Reading-strategy Inspired Visual Representation Learning for Text-to-Video Retrieval
Zhejiang Gongshang University（浙江工商大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Huazhong University of Science and Technology（华中科技大学）；Renmin University of China（中国人民大学）
This paper aims for the task of text-to-video retrieval, where given a query in the form of a natural-language sentence, it is asked to retrieve videos which are semantically relevant to the given query, from a great number of unlabeled videos. The success of this task depends on cross-modal representation learning that projects both videos and sentences into common spaces for semantic similarity computation. In this work, we concentrate on video representation learning, an essential component for text-to-video retrieval. Inspired by the reading strategy of humans, we propose a Reading-strategy Inspired Visual Representation Learning (RIVRL) to represent videos, which consists of two branches: a previewing branch and an intensive-reading branch. The previewing branch is designed to briefly capture the overview information of videos, while the intensive-reading branch is designed to obtain more in-depth information. Moreover, the intensive-reading branch is aware of the video overview captured by the previewing branch. Such holistic information is found to be useful for the intensive-reading branch to extract more fine-grained features. Extensive experiments on three datasets are conducted, where our model RIVRL achieves a new state-of-the-art on TGIF and VATEX. Moreover, on MSR-VTT, our model using two video features shows comparable performance to the state-of-the-art using seven video features and even outperforms models pre-trained on the large-scale HowTo100M dataset.
A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension
University of Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）；Kuaishou Technology（快手科技）；Huazhong University of Science and Technology（华中科技大学）
ClinReadNet: A clinical reading-inspired network for low-dose abdominal CT image quality assessment
School of Mathematics and Computer Sciences, Nanchang University（南昌大学数学与计算机科学学院）；School of Information Engineering, Nanchang University（南昌大学信息工程学院）；Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University（中山纪念医院放射科，中山大学）；Department of Stomatology, Zhujiang Hospital, Southern Medical University（南方医科大学珠江医院口腔科）；Department of Biological Sciences, School of Science, Xi'an Jiaotong Liverpool University（西安交通大学利物浦大学科学学院生物科学系）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
Brain-Inspired Multimodal Spiking Neural Network for Image-Text Retrieval
Hubei Key Laboratory of Transportation Internet of Things, Wuhan University of Technology（武汉理工大学交通物联网湖北省重点实验室）；State Key Laboratory for Multimedia Information Processing, Peking University（北京大学多媒体信息处理全国重点实验室）
A pooling based scene text proposal technique for scene text reading in the wild
Sorbonne University - University Pierre and Marie CURIE（索邦大学 - 皮埃尔和玛丽·居里大学）；Image & Pervasive Access Lab（图像与普适访问实验室）；CNRS（法国国家科学研究中心）；I2R（资讯通信研究院）；Nanyang Technological University（南洋理工大学）；Tencent Co. LTD（腾讯有限公司）；Institut Mines-Telecom（矿业电信学院）
Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval
National Cancer Center Research Institute（国立癌症中心研究所）；RIKEN Center for Advanced Intelligent Project（理化学研究所先进智能项目中心）；The University of Tokyo（东京大学）；National Cancer Center Hospital（国立癌症中心医院）
Efficient Media Retrieval from Non-Cooperative Queries
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；eBay Research Labs（eBay研究实验室）
文本在人工世界中无处不在，当涉及到书名和作者姓名时，文本很容易获得。使用来自Stanford Mobile Visual Search数据集的书籍封面集的图像以及来自openlibrary.org的其他书籍封面和元数据，我们构建了一个大规模的书籍封面检索数据集，每个数据集包含10万个干扰项封面以及标题和作者字符串。由于我们的查询图像是不好的条件，干净的文本提取，我们提出了一种方法，用于提取匹配的噪声和错误的OCR读数，并匹配它对干净的作者和书名字符串在一个标准的文档查找问题设置。最后，我们演示了如何使用这种文本匹配作为一个功能，结合流行的检索功能，如VLAD使用一个简单的学习设置，以实现检索精度的显着改善，无论是VLAD或单独的文本。
End-to-End Lip Reading in Romanian with Cross-Lingual Domain Adaptation and Lateral Inhibition
University of Bucharest（布加勒斯特大学）；University Politehnica of Bucharest（布加勒斯特理工大学）；National Institute for Research and Development in Informatics - ICI Bucharest（布加勒斯特国家信息学研发研究院（ICI布加勒斯特））
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；College of Information Science and Technology and Artificial Intelligence, Nanjing Forestry University（南京林业大学信息科学技术与人工智能学院）
