STADB: A Self-Thresholding Attention Guided ADB Network for Person Re-identification
arXiv:2007.03584
Abstract
Recently, Batch DropBlock network (BDB) has demonstrated its effectiveness on person image representation and re-identification task via feature erasing. However, BDB drops the features \textbf{randomly} which may lead to sub-optimal results. In this paper, we propose a novel Self-Thresholding attention guided Adaptive DropBlock network (STADB) for person re-ID which can \textbf{adaptively} erase the most discriminative regions. Specifically, STADB first obtains an attention map by channel-wise pooling and returns a drop mask by thresholding the attention map. Then, the input features and self-thresholding attention guided drop mask are multiplied to generate the dropped feature maps. In addition, STADB utilizes the spatial and channel attention to learn a better feature map and iteratively trains the feature dropping module for person re-ID. Experiments on several benchmark datasets demonstrate that the proposed STADB outperforms many other related methods for person re-ID. The source code of this paper is released at: \textcolor{red}{\url{https://github.com/wangxiao5791509/STADB_ReID}}.
References in corpus (8)
- GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval
- What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification
- Semantic-guided Pixel Sampling for Cloth-Changing Person Re-identification
- Fracking Deep Convolutional Image Descriptors
- Rotate to Attend: Convolutional Triplet Attention Module
- Identity-Guided Human Semantic Parsing for Person Re-Identification
- Pedestrian Attribute Recognition: A Survey
- Improved Hard Example Mining by Discovering Attribute-based Hard Person Identity