Deep multi context Network for FINE GRAINED VISUAL RECOGNITION
In this paper, we tackle the FINE GRAINED VISUAL RECOGNITION problem by proposing a deep multi context framework. We employ deep Convolutional Neural Networks to model features of objects in images. Global context and local context are both taken into consideration, and are jointly modeled in a unified multi context deep learning framework. To cleanse the relatively dirty data for training, a regional proposal method is designed to make the multi context modeling suited for fine grained visual recognition in the real world. Furthermore, recently proposed contemporary deep models are used, and their combination is investigated. Our approaches are evaluated on MSR IRC 2016 and further assessed on the more complex validation set. The results show significant and consistent improvements over the baseline.
2016 IEEE International Conference on Multimedia Expo Workshops (ICMEW)