Deep Residual Network with Self Attention Improves Person Re-Identification Accuracy
| Autor: | Guisong Liu, Jean-Paul Ainam, Guangchun Luo, Ke Qin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Pattern recognition 02 engineering and technology Filter (signal processing) 010501 environmental sciences Grid 01 natural sciences Image (mathematics) Discriminative model Feature (computer vision) Margin (machine learning) Softmax function 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Representation (mathematics) business 0105 earth and related environmental sciences |
| Zdroj: | ICMLC |
| DOI: | 10.1145/3318299.3318324 |
| Popis: | In this paper, we present an attention mechanism scheme to improve the person re-identification task. Inspired by biology, we propose Self Attention Grid (SAG) to discover the most informative parts from a high-resolution image using its internal representation. In particular, given an input image, the proposed model is fed with two copies of the same image and consists of two branches. The upper branch processes the high-resolution image and learns high dimensional feature representation while the lower branch processes the low-resolution image and learns a filtering attention grid. We apply a max filter operation to non-overlapping sub-regions on the high feature representation before element-wise multiplied with the output of the second branch. The feature maps of the second branch are subsequently weighted to reflect the importance of each patch of the grid using a softmax operation. Our attention module helps the network to learn the most discriminative visual features of multiple image regions and is specifically optimized to attend feature representation at different levels. Extensive experiments on three large-scale datasets show that our self-attention mechanism significantly improves the baseline model and outperforms various state-of-art models by a large margin. |
| Databáze: | OpenAIRE |
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