Recurrent Attention for Deep Neural Object Detection
| Autor: | Anastasios Tefas, Georgios Symeonidis |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Class (computer programming)
Machine translation Computer science business.industry Deep learning Cognitive neuroscience of visual object recognition Context (language use) 02 engineering and technology Optical character recognition computer.software_genre Machine learning Convolutional neural network Object detection 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | SETN |
| DOI: | 10.1145/3200947.3201024 |
| Popis: | Recent advances in deep learning have achieved state-of-the-art results for object detection by replacing the traditional detection methodologies with deep convolutional neural network architectures. A contemporary technique that is shown to further improve the performance of these models on tasks ranging from optical character recognition and neural machine translation to object detection is based on incorporating an attention mechanism within the models. The idea behind the attention mechanism and its variations was to improve the information quality extracted for any confronted task by focusing on the most relevant parts of the input. In this paper we propose two novel deep neural architectures for object recognition that incorporate the idea of the attention mechanism in the well-known faster-RCNN object detector. The objective is to develop attention mechanisms that can be used for small objects detection as they appear when using Drones for covering sport events like bicycle races, football matches and rowing races. The proposed approaches include a class agnostic method that applies the same predetermined context for every class, and a class specific method which learns to include context that maximizes the class's precision individually for each class. The proposed methods are evaluated in the VOC2007 dataset, improving the performance of the baseline faster-RCNN architecture. |
| Databáze: | OpenAIRE |
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