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Contrastive language-image pretraining (CLIP) using image-text pairs has achieved impressive results on image classification in both zero-shot and transfer learning settings.
Image-to-word transformation based on dividing and vector quantizing images with words
Yasuhide Mori Hironobu, Hironobu Takahashi, and Ryuichi Oka · 1999
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Matching words and pictures
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ImageNet: A Large-Scale Hierarchical Image Database
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Learning models for object recognition from natural language descriptions
Josiah Wang, Katja Markert, and Mark Everingham · 2009
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Gool, Christopher K. Williams, John Winn, and Andrew Zisserman · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Learning everything about anything: Webly-supervised visual concept learning
Santosh K Divvala, Ali Farhadi, and Carlos Guestrin · 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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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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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Microsoft COCO captions: Data collection and evaluation server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick · 2015
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Webly supervised learning of convolutional networks
Xinlei Chen and Abhinav Gupta · 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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Generating semantically precise scene graphs from textual descriptions for improved image retrieval
Sebastian Schuster, Ranjay Krishna, Angel Chang, Li Fei-Fei, and Christopher D. Manning · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning visual features from large weakly supervised data
Armand Joulin, Laurens Van Der Maaten, Allan Jabri, and Nicolas Vasilache · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Fine-grained image classification via combining vision and language
Xiangteng He and Yuxin Peng · 2017
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Visual Genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
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Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang · 2018
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Zero-shot object detection
Ankan Bansal, Karan Sikka, Gaurav Sharma, Rama Chellappa, and Ajay Divakaran · 2018
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Scene graph parser
Mao Jiayuan and Kasai Seito · 2018
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
Zero-shot object detection: Joint recognition and localization of novel concepts
Shafin Rahman, Salman H Khan, and Fatih Porikli · 2020
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Learning visual representations with caption annotations
Mert Bulent Sariyildiz, Julien Perez, and Diane Larlus · 2020
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A simple semi-supervised learning framework for object detection
Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li, Han Zhang, Chen-Yu Lee, and Tomas Pfister · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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Unified vision-language pre-training for image captioning and vqa
Luowei Zhou, Hamid Palangi, Lei Zhang, Houdong Hu, Jason Corso, and Jianfeng Gao · 2020
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R-fcn-3000 at 30fps: Decoupling detection and classification
Bharat Singh, Hengduo Li, Abhishek Sharma, and Larry S Davis · 2018
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Lvis: A dataset for large vocabulary instance segmentation
Agrim Gupta, Piotr Dollar, and Ross Girshick · 2019
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ViLBERT: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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Lxmert: Learning cross-modality encoder representations from transformers
Hao Tan and Mohit Bansal · 2019
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Fcos: Fully convolutional one-stage object detection
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He · 2019
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Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
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Don’t even look once: Synthesizing features for zero-shot detection
Pengkai Zhu, Hanxiao Wang, and Venkatesh Saligrama · 2020
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Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, and Quoc Le · 2020
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VirTex: Learning visual representations from textual annotations
Karan Desai and Justin Johnson · 2021
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Simple copy-paste is a strong data augmentation method for instance segmentation
Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph · 2021
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Zero-shot detection via vision and language knowledge distillation
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Efficient visual pretraining with contrastive detection
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V Le, Yunhsuan Sung, Zhen Li, and Tom Duerig · 2021
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Meta pseudo labels
Hieu Pham, Zihang Dai, Qizhe Xie, and Quoc V Le · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Predet: Large-scale weakly supervised pre-training for detection
Vignesh Ramanathan, Rui Wang, and Dhruv Mahajan · 2021
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End-to-end semi-supervised object detection with soft teacher
Mengde Xu, Zheng Zhang, Han Hu, Jianfeng Wang, Lijuan Wang, Fangyun Wei, Xiang Bai, and Zicheng Liu · 2021
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Ernie-vil: Knowledge enhanced vision-language representations through scene graphs
Fei Yu, Jiji Tang, Weichong Yin, Yu Sun, Hao Tian, Hua Wu, and Haifeng Wang · 2021
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Open-vocabulary object detection using captions
Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang · 2021
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Vinvl: Revisiting visual representations in vision-language models
Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, and Jianfeng Gao · 2021
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Learning to generate scene graph from natural language supervision
Yiwu Zhong, Jing Shi, Jianwei Yang, Chenliang Xu, and Yin Li · 2021
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Probabilistic two-stage detection
Xingyi Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2021
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