Fetching the paper…
Reading the bibliography…
Although foundational vision-language models (VLMs) have proven to be very successful for various semantic discrimination tasks, they still struggle to perform faithfully for fine-grained categorization.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Probabilistic model-agnostic meta-learning
Chelsea Finn, Kelvin Xu, and Sergey Levine · 2018
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
Earlier work this paper cites.
Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Earlier work this paper cites.
Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
Earlier work this paper cites.
Lung and colon cancer histopathological image dataset (lc25000)
Andrew A Borkowski, Marilyn M Bui, L Brannon Thomas, Catherine P Wilson, Lauren A DeLand, and Stephen M Mastorides · 2019
Earlier work this paper cites.
A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
Earlier work this paper cites.
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
Jakob Nikolas Kather, Johannes Krisam, Pornpimol Charoentong, Tom Luedde, Esther Herpel, Cleo-Aron Weis, Timo Gaiser, Alexander Marx, Nektarios A Valous, Dyke Ferber, et al · 2019
Earlier work this paper cites.
Md Ashraful Alam Milton · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Earlier work this paper cites.
Parn: Position-aware relation networks for few-shot learning
Ziyang Wu, Yuwei Li, Lihua Guo, and Kui Jia · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Pre-training without natural images
Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, and Yutaka Satoh · 2020
Cited alongside, same era.
Rethinking few-shot image classification: a good embedding is all you need?
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola · 2020
Cited alongside, same era.
Generalizing from a few examples: A survey on few-shot learning
Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni · 2020
Cited alongside, same era.
Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers
Chi Zhang, Yujun Cai, Guosheng Lin, and Chunhua Shen · 2020
Cited alongside, same era.
Meta-baseline: Exploring simple meta-learning for few-shot learning
Yinbo Chen, Zhuang Liu, Huijuan Xu, Trevor Darrell, and Xiaolong Wang · 2021
Cited alongside, same era.
An image is worth one word: Personalizing text-to-image generation using textual inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or · 2023
Later among the works it cites.
Your diffusion model is secretly a zero-shot classifier
Alexander C Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, and Deepak Pathak · 2023
Later among the works it cites.
A closer look at few-shot classification again
Xu Luo, Hao Wu, Ji Zhang, Lianli Gao, Jing Xu, and Jingkuan Song · 2023
Later among the works it cites.
Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2023
Later among the works it cites.
Clip for all things zero-shot sketch-based image retrieval, fine-grained or not
Aneeshan Sain, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Subhadeep Koley, Tao Xiang, and Yi-Zhe Song · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
Cited alongside, same era.
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 Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
Cited alongside, same era.
On episodes, prototypical networks, and few-shot learning
Steinar Laenen and Luca Bertinetto · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Analyzing the potential of zero-shot recognition for document image classification
Shoaib Ahmed Siddiqui, Andreas Dengel, and Sheraz Ahmed · 2021
Cited alongside, same era.
Few-shot classification with feature map reconstruction networks
Davis Wertheimer, Luming Tang, and Bharath Hariharan · 2021
Cited alongside, same era.
Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
Cited alongside, same era.
Bi-directional feature reconstruction network for fine-grained few-shot image classification
Jijie Wu, Dongliang Chang, Aneeshan Sain, Xiaoxu Li, Zhanyu Ma, Jie Cao, Jun Guo, and Yi-Zhe Song · 2023
Later among the works it cites.
Visual-language prompt tuning with knowledge-guided context optimization
Hantao Yao, Rui Zhang, and Changsheng Xu · 2023
Later among the works it cites.
Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
Later among the works it cites.
Prompt-aligned gradient for prompt tuning
Beier Zhu, Yulei Niu, Yucheng Han, Yue Wu, and Hanwang Zhang · 2023
Later among the works it cites.
Test-time adaptation with salip: A cascade of sam and clip for zero shot medical image segmentation
Sidra Aleem, Fangyijie Wang, Mayug Maniparambil, Eric Arazo, Julia Dietlmeier, Kathleen Curran, Noel E O’Connor, and Suzanne Little · 2024
Closest in time.
Universal guidance for diffusion models
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2024
Closest in time.
Gazediff: A radiologist visual attention guided diffusion model for zero-shot disease classification
Moinak Bhattacharya and Prateek Prasanna · 2024
Closest in time.
Freeseg-diff: Training-free open-vocabulary segmentation with diffusion models
Barbara Toniella Corradini, Mustafa Shukor, Paul Couairon, Guillaume Couairon, Franco Scarselli, and Matthieu Cord · 2024
Closest in time.
Lp++: A surprisingly strong linear probe for few-shot clip
Yunshi Huang, Fereshteh Shakeri, Jose Dolz, Malik Boudiaf, Houda Bahig, and Ismail Ben Ayed · 2024
Closest in time.
Intriguing properties of generative classifiers
Priyank Jaini, Kevin Clark, and Robert Geirhos · 2024
Closest in time.
Promptad: Zero-shot anomaly detection using text prompts
Yiting Li, Adam Goodge, Fayao Liu, and Chuan-Sheng Foo · 2024
Closest in time.
Few-shot adaptation of multi-modal foundation models: A survey
Fan Liu, Tianshu Zhang, Wenwen Dai, Wenwen Cai, Xiaocong Zhou, and Delong Chen · 2024
Closest in time.
Consistency-guided prompt learning for vision-language models
Shuvendu Roy and Ali Etemad · 2024
Closest in time.
Data-free generalized zero-shot learning
Bowen Tang, Jing Zhang, Long Yan, Qian Yu, Lu Sheng, and Dong Xu · 2024
Closest in time.
A survey of efficient fine-tuning methods for vision-language models—prompt and adapter
JiaLu Xing, JianPing Liu, Jian Wang, LuLu Sun, Xi Chen, XunXun Gu, and YingFei Wang · 2024
Closest in time.
Vision-language models for vision tasks: A survey
Jingyi Zhang, Jiaxing Huang, Sheng Jin, and Shijian Lu · 2024
Closest in time.