Blue Attraction

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Perceptual Generative Adversarial Networks for Small Object Detection

Detecting small objects is notoriously challenging due to their low resolution and noisy representation. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. However, the performance gain of such ad hoc architectures is usually limited to pay off the computational cost. In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection. For this purpose, we propose a new Perceptual Generative Adversarial Network (Perceptual GAN) model that improves small object detection through narrowing representation difference of small objects from the large ones. Specifically, its generator learns to transfer perceived poor representations of the small objects to super-resolved ones that are similar enough to real large objects to fool a competing discriminator. Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator. Extensive evaluations on the challenging Tsinghua-Tencent 100K and the Caltech benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects, including traffic signs and pedestrians, over well-established state-of-the-arts

Integrated Face Analytics Networks through Cross-Dataset Hybrid Training

Face analytics benefits many multimedia applications. It consists of a number of tasks, such as facial emotion recognition and face parsing, and most existing approaches generally treat these tasks independently, which limits their deployment in real scenarios. In this paper we propose an integrated Face Analytics Network (iFAN), which is able to perform multiple tasks jointly for face analytics with a novel carefully designed network architecture to fully facilitate the informative interaction among different tasks. The proposed integrated network explicitly models the interactions between tasks so that the correlations between tasks can be fully exploited for performance boost. In addition, to solve the bottleneck of the absence of datasets with comprehensive training data for various tasks, we propose a novel cross-dataset hybrid training strategy. It allows "plug-in and play" of multiple datasets annotated for different tasks without the requirement of a fully labeled common dataset for all the tasks. We experimentally show that the proposed iFAN achieves state-of-the-art performance on multiple face analytics tasks using a single integrated model. Specifically, iFAN achieves an overall F-score of 91.15% on the Helen dataset for face parsing, a normalized mean error of 5.81% on the MTFL dataset for facial landmark localization and an accuracy of 45.73% on the BNU dataset for emotion recognition with a single model.Comment: 10 page

Spectrum of topics for world congresses and other activities of the International Society for Physical and Rehabilitation Medicine (ISPRM) : a first proposal

Background: One of the objectives of the International Society for Physical and Rehabilitation Medicine is to improve the continuity of World Congresses. This requires the development of an abstract topic list for use in congress announcements and abstract submissions. Methods: An abstract topic list was developed on the basis of the definitions of human functioning and rehabilitation research, which define 5 main areas of research (biosciences in rehabilitation, biomedical rehabilitation sciences and engineering, clinical Physical and Rehabilitation Medicine (PRM) sciences, integrative rehabilitation sciences, and human functioning sciences). For the abstract topic list, these research areas were grouped according to the proposals of congress streams. In a second step, the first version of the list was systematically compared with the topics of the 2003 ISPRM World Congress. Results: The resulting comprehensive abstract topic list contains 5 chapters according to the definition of human functioning and rehabilitation research. Due to the high significance of clinical research, clinical PRM sciences were placed at the top of the list, comprising all relevant health conditions treated in PRM services. For congress announcements a short topic list was derived. Discussion: The ISPRM topic list is sustainable and covers a full range of topics. It may be useful for congresses and elsewhere in structuring research in PRM