Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval最新文献_第8页

Session details: Oral Session 3: Multimedia Applications 会议详情:口头会议3:多媒体应用

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3252928

Wolfgang Hürst

引用次数: 0

Optimally Grouped Deep Features Using Normalized Cost for Video Scene Detection 基于归一化代价的深度特征最佳分组视频场景检测

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3206055

Daniel Rotman, Dror Porat, G. Ashour, Udi Barzelay

{"title":"Optimally Grouped Deep Features Using Normalized Cost for Video Scene Detection","authors":"Daniel Rotman, Dror Porat, G. Ashour, Udi Barzelay","doi":"10.1145/3206025.3206055","DOIUrl":"https://doi.org/10.1145/3206025.3206055","url":null,"abstract":"Video scene detection is the task of temporally dividing a video into its semantic sections. This is an important preliminary step for effective analysis of heterogeneous video content. We present a unique formulation of this task as a generic optimization problem with a novel normalized cost function, aimed at optimal grouping of consecutive shots into scenes. The mathematical properties of the proposed normalized cost function enable robust scene detection, also in challenging real-world scenarios. We present a novel dynamic programming formulation for efficiently optimizing the proposed cost function despite an inherent dependency between subproblems. We use deep neural network models for visual and audio analysis to encode the semantic elements in the video scene, enabling effective and more accurate video scene detection. The proposed method has two key advantages compared to other approaches: it inherently provides a temporally consistent division of the video into scenes, and is also parameter-free, eliminating the need for fine-tuning for different types of content. While our method can adaptively estimate the number of scenes from the video content, we also present a new non-greedy procedure for creating a hierarchical consensus-based division tree spanning multiple levels of granularity. We provide comprehensive experimental results showing the benefits of the normalized cost function, and demonstrating that the proposed method outperforms the current state of the art in video scene detection.","PeriodicalId":224132,"journal":{"name":"Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131411070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

Session details: Special Session 1: Predicting User Perceptions of Multimedia Content 专题会议1:预测用户对多媒体内容的感知

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3252931

C. Demarty

引用次数: 0

A Multi-Oriented Scene Text Detector with Position-Sensitive Segmentation 基于位置敏感分割的多方向场景文本检测器

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3206043

Peirui Cheng, Weiqiang Wang

{"title":"A Multi-Oriented Scene Text Detector with Position-Sensitive Segmentation","authors":"Peirui Cheng, Weiqiang Wang","doi":"10.1145/3206025.3206043","DOIUrl":"https://doi.org/10.1145/3206025.3206043","url":null,"abstract":"Scene text detection has been studied for a long time and lots of approaches have achieved promising performances. Most approaches regard text as a specific object and utilize the popular frameworks of object detection to detect scene text. However, scene text is different from general objects in terms of orientations, sizes and aspect ratios. In this paper, we present an end-to-end multi-oriented scene text detection approach, which combines the object detection framework with the position-sensitive segmentation. For a given image, features are extracted through a fully convolutional network. Then they are input into text detection branch and position-sensitive segmentation branch simultaneously, where text detection branch is used for generating candidates and position-sensitive segmentation branch is used for generating segmentation maps. Finally the candidates generated by text detection branch are projected onto the position-sensitive segmentation maps for filtering. The proposed approach utilizes the merits of position-sensitive segmentation to improve the expressiveness of the proposed network. Additionally, the approach uses position-sensitive segmentation maps to further filter the candidates so as to highly improve the precision rate. Experiments on datasets ICDAR2015 and COCO-Text demonstrate that the proposed method outperforms previous state-of-the-art methods. For ICDAR2015 dataset, the proposed method achieves an F-score of 0.83 and a precision rate of 0.87.","PeriodicalId":224132,"journal":{"name":"Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116183745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

WTPlant (What's That Plant?): A Deep Learning System for Identifying Plants in Natural Images WTPlant(那是什么植物?):一个用于识别自然图像中的植物的深度学习系统

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3206089

Jonas Krause, Gavin Sugita, K. Baek, Lipyeow Lim

{"title":"WTPlant (What's That Plant?): A Deep Learning System for Identifying Plants in Natural Images","authors":"Jonas Krause, Gavin Sugita, K. Baek, Lipyeow Lim","doi":"10.1145/3206025.3206089","DOIUrl":"https://doi.org/10.1145/3206025.3206089","url":null,"abstract":"Despite the availability of dozens of plant identification mobile applications, identifying plants from a natural image remains a challenging problem - most of the existing applications do not address the complexity of natural images, the large number of plant species, and the multi-scale nature of natural images. In this technical demonstration, we present the WTPlant system for identifying plants in natural images. WTPlant is based on deep learning approaches. Specifically, it uses stacked Convolutional Neural Networks for image segmentation, a novel preprocessing stage for multi-scale analyses, and deep convolutional networks to extract the most discriminative features. WTPlant employs different classification architectures for plants and flowers, thus enabling plant identification throughout all the seasons. The user interface also shows, in an interactive way, the most representative areas in the image that are used to predict each plant species. The first version of WTPlant is trained to classify 100 different plant species present in the campus of the University of Hawai'i at Manoa. First experiments support the hypothesis that an initial segmentation process helps guide the extraction of representative samples and, consequently, enables Convolutional Neural Networks to better recognize objects of different scales in natural images. Future versions aim to extend the recognizable species to cover the land-based flora of the Hawaiian Islands.","PeriodicalId":224132,"journal":{"name":"Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115526618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Session details: Oral Session 2: Multimedia Content Analysis 会议详情:口头会议2:多媒体内容分析

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3252927

W. Chu

引用次数: 0

Class-aware Self-Attention for Audio Event Recognition 音频事件识别的类感知自关注

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3206067

Shizhe Chen, Jia Chen, Qin Jin, Alexander Hauptmann

{"title":"Class-aware Self-Attention for Audio Event Recognition","authors":"Shizhe Chen, Jia Chen, Qin Jin, Alexander Hauptmann","doi":"10.1145/3206025.3206067","DOIUrl":"https://doi.org/10.1145/3206025.3206067","url":null,"abstract":"Audio event recognition (AER) has been an important research problem with a wide range of applications. However, it is very challenging to develop large scale audio event recognition models. On the one hand, usually there are only \"weak\" labeled audio training data available, which only contains labels of audio events without temporal boundaries. On the other hand, the distribution of audio events is generally long-tailed, with only a few positive samples for large amounts of audio events. These two issues make it hard to learn discriminative acoustic features to recognize audio events especially for long-tailed events. In this paper, we propose a novel class-aware self-attention mechanism with attention factor sharing to generate discriminative clip-level features for audio event recognition. Since a target audio event only occurs in part of an entire audio clip and its corresponding temporal interval varies, the proposed class-aware self-attention approach learns to highlight relevant temporal intervals and to suppress irrelevant noises at the same time. In order to learn attention patterns effectively for those long-tailed events, we combine both the domain knowledge and data driven strategies to share attention factors in the proposed attention mechanism, which transfers the common knowledge learned from other similar events to the rare events. The proposed attention mechanism is a pluggable component and can be trained end-to-end in the overall AER model. We evaluate our model on a large-scale audio event corpus \"Audio Set\" with both short-term and long-term acoustic features. The experimental results demonstrate the effectiveness of our model, which improves the overall audio event recognition performance with different acoustic features especially for events with low resources. Moreover, the experiments also show that our proposed model is able to learn new audio events with a few training examples effectively and efficiently without disturbing the previously learned audio events.","PeriodicalId":224132,"journal":{"name":"Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132701386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Compare Stereo Patches Using Atrous Convolutional Neural Networks 比较使用卷积神经网络的立体图像

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3206075

Zhiwei Li, Lei Yu

引用次数: 3

Session details: Industrial Talks 会议详情:工业会谈

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3252924

Go Irie Tao Mei

引用次数: 0

Multimedia Content Understanding by Learning from Very Few Examples: Recent Progress on Unsupervised, Semi-Supervised and Supervised Deep Learning Approaches 从很少的例子中学习来理解多媒体内容:无监督、半监督和监督深度学习方法的最新进展

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3210498

Guo-Jun Qi

引用次数: 0