Object extent pooling for weakly supervised single-shot localization
| Autor: | Nicolai van Rosmalen, Marco Loog, Jan C. van Gemert, Amogh Gudi |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Class (computer programming) business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Detector Pooling Computer Science - Computer Vision and Pattern Recognition Inference Pattern recognition 02 engineering and technology Object (computer science) Face (geometry) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pyramid (image processing) Artificial intelligence Layer (object-oriented design) business |
| Zdroj: | British Machine Vision Conference 2017, BMVC 2017 BMVC |
| Popis: | In the face of scarcity in detailed training annotations, the ability to perform object localization tasks in real-time with weak-supervision is very valuable. However, the computational cost of generating and evaluating region proposals is heavy. We adapt the concept of Class Activation Maps (CAM) into the very first weakly-supervised 'single-shot' detector that does not require the use of region proposals. To facilitate this, we propose a novel global pooling technique called Spatial Pyramid Averaged Max (SPAM) pooling for training this CAM-based network for object extent localisation with only weak image-level supervision. We show this global pooling layer possesses a near ideal flow of gradients for extent localization, that offers a good trade-off between the extremes of max and average pooling. Our approach only requires a single network pass and uses a fast-backprojection technique, completely omitting any region proposal steps. To the best of our knowledge, this is the first approach to do so. Due to this, we are able to perform inference in real-time at 35fps, which is an order of magnitude faster than all previous weakly supervised object localization frameworks. In British Machine Vision Conference 2017 (BMVC'17) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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