In this paper, we study the challenging problem of categorizing videos according to high-level semantics such as the existence of a particular human action or a complex event. Although extensive efforts have been devoted in recent years, most existing works combined multiple video features using simple fusion strategies and neglected the utilization of inter-class semantic relationships. This paper proposes a novel unified framework that jointly exploits the feature relationships and the class relationships for improved categorization performance. Specifically, these two types of relationships are estimated and utilized by rigorously imposing regularizations in the learning process of a deep neural network (DNN). Such a regularized DNN (rDNN) can be efficiently realized using a GPU-based implementation with an affordable training cost. Through arming the DNN with better capability of harnessing both the feature and the class relationships, the proposed rDNN is more suitable for modeling video semantics. With extensive experimental evaluations, we show that rDNN produces superior performance over several state-of-the-art approaches. On the well-known Hollywood2 and Columbia Consumer Video benchmarks, we obtain very competitive results: 66.9\% and 73.5\% respectively in terms of mean average precision. In addition, to substantially evaluate our rDNN and stimulate future research on large scale video categorization, we collect and release a new benchmark dataset, called FCVID, which contains 91,223 Internet videos and 239 manually annotated categories.
Channel Interaction Networks for Fine-Grained Image Categorization
Malong Technologies（码隆科技）；Shenzhen Malong Artificial Intelligence Research Center（深圳码隆人工智能研究中心）
细粒度图像分类由于类间的细微差异而具有挑战性，我们认为利用通道之间丰富的关系可以帮助捕捉这些差异，因为不同的通道对应不同的语义。在本文中，我们提出了一个通道交互网络（CIN），它模拟了图像内和图像之间的通道相互作用。对于单个图像，提出了自通道交互（SCI）模块来探索图像内的通道相关性。这允许模型从相关通道中学习互补特征，从而产生更强的细粒度特征。此外，给定一个图像对，我们引入了一个对比通道交互（CCI）模块，用一个度量学习框架来模拟跨样本通道交互，使CIN能够区分图像之间的细微视觉差异。我们的模型可以以端到端的方式进行有效的训练，而不需要多阶段的训练和测试。最后，在三个公开可用的基准上进行了全面的实验，其中所提出的方法始终优于最先进的方法，如DFL-CNN（Wang，Morariu和Davis 2018）和NTS（Yang et al. 2018）。
Data-free Knowledge Distillation for Fine-grained Visual Categorization
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）
Refining Image Categorization by Exploiting Web Images and General Corpus
University of Technology Sydney（悉尼科技大学）；University of Electronic Science and Technology of China（电子科技大学）；Alibaba Group（阿里巴巴集团）；SouthEast University（东南大学）；Nanjing University of Science and Technology（南京理工大学）
SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation
Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；University of Groningen（格罗宁根大学）；Imagga Technologies Ltd（Imagga技术有限公司）；Digital Spaces Living Lab（数字空间生活实验室）
CARD: Semantic Segmentation with Efficient Class-Aware Regularized Decoder
Shenzhen Institute for Advanced Study, UESTC（电子科技大学深圳高等研究院）；Tencent AI Lab（腾讯人工智能实验室）；Fujian Normal University（福建师范大学）；University of Technology Sydney（悉尼科技大学）；University of Nottingham Ningbo China（宁波诺丁汉大学）
DeepMpMRI: Tensor-decomposition Regularized Learning for Fast and High-Fidelity Multi-Parametric Microstructural MR Imaging
Paul C. Lauterbur Research Center for Biomedical Imaging（Paul C. Lauterbur生物医学成像研究中心）；Shenzhen Institutes of Advanced Technology（深圳先进技术研究院）；Chinese Academy of Sciences（中国科学院）；State Key Laboratory of Complex & Critical Software Environment（复杂与关键软件环境国家重点实验室）；Beihang University（北航）；School of Biomedical Engineering（生物医学工程学院）；Tsinghua University（清华大学）；School of Physics and Electronic Science（物理与电子科学学院）；Changsha University of Science and Technology（长沙理工大学）；Faculty of Computer Science and Control Engineering（计算机科学与控制工程学院）；Shenzhen University of Advanced Technology（深圳大学先进技术学院）；Department of Information Engineering（信息工程学院）；Zhejiang University of Technology（浙江工业大学）
