniques > Attention

Keywords

metric learning video recognition attention mechanism person re-identification convolutional neural network temporal attention center loss neural network online soft mining attention neural network
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