Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval最新文献_第10页

How to Make an Image More Memorable?: A Deep Style Transfer Approach 如何让一张照片更令人难忘?:深度风格转移方法

Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval Pub Date : 2017-04-06 DOI: 10.1145/3078971.3078986

Aliaksandr Siarohin, Gloria Zen, Cveta Majtanovic, Xavier Alameda-Pineda, E. Ricci, N. Sebe

{"title":"How to Make an Image More Memorable?: A Deep Style Transfer Approach","authors":"Aliaksandr Siarohin, Gloria Zen, Cveta Majtanovic, Xavier Alameda-Pineda, E. Ricci, N. Sebe","doi":"10.1145/3078971.3078986","DOIUrl":"https://doi.org/10.1145/3078971.3078986","url":null,"abstract":"Recent works have shown that it is possible to automatically predict intrinsic image properties like memorability. In this paper, we take a step forward addressing the question: \"Can we make an image more memorable?\". Methods for automatically increasing image memorability would have an impact in many application fields like education, gaming or advertising. Our work is inspired by the popular editing-by-applying-filters paradigm adopted in photo editing applications, like Instagram and Prisma. In this context, the problem of increasing image memorability maps to that of retrieving ``memorabilizing'' filters or style ``seeds''. Still, users generally have to go through most of the available filters before finding the desired solution, thus turning the editing process into a resource and time consuming task. In this work, we show that it is possible to automatically retrieve the best style seeds for a given image, thus remarkably reducing the number of human attempts needed to find a good match. Our approach leverages from recent advances in the field of image synthesis and adopts a deep architecture for generating a memorable picture from a given input image and a style seed. Importantly, to automatically select the best style a novel learning-based solution, also relying on deep models, is proposed. Our experimental evaluation, conducted on publicly available benchmarks, demonstrates the effectiveness of the proposed approach for generating memorable images through automatic style seed selection.","PeriodicalId":403556,"journal":{"name":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131928598","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: 29

Embedding Watermarks into Deep Neural Networks 在深度神经网络中嵌入水印

Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval Pub Date : 2017-01-15 DOI: 10.1145/3078971.3078974

Yusuke Uchida, Yuki Nagai, S. Sakazawa, S. Satoh

{"title":"Embedding Watermarks into Deep Neural Networks","authors":"Yusuke Uchida, Yuki Nagai, S. Sakazawa, S. Satoh","doi":"10.1145/3078971.3078974","DOIUrl":"https://doi.org/10.1145/3078971.3078974","url":null,"abstract":"Significant progress has been made with deep neural networks recently. Sharing trained models of deep neural networks has been a very important in the rapid progress of research and development of these systems. At the same time, it is necessary to protect the rights to shared trained models. To this end, we propose to use digital watermarking technology to protect intellectual property and detect intellectual property infringement in the use of trained models. First, we formulate a new problem: embedding watermarks into deep neural networks. Second, we propose a general framework for embedding a watermark in model parameters, using a parameter regularizer. Our approach does not impair the performance of networks into which a watermark is placed because the watermark is embedded while training the host network. Finally, we perform comprehensive experiments to reveal the potential of watermarking deep neural networks as the basis of this new research effort. We show that our framework can embed a watermark during the training of a deep neural network from scratch, and during fine-tuning and distilling, without impairing its performance. The embedded watermark does not disappear even after fine-tuning or parameter pruning; the watermark remains complete even after 65% of parameters are pruned.","PeriodicalId":403556,"journal":{"name":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130071260","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: 448

Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval 用于图像实例检索的嵌套不变性池和RBM哈希

Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval Pub Date : 2016-03-15 DOI: 10.1145/3078971.3078987

Olivier Morère, Jie Lin, A. Veillard, V. Chandrasekhar

{"title":"Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval","authors":"Olivier Morère, Jie Lin, A. Veillard, V. Chandrasekhar","doi":"10.1145/3078971.3078987","DOIUrl":"https://doi.org/10.1145/3078971.3078987","url":null,"abstract":"The goal of this work is the computation of very compact binary hashes for image instance retrieval. Our approach has two novel contributions. The first one is Nested Invariance Pooling (NIP), a method inspired from i-theory, a mathematical theory for computing group invariant transformations with feed-forward neural networks. NIP is able to produce compact and well-performing descriptors with visual representations extracted from convolutional neural networks. We specifically incorporate scale, translation and rotation invariances but the scheme can be extended to any arbitrary sets of transformations. We also show that using moments of increasing order throughout nesting is important. The NIP descriptors are then hashed to the target code size (32-256 bits) with a Restricted Boltzmann Machine with a novel batch-level regularization scheme specifically designed for the purpose of hashing (RBMH). A thorough empirical evaluation with state-of-the-art shows that the results obtained both with the NIP descriptors and the NIP+RBMH hashes are consistently outstanding across a wide range of datasets.","PeriodicalId":403556,"journal":{"name":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130436286","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: 16

Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval 2017年ACM多媒体检索国际会议论文集

Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval Pub Date : 1900-01-01 DOI: 10.1145/3078971

引用次数: 6