{"type":"ScholarlyArticle","title":"Image Retrieval with Self-Supervised Divergence Minimization and Cross-Attention Classification","authors":[{"name":"Vivek Trivedy","slug":"vivek-trivedy","given":"Vivek","family":"Trivedy","affiliation":"Department of Computer and Information Sciences , Temple University , Philadelphia"},{"name":"Longin Jan Latecki","slug":"longin-jan-latecki","given":"Longin Jan","family":"Latecki","affiliation":"Department of Computer and Information Sciences , Temple University , Philadelphia"}],"abstract":"Common approaches to image retrieval include contrastive methods and specialized loss functions such as ranking losses and entropy regularizers. We present DMCAC (Divergence Minimization with Cross-Attention Classification), a novel image retrieval method that offers a new perspective on this training paradigm.  We use self-supervision with a novel divergence loss framework alongside a simple data flow adjustment that minimizes a distribution over a database directly during training. We show that jointly learning a query representation over a database is a competitive and often improved alternative to traditional contrastive methods for image retrieval. We evaluate our method across several model configurations and four datasets, achieving state-of-the-art performance in multiple settings. We also conduct a thorough set of ablations that show the robustness of our method across full vs. approximate retrieval and different hyperparameter configurations.","doi":"10.24963/ijcai.2024/149","url":"https://ijcai.org/proceedings/2024/149","pdf":"https://ijcai.org/proceedings/2024/0149.pdf","markdown":"https://ijcai.org/proceedings/2024/149.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/149","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":149,"pages":{"first":1344,"last":1352},"keywords":["Computer Vision: CV: Image and video retrieval","Computer Vision: CV: Representation learning"],"videos":[],"references":[{"position":1,"raw":"[Ba et al., 2016] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization, 2016.","title":"Layer normalization","authors":"Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E Hinton","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Baevski et al., 2022] Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, and Michael Auli. data2vec: A general framework for self-supervised learning in speech, vision and language, 2022.","title":"data2vec: A general framework for self-supervised learning in speech, vision and language","authors":"Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Boudiaf et al., 2021] Malik Boudiaf, Jérôme Rony, Imtiaz Masud Ziko, Eric Granger, Marco Pedersoli, Pablo Piantanida, and Ismail Ben Ayed. A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses, 2021.","title":"A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses","authors":"Malik Boudiaf, Jérôme Rony, Masud Imtiaz, Eric Ziko, Marco Granger, Pablo Pedersoli, Ismail Piantanida, Ayed Ben","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Caron et al., 2019] Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. Deep clustering for unsupervised learning of visual features, 2019.","title":"Deep clustering for unsupervised learning of visual features","authors":"Mathilde Caron, Piotr Bojanowski, Armand Joulin, Matthijs Douze","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Caron et al., 2021a] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments, 2021.","title":"Unsupervised learning of visual features by contrasting cluster assignments","authors":"Caron","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Caron et al., 2021b] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers, 2021.","title":"Emerging properties in self-supervised vision transformers","authors":"Caron","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Carreira-Perpiñán and Hinton, 2005] Miguel Á. Carreira-Perpiñán and Geoffrey Hinton. On contrastive divergence learning. In Robert G. Cowell and Zoubin Ghahramani, editors, Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, volume R5 of Proceedings of Machine Learning Research, pages 33-40. PMLR, 06-08 Jan 2005. Reissued by PMLR on 30 March 2021.","title":"On contrastive divergence learning","authors":"Á Miguel, Geoffrey Carreira-Perpiñán, Hinton","year":"2005","venue":"Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, volume R5 of Proceedings of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chen et al., 2020] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations, 2020.","title":"A simple framework for contrastive learning of visual representations","authors":"Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Chen et al., 2021] Chun-Fu (Richard) Chen, Quanfu Fan, and Rameswar Panda. Crossvit: Cross-attention multi-scale vision transformer for image classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 357-366, October 2021.","title":"Crossvit: Cross-attention multi-scale vision transformer for image classification","authors":"Chun-Fu ( Richard) Chen, Quanfu Fan, Rameswar Panda","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Cubuk et al., 2019a] Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le. Autoaugment: Learning augmentation policies from data, 2019.","title":"Autoaugment: Learning augmentation policies from data","authors":"Cubuk","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Cubuk et al., 2019b] Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le. Randaugment: Practical automated data augmentation with a reduced search space, 2019.","title":"Randaugment: Practical automated data augmentation with a reduced search space","authors":"Cubuk","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Deng et al., 2009] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248-255, 2009.","title":"Imagenet: A large-scale hierarchical image database","authors":"Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei","year":"2009","venue":"IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Devlin et al., 2019] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding, 2019.","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","authors":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Doersch et al., 2016] Carl Doersch, Abhinav Gupta, and Alexei A. Efros. Unsupervised visual representation learning by context prediction, 2016. [Dosovitskiy et al., 2021]","title":"Unsupervised visual representation learning by context prediction","authors":"Carl Doersch, Abhinav Gupta, Alexei A Efros","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale, 2021.","title":"An image is worth 16x16 words: Transformers for image recognition at scale","authors":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[El-Nouby et al., 2021a] Alaaeldin El-Nouby, Natalia Neverova, Ivan Laptev, and Hervé Jégou. Training vision transformers for image retrieval, 2021.","title":"Training vision transformers for image retrieval","authors":"El-Nouby","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[El-Nouby et al., 2021b] Alaaeldin El-Nouby, Natalia Neverova, Ivan Laptev, and Hervé Jégou. Training vision transformers for image retrieval, 2021.","title":"Training vision transformers for image retrieval","authors":"El-Nouby","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Ermolov et al., 2022] Aleksandr Ermolov, Leyla Mirvakhabova, Valentin Khrulkov, Nicu Sebe, and Ivan Oseledets. Hyperbolic vision transformers: Combining improvements in metric learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7409- 7419, June 2022.","title":"Hyperbolic vision transformers: Combining improvements in metric learning","authors":"Aleksandr Ermolov, Leyla Mirvakhabova, Valentin Khrulkov, Nicu Sebe, Ivan Oseledets","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ge et al., 2018] Weifeng Ge, Weilin Huang, Dengke Dong, and Matthew R. Scott. Deep metric learning with hierarchical triplet loss, 2018.","title":"Deep metric learning with hierarchical triplet loss","authors":"Weifeng Ge, Weilin Huang, Dengke Dong, Matthew R Scott","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Hadsell et al., 2006] R. Hadsell, S. Chopra, and Y. LeCun. Dimensionality reduction by learning an invariant mapping. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), volume 2, pages 1735- 1742, 2006.","title":"Dimensionality reduction by learning an invariant mapping","authors":"R Hadsell, S Chopra, Y Lecun","year":"2006","venue":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[He et al., 2015] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition, 2015.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[He et al., 2021] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners, 2021.","title":"Masked autoencoders are scalable vision learners","authors":"Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Hoffer and Ailon, 2018] Elad Hoffer and Nir Ailon. Deep metric learning using triplet network, 2018.","title":"Deep metric learning using triplet network","authors":"Elad Hoffer, Nir Ailon","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Jacob et al., 2019] Pierre Jacob, David Picard, Aymeric Histace, and Edouard Klein. Metric learning with horde: High-order regularizer for deep embeddings, 2019.","title":"Metric learning with horde: High-order regularizer for deep embeddings","authors":"Jacob","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Johnson et al., 2019] Jeff Johnson, Matthijs Douze, and Hervé Jégou. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3):535-547, 2019.","title":"Billion-scale similarity search with GPUs","authors":"Jeff Johnson, Matthijs Douze, Hervé Jégou","year":"2019","venue":"IEEE Transactions on Big Data","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Khosla et al., 2021] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning, 2021.","title":"Supervised contrastive learning","authors":"Khosla","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Kim et al., 2018] Wonsik Kim, Bhavya Goyal, Kunal Chawla, Jungmin Lee, and Keunjoo Kwon. Attention-based ensemble for deep metric learning, 2018.","title":"Attention-based ensemble for deep metric learning","authors":"Wonsik Kim, Bhavya Goyal, Kunal Chawla, Jungmin Lee, Keunjoo Kwon","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Kim et al., 2020a] Sungyeon Kim, Dongwon Kim, Minsu Cho, and Suha Kwak. Proxy anchor loss for deep metric learning, 2020.","title":"Proxy anchor loss for deep metric learning","authors":"Kim","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Kim et al., 2020b] Sungyeon Kim, Dongwon Kim, Minsu Cho, and Suha Kwak. Proxy anchor loss for deep metric learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.","title":"Proxy anchor loss for deep metric learning","authors":"Kim","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Koppula et al., 2022] Skanda Koppula, Yazhe Li, Evan Shelhamer, Andrew Jaegle, Nikhil Parthasarathy, Relja Arandjelovic, João Carreira, and Olivier Hénaff. Where should i spend my flops? efficiency evaluations of visual pre-training methods, 2022.","title":"Where should i spend my flops? efficiency evaluations of visual pre-training methods","authors":"Skanda Koppula, Yazhe Li, Evan Shelhamer, Andrew Jaegle, Nikhil Parthasarathy, Relja Arandjelovic, João Carreira, Olivier Hénaff","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Krause et al., 2013] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13), Sydney, Australia, 2013.","title":"3d object representations for fine-grained categorization","authors":"Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei","year":"2013","venue":"4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13)","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Liu et al., 2016] Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang. Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1096-1104, 2016.","title":"Deepfashion: Powering robust clothes recognition and retrieval with rich annotations","authors":"Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, Xiaoou Tang","year":"2016","venue":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Liu et al., 2021] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021.","title":"Swin transformer: Hierarchical vision transformer using shifted windows","authors":"Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Loshchilov and Hutter, 2019] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization, 2019.","title":"Ilya Loshchilov and Frank Hutter","authors":"Hutter Loshchilov","year":"2019","venue":"Decoupled weight decay regularization","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Movshovitz-Attias et al., 2017a] Y. Movshovitz-Attias, A. Toshev, T. K. Leung, S. Ioffe, and S. Singh. No fuss distance metric learning using proxies. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 360-368, Los Alamitos, CA, USA, oct 2017. IEEE Computer Society.","title":"No fuss distance metric learning using proxies","authors":"Movshovitz-Attias","year":"2017","venue":"IEEE International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Movshovitz-Attias et al., 2017b] Yair Movshovitz-Attias, Alexander Toshev, Thomas K. Leung, Sergey Ioffe, and Saurabh Singh. No fuss distance metric learning using proxies, 2017.","title":null,"authors":"Movshovitz-Attias","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Noroozi and Favaro, 2017a] Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving jigsaw puzzles, 2017.","title":"Unsupervised learning of visual representations by solving jigsaw puzzles","authors":"Favaro Noroozi, Mehdi Noroozi, Paolo Favaro","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Noroozi and Favaro, 2017b] Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving jigsaw puzzles, 2017.","title":"Unsupervised learning of visual representations by solving jigsaw puzzles","authors":"Favaro Noroozi, Mehdi Noroozi, Paolo Favaro","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Opitz et al., 2020] Michael Opitz, Georg Waltner, Horst Possegger, and Horst Bischof. Deep metric learning with bier: Boosting independent embeddings robustly. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2):276-290, 2020.","title":"Deep metric learning with bier: Boosting independent embeddings robustly","authors":"Michael Opitz, Georg Waltner, Horst Possegger, Horst Bischof","year":"2020","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Park et al., 2023] Namuk Park, Wonjae Kim, Byeongho Heo, Taekyung Kim, and Sangdoo Yun. What do self-supervised vision transformers learn?, 2023.","title":"What do self-supervised vision transformers learn?","authors":"Park","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Qian et al., 2019] Qi Qian, Lei Shang, Baigui Sun, Juhua Hu, Hao Li, and Rong Jin. Softtriple loss: Deep metric learning without triplet sampling. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019.","title":"Softtriple loss: Deep metric learning without triplet sampling","authors":"Lei Qi Qian, Baigui Shang, Juhua Sun, Hao Hu, Rong Li, Jin","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Robinson et al., 2021] Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka. Contrastive learning with hard negative samples, 2021.","title":"Contrastive learning with hard negative samples","authors":"Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Shen et al., 2013] Chunhua Shen, Junae Kim, Fayao Liu, Lei Wang, and Anton van den Hengel. An efficient dual approach to distance metric learning, 2013.","title":"An efficient dual approach to distance metric learning","authors":"Chunhua Shen, Junae Kim, Fayao Liu, Lei Wang, Anton Van Den, Hengel","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Song et al., 2016] Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese. Deep metric learning via lifted structured feature embedding. In Computer Vision and Pattern Recognition (CVPR), 2016.","title":"Deep metric learning via lifted structured feature embedding","authors":"Hyun Oh Song, Yu Xiang, Stefanie Jegelka, Silvio Savarese","year":"2016","venue":"Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Song et al., 2023] Chull Hwan Song, Jooyoung Yoon, Shunghyun Choi, and Yannis Avrithis. Boosting vision transformers for image retrieval. In WACV, 2023.","title":"Boosting vision transformers for image retrieval","authors":"Song","year":"2023","venue":"WACV","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Steiner et al., 2022] Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer. How to train your vit? data, augmentation, and regularization in vision transformers, 2022.","title":"How to train your vit? data, augmentation, and regularization in vision transformers","authors":"Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, Lucas Beyer","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Strudel et al., 2021] Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. Segmenter: Transformer for semantic segmentation, 2021.","title":"Segmenter: Transformer for semantic segmentation","authors":"Strudel","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Suh et al., 2019] Yumin Suh, Bohyung Han, Wonsik Kim, and Kyoung Mu Lee. Stochastic class-based hard example mining for deep metric learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.","title":"Stochastic class-based hard example mining for deep metric learning","authors":"Yumin Suh, Bohyung Han, Wonsik Kim, Kyoung Mu, Lee","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Szegedy et al., 2014] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions, 2014.","title":"Going deeper with convolutions","authors":"Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Teh et al., 2020a] Eu Wern Teh, Terrance DeVries, and Graham W. Taylor. Proxynca++: Revisiting and revitalizing proxy neighborhood component analysis, 2020.","title":"Proxynca++: Revisiting and revitalizing proxy neighborhood component analysis","authors":"Teh","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Teh et al., 2020b] Eu Wern Teh, Terrance DeVries, and Graham W. Taylor. Proxynca++: Revisiting and revitalizing proxy neighborhood component analysis, 2020.","title":"Proxynca++: Revisiting and revitalizing proxy neighborhood component analysis","authors":"Teh","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Touvron et al., 2021] Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers distillation through attention, 2021.","title":"Training data-efficient image transformers distillation through attention","authors":"Touvron","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Trivedy and Latecki, 2023] Vivek Trivedy and Longin Jan Latecki. Cnn2graph: Building graphs for image classification. In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 1-11, 2023.","title":"Cnn2graph: Building graphs for image classification","authors":"Vivek Trivedy, Jan Longin, Latecki","year":"2023","venue":"IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17, page 6000-6010, Red Hook, NY, USA, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"Curran Associates Inc. [Wah et al., 2011]","title":null,"authors":"Curran Associates, Inc [ Wah","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":56,"raw":"C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. Caltech-ucsd birds-200-2011. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011.","title":null,"authors":"C Wah, S Branson, P Welinder, P Perona, S Belongie","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Wang et al., 2019] Xun Wang, Xintong Han, Weilin Huang, Dengke Dong, and Matthew R. Scott. Multi-similarity loss with general pair weighting for deep metric learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.","title":"Multi-similarity loss with general pair weighting for deep metric learning","authors":"Xun Wang, Xintong Han, Weilin Huang, Dengke Dong, Matthew R Scott","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Wang et al., 2020] Xun Wang, Haozhi Zhang, Weilin Huang, and Matthew R. Scott. Cross-batch memory for embedding learning, 2020.","title":"Cross-batch memory for embedding learning","authors":"Xun Wang, Haozhi Zhang, Weilin Huang, Matthew R Scott","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Yu et al., 2019] Jian Yu, Changhui Hu, Xiaoyuan Jing, Guangliang Zhou, and Shen Jing. Deep metric learning with dynamic margin hard sampling loss. In 2019 Chinese Control Conference (CCC), pages 7901-7905, 2019.","title":"Deep metric learning with dynamic margin hard sampling loss","authors":"Jian Yu, Changhui Hu, Xiaoyuan Jing, Guangliang Zhou, Shen Jing","year":"2019","venue":"2019 Chinese Control Conference (CCC)","doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Zhai and Wu, 2019a] Andrew Zhai and Hao-Yu Wu. Classification is a strong baseline for deep metric learning, 2019.","title":"Classification is a strong baseline for deep metric learning","authors":"Wu Zhai, Andrew Zhai, Hao-Yu Wu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":61,"raw":"[Zhai and Wu, 2019b] Andrew Zhai and Hao-Yu Wu. Classification is a strong baseline for deep metric learning, 2019.","title":"Classification is a strong baseline for deep metric learning","authors":"Wu Zhai, Andrew Zhai, Hao-Yu Wu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}