Fetching the paper…
Reading the bibliography…
This paper presents a simple and effective approach to solving the multi-label classification problem.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Nus-wide: a real-world web image database from national university of singapore
Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Gpt2: Empirical slant delay model for radio space geodetic techniques
K. Lagler, M. Schindelegger, J. Böhm, H. Krásná, and T. Nilsson · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
Earlier work this paper cites.
The pascal visual object classes challenge: A retrospective
Mark Everingham, S. M. Eslami, Luc Gool, Christopher K. Williams, John Winn, and Andrew Zisserman · 2015
Earlier work this paper cites.
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu · 2015
Earlier work this paper cites.
Deeply learned attributes for crowded scene understanding
Jing Shao, Kai Kang, Chen Change Loy, and Xiaogang Wang · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Hcp: A flexible cnn framework for multi-label image classification
Yunchao Wei, Wei Xia, Min Lin, Junshi Huang, Bingbing Ni, Jian Dong, Yao Zhao, and Shuicheng Yan · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Cnn-rnn: A unified framework for multi-label image classification
Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, and Wei Xu · 2016
Earlier work this paper cites.
Exploit bounding box annotations for multi-label object recognition
Hao Yang, Joey Tianyi Zhou, Yu Zhang, Bin-Bin Gao, Jianxin Wu, and Jianfei Cai · 2016
Earlier work this paper cites.
Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
Earlier work this paper cites.
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, Michael S. Bernstein, and Li Fei-Fei · 2017
Earlier work this paper cites.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Multi-label image recognition by recurrently discovering attentional regions
Zhouxia Wang, Tianshui Chen, Guanbin Li, Ruijia Xu, and Liang Lin · 2017
Cited alongside, same era.
Learning spatial regularization with image-level supervisions for multi-label image classification
Feng Zhu, Hongsheng Li, Wanli Ouyang, Nenghai Yu, and Xiaogang Wang · 2017
Cited alongside, same era.
Recurrent attentional reinforcement learning for multi-label image recognition
Tianshui Chen, Zhouxia Wang, Guanbin Li, and Liang Lin · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina N. Toutanova · 2018
Cited alongside, same era.
Chest x-rays classification: A multi-label and fine-grained problem
Zongyuan Ge, Dwarikanath Mahapatra, Suman Sedai, Rahil Garnavi, and Rajib Chakravorty · 2018
Cited alongside, same era.
Learning to discover multi-class attentional regions for multi-label image recognition, 2020
Bin-Bin Gao and Hong-Yu Zhou · 2020
Later among the works it cites.
Multi-label image recognition with multi-class attentional regions
Bin-Bin Gao and Hong-Yu Zhou · 2020
Later among the works it cites.
Meng-Hao Guo, Jun-Xiong Cai, Zheng-Ning Liu, Tai-Jiang Mu, Ralph R Martin, and Shi-Min Hu · 2020
Later among the works it cites.
A survey on visual transformer
Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, et al · 2020
Later among the works it cites.
Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Multi-label image classification via knowledge distillation from weakly-supervised detection
Yongcheng Liu, Lu Sheng, Jing Shao, Junjie Yan, Shiming Xiang, and Chunhong Pan · 2018
Cited alongside, same era.
Leslie N Smith · 2018
Cited alongside, same era.
Learning semantic-specific graph representation for multi-label image recognition
Tianshui Chen, Muxin Xu, Xiaolu Hui, Hefeng Wu, and Liang Lin · 2019
Cited alongside, same era.
Multi-label image recognition with joint class-aware map disentangling and label correlation embedding
Z. Chen, X. Wei, X. Jin, and Y. Guo · 2019
Cited alongside, same era.
Multi-Label Image Recognition with Graph Convolutional Networks
Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, and Yanwen Guo · 2019
Cited alongside, same era.
Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G. Carbonell, Quoc Viet Le, and Ruslan Salakhutdinov · 2019
Cited alongside, same era.
Ernie: Enhanced language representation with informative entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu · 2019
Cited alongside, same era.
Later among the works it cites.
General multi-label image classification with transformers
Jack Lanchantin, Tianlu Wang, Vicente Ordonez, and Yanjun Qi · 2020
Later among the works it cites.
Tresnet: High performance gpu-dedicated architecture
Tal Ridnik, Hussam Lawen, Asaf Noy, Emanuel Ben Baruch, Gilad Sharir, and Itamar Friedman · 2020
Later among the works it cites.
Distribution-balanced loss for multi-label classification in long-tailed datasets
Tong Wu, Qingqiu Huang, Ziwei Liu, Yu Wang, and Dahua Lin · 2020
Later among the works it cites.
Attention-driven dynamic graph convolutional network for multi-label image recognition
Jin Ye, Junjun He, Xiaojiang Peng, Wenhao Wu, and Yu Qiao · 2020
Later among the works it cites.
Cross-modality attention with semantic graph embedding for multi-label classification
Renchun You, Zhiyao Guo, Lei Cui, Xiang Long, Yingze Bao, and Shilei Wen · 2020
Later among the works it cites.
Deformable detr: Deformable transformers for end-to-end object detection
Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai · 2020
Later among the works it cites.
Mltr: Multi-label classification with transformer, 2021
Xing Cheng, Hezheng Lin, Xiangyu Wu, Fan Yang, Dong Shen, Zhongyuan Wang, Nian Shi, and Honglin Liu · 2021
Closest in time.
Beyond self-attention: External attention using two linear layers for visual tasks, 2021
Meng-Hao Guo, Zheng-Ning Liu, Tai-Jiang Mu, and Shi-Min Hu · 2021
Closest in time.
Transformers in vision: A survey
Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah · 2021
Closest in time.
Swin transformer: Hierarchical vision transformer using shifted windows, 2021
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Closest in time.
Imagenet-21k pretraining for the masses, 2021
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor · 2021
Closest in time.
Bottleneck transformers for visual recognition
Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, and Ashish Vaswani · 2021
Closest in time.
Cvt: Introducing convolutions to vision transformers, 2021
Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang · 2021
Closest in time.
Tokens-to-token vit: Training vision transformers from scratch on imagenet
Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Francis EH Tay, Jiashi Feng, and Shuicheng Yan · 2021
Closest in time.