Recurrent Scale Approximation for Object Detection in CNN
| Autor: | Xiaogang Wang, Junjie Yan, Hongyang Li, Fangyin Wei, Xiaoou Tang, Yu Liu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Artificial neural network Scale (ratio) Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Feature extraction Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences Convolutional neural network Object detection Feature (computer vision) Pyramid 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pyramid (image processing) Artificial intelligence Face detection business Algorithm 0105 earth and related environmental sciences |
| Zdroj: | ICCV |
| Popis: | Since convolutional neural network (CNN) lacks an inherent mechanism to handle large scale variations, we always need to compute feature maps multiple times for multi-scale object detection, which has the bottleneck of computational cost in practice. To address this, we devise a recurrent scale approximation (RSA) to compute feature map once only, and only through this map can we approximate the rest maps on other levels. At the core of RSA is the recursive rolling out mechanism: given an initial map at a particular scale, it generates the prediction at a smaller scale that is half the size of input. To further increase efficiency and accuracy, we (a): design a scale-forecast network to globally predict potential scales in the image since there is no need to compute maps on all levels of the pyramid. (b): propose a landmark retracing network (LRN) to trace back locations of the regressed landmarks and generate a confidence score for each landmark; LRN can effectively alleviate false positives caused by the accumulated error in RSA. The whole system can be trained end-to-end in a unified CNN framework. Experiments demonstrate that our proposed algorithm is superior against state-of-the-art methods on face detection benchmarks and achieves comparable results for generic proposal generation. The source code of RSA is available at github.com/sciencefans/RSA-for-object-detection. Accepted in ICCV 2017 |
| Databáze: | OpenAIRE |
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