Abstract
We propose a novel deep convolutional neural network (CNN) architecture able to perform the integrated object recognition and localization tasks. We propose the Focused Attention (FA) objective that aims to optimize the network to learn features only from objects of interest while suppress those features from the background. As a result, the features extracted by the learned models can be used to accurately predict both the object category and the bounding box of the recognized object in the input image. Experimental results show that the proposed CNN architecture trained with the FA objective achieves better performances than original AlexNet in both the object localization and recognition tasks.
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Acknowledgments
This work is supported by National Basic Research Program of China (973 Program) under Grant No. 2015CB351705, and the National Natural Science Foundation of China (NSFC) under Grant No. 61332018.
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Tao, X., Xu, C., Gong, Y., Wang, J. (2016). A Deep CNN with Focused Attention Objective for Integrated Object Recognition and Localization. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_5
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DOI: https://doi.org/10.1007/978-3-319-48896-7_5
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