Fetching the paper…
Reading the bibliography…
Vision-language pretrained models have seen remarkable success, but their application to safety-critical settings is limited by their lack of interpretability.
The information bottleneck method, 2000
Naftali Tishby, Fernando C. Pereira, and William Bialek · 2000
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
Earlier work this paper cites.
Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
Earlier work this paper cites.
Very deep convolutional neural network based image classification using small training sample size
Shuying Liu and Weihong Deng · 2015
Earlier work this paper cites.
Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 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.
"Why should I trust you?": Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2016
Earlier work this paper cites.
Deep variational information bottleneck
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy · 2017
Earlier work this paper cites.
Exploring models and data for remote sensing image caption generation
Xiaoqiang Lu, Binqiang Wang, Xiangtao Zheng, and Xuelong Li · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, and Been Kim · 2018
Cited alongside, same era.
Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks
Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian · 2018
Cited alongside, same era.
Radiology Objects in cOntext (ROCO): A multimodal image dataset
Obioma Pelka, Sven Koitka, Johannes Rückert, Felix Nensa, and C. Friedrich · 2018
Cited alongside, same era.
RISE: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
Cited alongside, same era.
Score-CAM: Score-weighted visual explanations for convolutional neural networks
Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, and Xia Hu · 2020
Later among the works it cites.
Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers
Hila Chefer, Shir Gur, and Lior Wolf · 2021
Later among the works it cites.
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, Jakob Uszkoreit, and Neil Houlsby · 2021
Later among the works it cites.
Retrieval-based chest x-ray report generation using a pre-trained contrastive language-image model
Mark Endo, Rayan Krishnan, Viswesh Krishna, Andrew Y. Ng, and Pranav Rajpurkar · 2021
Later among the works it cites.
SLIP: self-supervision meets language-image pre-training
Norman Mu, Alexander Kirillov, David A. Wagner, and Saining Xie · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
Cited alongside, same era.
A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
Cited alongside, same era.
MIMIC-CXR: A large publicly available database of labeled chest radiographs
Alistair E. W. Johnson, Tom J. Pollard, Seth J. Berkowitz, Nathaniel R. Greenbaum, Matthew P. Lungren, Chih-ying Deng, Roger G. Mark, and Steven Horng · 2019
Cited alongside, same era.
Transformer interpretability beyond attention visualization
Hila Chefer, Shir Gur, and Lior Wolf · 2020
Cited alongside, same era.
Inserting information bottlenecks for attribution in transformers
Zhiying Jiang, Raphael Tang, Ji Xin, and Jimmy Lin · 2020
Cited alongside, same era.
Restricting the flow: Information bottlenecks for attribution
Karl Schulz, Leon Sixt, Federico Tombari, and Tim Landgraf · 2020
Cited alongside, same era.
Later among the works it cites.
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, Gretchen Krueger, and Ilya Sutskever · 2021
Later among the works it cites.
Making the most of text semantics to improve biomedical vision–language processing
Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez-Valle, Hoifung Poon, and Ozan Oktay · 2022
Later among the works it cites.
Vision-and-language pretrained models: A survey
Siqu Long, Feiqi Cao, Soyeon Caren Han, and Haiqin Yang · 2022
Later among the works it cites.
A consistent and efficient evaluation strategy for attribution methods
Yao Rong, Tobias Leemann, Vadim Borisov, Gjergji Kasneci, and Enkelejda Kasneci · 2022
Later among the works it cites.
How much can CLIP benefit vision-and-language tasks?
Sheng Shen, Liunian Harold Li, Hao Tan, Mohit Bansal, Anna Rohrbach, Kai-Wei Chang, Zhewei Yao, and Kurt Keutzer · 2022
Later among the works it cites.
ImageBind: One embedding space to bind them all, 2023
Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra · 2023
Closest in time.