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The popularity of Contrastive Language-Image Pre-training (CLIP) has propelled its application to diverse downstream vision tasks.
Linear inversion of band-limited reflection seismograms
Fadil Santosa and William W Symes · 1986
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Optimal brain damage
Yann LeCun, John Denker, and Sara Solla · 1989
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Pruning algorithms-a survey
Russell Reed · 1993
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Feature selection for classification
Manoranjan Dash and Huan Liu · 1997
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Linear discriminant analysis-a brief tutorial
Suresh Balakrishnama and Aravind Ganapathiraju · 1998
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
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Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
Hanchuan Peng, Fuhui Long, and Chris Ding · 2005
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Machine learning techniques and chi-square feature selection for cancer classification using sage gene expression profiles
Xin Jin, Anbang Xu, Rongfang Bie, and Ping Guo · 2006
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Feature selection for high-dimensional data—a pearson redundancy based filter
Jacek Biesiada and Wlodzisław Duch · 2007
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Normalized mutual information feature selection
Pablo A Estévez, Michel Tesmer, Claudio A Perez, and Jacek M Zurada · 2009
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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Pruning algorithms of neural networks—a comparative study
M Augasta and Thangairulappan Kathirvalavakumar · 2013
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Cuttlefish algorithm-a novel bio-inspired optimization algorithm
Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, and Zeynep Orman · 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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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Feature selection for classification: A review
Jiliang Tang, Salem Alelyani, and Huan Liu · 2014
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A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems
Adel Sabry Eesa, Zeynep Orman, and Adnan Mohsin Abdulazeez Brifcani · 2015
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Prototypical priors: From improving classification to zero-shot learning
Saumya Jetley, Bernardino Romera-Paredes, Sadeep Jayasumana, and Philip Torr · 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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Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
Hengyuan Hu, Rui Peng, Yu-Wing Tai, and Chi-Keung Tang · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Comparative study of feature subset selection methods for dimensionality reduction on scientific data
D Lakshmi Padmaja and B Vishnuvardhan · 2016
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Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Few-shot semantic segmentation with prototype learning
Nanqing Dong and Eric P Xing · 2018
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Deepncm: Deep nearest class mean classifiers
Samantha Guerriero, Barbara Caputo, and Thomas Mensink · 2018
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Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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A comparison of three classification algorithms for handwritten digit recognition
Maiwan Bahjat Abdulrazzaq and Jwan Najeeb Saeed · 2019
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
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A review on dimensionality reduction techniques
Xuan Huang, Lei Wu, and Yinsong Ye · 2019
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Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Image retrieval from contextual descriptions
Benno Krojer, Vaibhav Adlakha, Vibhav Vineet, Yash Goyal, Edoardo Ponti, and Siva Reddy · 2022
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
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Scaling language-image pre-training via masking
Yanghao Li, Haoqi Fan, Ronghang Hu, Christoph Feichtenhofer, and Kaiming He · 2022
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Revisiting few-shot learning from a causal perspective
Guoliang Lin and Hanjiang Lai · 2022
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Frozen clip models are efficient video learners
Ziyi Lin, Shijie Geng, Renrui Zhang, Peng Gao, Gerard de Melo, Xiaogang Wang, Jifeng Dai, Yu Qiao, and Hongsheng Li · 2022
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S Velliangiri, SJPCS Alagumuthukrishnan, et al · 2019
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A review of feature selection and its methods
B Venkatesh and J Anuradha · 2019
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Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
Cited alongside, same era.
Overview and comparative study of dimensionality reduction techniques for high dimensional data
Shaeela Ayesha, Muhammad Kashif Hanif, and Ramzan Talib · 2020
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What is the state of neural network pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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What does a platypus look like? generating customized prompts for zero-shot image classification
Sarah Pratt, Rosanne Liu, and Ali Farhadi · 2022
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Denseclip: Language-guided dense prediction with context-aware prompting
Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu · 2022
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Proposalclip: unsupervised open-category object proposal generation via exploiting clip cues
Hengcan Shi, Munawar Hayat, Yicheng Wu, and Jianfei Cai · 2022
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Reco: Retrieve and co-segment for zero-shot transfer
Gyungin Shin, Weidi Xie, and Samuel Albanie · 2022
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Test-time prompt tuning for zero-shot generalization in vision-language models
Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, and Chaowei Xiao · 2022
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Sus-x: Training-free name-only transfer of vision-language models
Vishaal Udandarao, Ankush Gupta, and Samuel Albanie · 2022
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Image as a foreign language: Beit pretraining for all vision and vision-language tasks
Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, et al · 2022
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Visual recognition with deep nearest centroids
Wenguan Wang, Cheng Han, Tianfei Zhou, and Dongfang Liu · 2022
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Cris: Clip-driven referring image segmentation
Zhaoqing Wang, Yu Lu, Qiang Li, Xunqiang Tao, Yandong Guo, Mingming Gong, and Tongliang Liu · 2022
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Eda: Explicit text-decoupling and dense alignment for 3d visual and language learning
Yanmin Wu, Xinhua Cheng, Renrui Zhang, Zesen Cheng, and Jian Zhang · 2022
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Detclip: Dictionary-enriched visual-concept paralleled pre-training for open-world detection
Lewei Yao, Jianhua Han, Youpeng Wen, Xiaodan Liang, Dan Xu, Wei Zhang, Zhenguo Li, Chunjing Xu, and Hang Xu · 2022
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Collaboration of pre-trained models makes better few-shot learner
Renrui Zhang, Hanqiu Deng, Bohao Li, Wei Zhang, Hao Dong, Hongsheng Li, Peng Gao, and Yu Qiao · 2022
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Pointclip: Point cloud understanding by clip
Renrui Zhang, Ziyu Guo, Wei Zhang, Kunchang Li, Xupeng Miao, Bin Cui, Yu Qiao, Peng Gao, and Hongsheng Li · 2022
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Nearest neighbors meet deep neural networks for point cloud analysis
Renrui Zhang, Liuhui Wang, Ziyu Guo, and Jianbo Shi · 2022
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Can language understand depth?
Renrui Zhang, Ziyao Zeng, and Ziyu Guo · 2022
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Regionclip: Region-based language-image pretraining
Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al · 2022
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Conditional prompt learning for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
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Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
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Pointclip v2: Adapting clip for powerful 3d open-world learning
Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyao Zeng, Shanghang Zhang, and Peng Gao · 2022
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Pimae: Point cloud and image interactive masked autoencoders for 3d object detection
Anthony Chen, Kevin Zhang, Renrui Zhang, Zihan Wang, Yuheng Lu, Yandong Guo, and Shanghang Zhang · 2023
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Mimic before reconstruct: Enhancing masked autoencoders with feature mimicking
Peng Gao, Renrui Zhang, Rongyao Fang, Ziyi Lin, Hongyang Li, Hongsheng Li, and Qiao Yu · 2023
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Joint-mae: 2d-3d joint masked autoencoders for 3d point cloud pre-training
Ziyu Guo, Xianzhi Li, and Pheng Ann Heng · 2023
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Viewrefer: Grasp the multi-view knowledge for 3d visual grounding with gpt and prototype guidance
Ziyu Guo, Yiwen Tang, Renrui Zhang, Dong Wang, Zhigang Wang, Bin Zhao, and Xuelong Li · 2023
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Tig-bev: Multi-view bev 3d object detection via target inner-geometry learning
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Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
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Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners
Renrui Zhang, Xiangfei Hu, Bohao Li, Siyuan Huang, Hanqiu Deng, Hongsheng Li, Yu Qiao, and Peng Gao · 2023
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Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders
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Parameter is not all you need: Starting from non-parametric networks for 3d point cloud analysis
Renrui Zhang, Liuhui Wang, Yali Wang, Peng Gao, Hongsheng Li, and Jianbo Shi · 2023
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