Adversarial Multi-scale Feature Learning for Person Re-identification
arXiv:2012.14061
Abstract
Person Re-identification (Person ReID) is an important topic in intelligent surveillance and computer vision. It aims to accurately measure visual similarities between person images for determining whether two images correspond to the same person. The key to accurately measure visual similarities is learning discriminative features, which not only captures clues from different spatial scales, but also jointly inferences on multiple scales, with the ability to determine reliability and ID-relativity of each clue. To achieve these goals, we propose to improve Person ReID system performance from two perspective: \textbf{1).} Multi-scale feature learning (MSFL), which consists of Cross-scale information propagation (CSIP) and Multi-scale feature fusion (MSFF), to dynamically fuse features cross different scales.\textbf{2).} Multi-scale gradient regularizor (MSGR), to emphasize ID-related factors and ignore irrelevant factors in an adversarial manner. Combining MSFL and MSGR, our method achieves the state-of-the-art performance on four commonly used person-ReID datasets with neglectable test-time computation overhead.
References in corpus (7)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
- Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
- Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
- Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
- Omni-Scale Feature Learning for Person Re-Identification
- Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering