Fully Convolutional Network and Region Proposal for Instance Identification with Egocentric Vision

This paper presents a novel approach for egocentric image retrieval and object detection. This approach uses fully convolutional networks (FCN) to obtain region proposals without the need for an additional component in the network and training. It is particularly suited for small datasets with low object variability. The proposed network can be trained end-to-end and produces an effective global descriptor as an image representation. Additionally, it can be built upon any type of CNN pre-trained for classification. Through multiple experiments on two egocentric image datasets taken from museum visits, we show that the descriptor obtained using our proposed network outperforms those from previous state-of-the-art approaches. It is also just as memory-efficient, making it adapted to mobile devices such as an augmented museum audio-guide.