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Detecting spliced images is one of the emerging challenges in computer vision.
Learning rich features for image manipulation detection
Peng Zhou, Xintong Han, Vlad I Morariu, and Larry S Davis · 1905
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Mean shift, mode seeking, and clustering
Yizong Cheng · 1995
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Image retrieval: Current techniques, promising directions, and open issues
Yong Rui, Thomas S Huang, and Shih-Fu Chang · 1999
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Video google: A text retrieval approach to object matching in videos
Josef Sivic and Andrew Zisserman · 2003
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Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
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Histograms of oriented gradients for human detection
Navneet Dalal and Bill Triggs · 2005
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Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Total recall: Automatic query expansion with a generative feature model for object retrieval
Ondrej Chum, James Philbin, Josef Sivic, Michael Isard, and Andrew Zisserman · 2007
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Object retrieval with large vocabularies and fast spatial matching
James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman · 2007
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Detecting digital image forgeries by measuring inconsistencies of blocking artifact
Shuiming Ye, Qibin Sun, and Ee-Chien Chang · 2007
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Object detection with discriminatively trained part-based models
Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan · 2009
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Using noise inconsistencies for blind image forensics
Babak Mahdian and Stanislav Saic · 2009
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Improving bag-of-features for large scale image search
Hervé Jégou, Matthijs Douze, and Cordelia Schmid · 2010
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Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
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Product quantization for nearest neighbor search
Herve Jegou, Matthijs Douze, and Cordelia Schmid · 2011
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Image forgery localization via fine-grained analysis of cfa artifacts
Pasquale Ferrara, Tiziano Bianchi, Alessia De Rosa, and Alessandro Piva · 2012
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Negative evidences and co-occurences in image retrieval: The benefit of pca and whitening
Hervé Jégou and Ondřej Chum · 2012
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 2014
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Neural codes for image retrieval
Artem Babenko, Anton Slesarev, Alexandr Chigorin, and Victor Lempitsky · 2014
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Fast feature pyramids for object detection
Piotr Dollár, Ron Appel, Serge Belongie, and Pietro Perona · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Aggregating local deep features for image retrieval
Artem Babenko and Victor Lempitsky · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Particular object retrieval with integral max-pooling of cnn activations
Convolutional neural network architecture for geometric matching
Ignacio Rocco, Relja Arandjelovic, and Josef Sivic · 2017
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Deep metric learning with angular loss
Jian Wang, Feng Zhou, Shilei Wen, Xiao Liu, and Yuanqing Lin · 2017
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Sampling matters in deep embedding learning
Chao-Yuan Wu, R Manmatha, Alexander J Smola, and Philipp Krahenbuhl · 2017
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Learning spread-out local feature descriptors
Xu Zhang, Felix X Yu, Sanjiv Kumar, and Shih-Fu Chang · 2017
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Deep metric learning with hierarchical triplet loss
Weifeng Ge · 2018
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The PS-Battles Dataset – an Image Collection for Image Manipulation Detection
Silvan Heller, Luca Rossetto, and Heiko Schuldt · 2018
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Giorgos Tolias, Ronan Sicre, and Hervé Jégou · 2015
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Exploiting local features from deep networks for image retrieval
Joe Yue-Hei Ng, Fan Yang, and Larry S Davis · 2015
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Netvlad: Cnn architecture for weakly supervised place recognition
Relja Arandjelovic, Petr Gronat, Akihiko Torii, Tomas Pajdla, and Josef Sivic · 2016
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Factors of transferability for a generic convnet representation
Hossein Azizpour, Ali Sharif Razavian, Josephine Sullivan, Atsuto Maki, and Stefan Carlsson · 2016
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Deep image retrieval: Learning global representations for image search
Albert Gordo, Jon Almazán, Jerome Revaud, and Diane Larlus · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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What makes imagenet good for transfer learning?
Minyoung Huh, Pulkit Agrawal, and Alexei A Efros · 2016
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Fighting fake news: Image splice detection via learned self-consistency
Minyoung Huh, Andrew Liu, Andrew Owens, and Alexei A Efros · 2018
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Do better imagenet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V Le · 2018
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Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Tom Duerig, and Vittorio Ferrari · 2018
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Deep learning for generic object detection: A survey
Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, and Matti Pietikäinen · 2018
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Dgc-net: Dense geometric correspondence network
Iaroslav Melekhov, Aleksei Tiulpin, Torsten Sattler, Marc Pollefeys, Esa Rahtu, and Juho Kannala · 2018
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Image provenance analysis at scale
Daniel Moreira, Aparna Bharati, Joel Brogan, Allan Pinto, Michael Parowski, Kevin W Bowyer, Patrick J Flynn, Anderson Rocha, and Walter J Scheirer · 2018
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Revisiting oxford and paris: Large-scale image retrieval benchmarking
Filip Radenović, Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondřej Chum · 2018
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Fine-tuning cnn image retrieval with no human annotation
Filip Radenović, Giorgos Tolias, and Ondrej Chum · 2018
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End-to-end weakly-supervised semantic alignment
Ignacio Rocco, Relja Arandjelović, and Josef Sivic · 2018
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Spreading vectors for similarity search
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, and Hervé Jégou · 2018
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Image splicing localization using a multi-task fully convolutional network (mfcn)
Ronald Salloum, Yuzhuo Ren, and C-C Jay Kuo · 2018
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Sniper: Efficient multi-scale training
Bharat Singh, Mahyar Najibi, and Larry S Davis · 2018
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Detect-to-retrieve: Efficient regional aggregation for image search
Marvin Teichmann, Andre Araujo, Menglong Zhu, and Jack Sim · 2018
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Busternet: Detecting copy-move image forgery with source/target localization
Yue Wu, Wael Abd-Almageed, and Prem Natarajan · 2018
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Sift meets cnn: A decade survey of instance retrieval
Liang Zheng, Yi Yang, and Qi Tian · 2018
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Toward realistic image compositing with adversarial learning
Bor-Chun Chen and Andrew Kae · 2019
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Norm-aware embedding for efficient person search
Di Chen, Shanshan Zhang, Jian Yang, and Bernt Schiele · 2020
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Bi-directional interaction network for person search
Wenkai Dong, Zhaoxiang Zhang, Chunfeng Song, and Tieniu Tan · 2020
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Generate, segment and replace: Towards generic manipulation segmentation
Peng Zhou, Bor-Chun Chen, Xintong Han, Mahyar Najibi, and Larry S Davis · 2020
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