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We consider the problem of visually explaining similarity models, i.e., explaining why a model predicts two images to be similar in addition to producing a scalar score.
Grabcut: Interactive foreground extraction using iterated graph cuts
Carsten Rother, Vladimir Kolmogorov, and Andrew Blake · 2004
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Distance metric learning for large margin nearest neighbor classification
Kilian Q. Weinberger and Lawrence K. Saul · 2009
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Content-based image retrieval in radiology: current status and future directions
Ceyhun Burak Akgül, Daniel L Rubin, Sandy Napel, Christopher F Beaulieu, Hayit Greenspan, and Burak Acar · 2011
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Large scale metric learning from equivalence constraints
Martin Koestinger, Martin Hirzer, Paul Wohlhart, Peter M. Roth, and Horst Bischof · 2012
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Local Fisher discriminant analysis for pedestrian re-identification
Sateesh Pedagadi, James Orwell, Sergio Velastin, and Boghos Boghossian · 2013
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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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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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DeepReID: Deep filter pairing neural network for person re-identification
Wei Li, Rui Zhao, Tong Xiao, and Xiaogang Wang · 2014
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Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 2015
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Person re-identification by local maximal occurrence representation and metric learning
Shengcai Liao, Yang Hu, Xiangyu Zhu, and Stan Z. Li · 2015
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Scalable person re-identification: A benchmark
Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian · 2015
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein · 2016
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Improved deep metric learning with multi-class N-pair loss objective
Kihyuk Sohn · 2016
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Deep metric learning via lifted structured feature embedding
Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese · 2016
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Metric learning with adaptive density discrimination
Oren Rippel, Manohar Paluri, Piotr Dollar, and Lubomir Bourdev · 2016
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Person re-identification: Past, present and future
Liang Zheng, Yi Yang, and Alexander G Hauptmann · 2016
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Residual attention network for image classification
Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, and Xiaoou Tang · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 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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Smart mining for deep metric learning
Ben Harwood, G VijayKumarB., Gustavo Carneiro, Ian Reid, and Tom Drummond · 2017
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No fuss distance metric learning using proxies
Yair Movshovitz-Attias, Alexander Toshev, Thomas K. Leung, Sergey Ioffe, and Saurabh P. Singh · 2017
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Hard-aware deeply cascaded embedding
Yuhui Yuan, Kuiyuan Yang, and Chao Zhang · 2017
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Learning Discriminative Features with Multiple Granularities for Person Re-Identification
Guanshuo Wang, Yufeng Yuan, Xiong Chen, Jiwei Li, and Xi Zhou · 2018
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Conditional networks for few-shot semantic segmentation
Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alyosha Efros, and Sergey Levine · 2018
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Few-shot semantic segmentation with prototype learning
Nanqing Dong and Eric P. Xing · 2018
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Sharpen focus: Learning with attention separability and consistency
Lezi Wang, Ziyan Wu, Srikrishna Karanam, Kuan-Chuan Peng, Rajat Vikram Singh, Bo Liu, and Dimitris Metaxas · 2019
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Attention branch network: Learning of attention mechanism for visual explanation
Hiroshi Fukui, Tsubasa Hirakawa, Takayoshi Yamashita, and Hironobu Fujiyoshi · 2019
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Re-identification with consistent attentive siamese networks
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BIER —- boosting independent embeddings robustly
Michael Opitz, Georg Waltner, Horst Possegger, and Horst Bischof · 2017
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Re-ranking person re-identification with k-reciprocal encoding
Zhun Zhong, Liang Zheng, Donglin Cao, and Shaozi Li · 2017
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SVDNet for pedestrian retrieval
Yifan Sun, Liang Zheng, Weijian Deng, and Shengjin Wang · 2017
Cited alongside, same era.
One-shot video object segmentation
Sergi Caelles, Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Laura Leal-Taixé, Daniel Cremers, and Luc Van Gool · 2017
Cited alongside, same era.
One-shot learning for semantic segmentation
Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, and Byron Boots · 2017
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Tell me where to look: Guided attention inference network
Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, and Yun Fu · 2018
Cited alongside, same era.
Meng Zheng, Srikrishna Karanam, Ziyan Wu, and Richard J Radke · 2019
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Deep metric learning to rank
Fatih Cakir, Kun He, Xide Xia, Brian Kulis, and Stan Sclaroff · 2019
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Multi-similarity loss with general pair weighting for deep metric learning
Xun Wang, Xintong Han, Weiling Huang, Dengke Dong, and Matthew R. Scott · 2019
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Hardness-aware deep metric learning
Wenzhao Zheng, Zhaodong Chen, Jiwen Lu, and Jie Zhou · 2019
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Hybrid-attention based decoupled metric learning for zero-shot image retrieval
Binghui Chen and Weihong Deng · 2019
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Panet: Few-shot image semantic segmentation with prototype alignment
K. Wang, J. H. Liew, Y. Zou, D. Zhou, and J. Feng · 2019
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Similar image search for histopathology: Smily
Narayan Hegde, Jason D Hipp, Yun Liu, Michael Emmert-Buck, Emily Reif, Daniel Smilkov, Michael Terry, Carrie J Cai, Mahul B Amin, Craig H Mermel, et al · 2019
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Why do these match? explaining the behavior of image similarity models
Bryan Plummer, Mariya Vasileva, Vitali Petsiuk, Kate Saenko, and David Forsyth · 2020
Closest in time.
Adapting grad-cam for embedding networks
L. Chen, J. Chen, H. Hajimirsadeghi, and G. Mori · 2020
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The group loss for deep metric learning
Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, and Laura Leal-Taixe · 2020
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Spherical feature transform for deep metric learning
Yuke Zhu, Yan Bai, and Yichen Wei · 2020
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A simple and effective framework for pairwise deep metric learning
Qi Qi, Yan Yan, Zixuan Wu, Xiaoyu Wang, and Tianbao Yang · 2020
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Rethinking the distribution gap of person re-identification with camera-based batch normalization
Zijie Zhuang, Longhui Wei, Lingxi Xie, Tianyu Zhang, Hengheng Zhang, Haozhe Wu, Haizhou Ai, and Qi Tian · 2020
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Guided saliency feature learning for person re-identification in crowded scenes
Lingxiao He and Liu Wu · 2020
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