Pay attention to the activations: a modular attention mechanism for fine-grained image recognition
| Autor: | F. Xavier Roca Marva, Diego Velazquez Dorta, Guillem Cucurull Preixens, Pau Rodríguez López, Jordi Gonzàlez Sabaté, Josep M. Gonfaus |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
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FOS: Computer and information sciences Computer Science - Machine Learning Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Image (mathematics) Machine Learning (cs.LG) Discriminative model 0202 electrical engineering electronic engineering information engineering Media Technology Feature (machine learning) FOS: Electrical engineering electronic engineering information engineering Computer vision Electrical and Electronic Engineering Artificial neural network business.industry Image and Video Processing (eess.IV) Modular design Electrical Engineering and Systems Science - Image and Video Processing Computer Science Applications Task (computing) Signal Processing Clutter 020201 artificial intelligence & image processing Artificial intelligence business |
| Popis: | Fine-grained image recognition is central to many multimedia tasks such as search, retrieval and captioning. Unfortunately, these tasks are still challenging since the appearance of samples of the same class can be more different than those from different classes. Attention has been typically implemented in neural networks by selecting the most informative regions of the image that improve classification. In contrast, in this paper, attention is not applied at the image level but to the convolutional feature activations. In essence, with our approach, the neural model learns to attend to lower-level feature activations without requiring part annotations and uses those activations to update and rectify the output likelihood distribution. The proposed mechanism is modular, architecture-independent and efficient in terms of both parameters and computation required. Experiments demonstrate that well-known networks such as Wide Residual Networks and ResNeXt, when augmented with our approach, systematically improve their classification accuracy and become more robust to changes in deformation and pose and to the presence of clutter. As a result, our proposal reaches state-of-the-art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford Dogs, and UEC-Food100 while obtaining competitive performance in ImageNet, CIFAR-100, CUB200 Birds, and Stanford Cars. In addition, we analyze the different components of our model, showing that the proposed attention modules succeed in finding the most discriminative regions of the image. Finally, as a proof of concept, we demonstrate that with only local predictions, an augmented neural network can successfully classify an image before reaching any fully connected layer, thus reducing the computational amount up to 10%. IEEE Transactions on Multimedia, ECCV extension |
| Databáze: | OpenAIRE |
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