The egocentric video provides a unique view of event participants to show their attention, vision, and interaction with objects. In this paper, we introduce Ego-Deliver, a new large-scale egocentric video benchmark recorded by takeaway riders about their daily work. To the best of our knowledge, Ego-Deliver presents the first attempt in understanding activities from the takeaway delivery process while being one of the largest egocentric video action datasets to date. Our dataset provides a total of 5,360 videos with more than 139,000 multi-track annotations and 45 different attributes, which we believe is pivotal to future research in this area. We introduce the FS-Net architecture, a new anchor-free action detection approach handling extreme variations of action durations. We partition videos into fragments and build dynamic graphs over fragments, where multi-fragment context information is aggregated to boost fragment classification. A splicing and scoring module is applied to obtain final action proposals. Our experimental evaluation confirms that the proposed framework outperforms existing approaches on the proposed Ego-Deliver benchmark and is competitive on other popular benchmarks. In our current version, Ego-Deliver is used to make a comprehensive comparison between algorithms for activity detection. We also show its application to action recognition with promising results. The dataset, toolkits and baseline results will be made available at: https://egodeliver.github.io/EgoDeliver_Dataset/

This paper introduces a novel rotation-based framework for arbitrary-oriented text detection in natural scene images. We present theRotation Region Proposal Networks, which are designed to generate inclined proposals with text orientation angle information. The angle information is then adapted for bounding box regression to make the proposals more accurately fit into the text region in terms of the orientation. TheRotation Region-of-Interestpooling layer is proposed to project arbitrary-oriented proposals to a feature map for a text region classifier. The whole framework is built upon a region-proposal-based architecture, which ensures the computational efficiency of the arbitrary-oriented text detection compared with previous text detection systems. We conduct experiments using the rotation-based framework on three real-world scene text detection datasets and demonstrate its superiority in terms of effectiveness and efficiency over previous approaches.

In this paper, we introduce an active annotation and learning framework for the face recognition task. Starting with an initial label deficient face image training set, we iteratively train a deep neural network and use this model to choose the examples for further manual annotation. We follow the active learning strategy and derive the Value of Information criterion to actively select candidate annotation images. During these iterations, the deep neural network is incrementally updated. Experimental results conducted on LFW benchmark and MS-Celeb-1M challenge demonstrate the effectiveness of our proposed framework.