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Many visual recognition models are evaluated only on their classification accuracy, a metric for which they obtain strong performance.
An analysis of physician attitudes regarding computer-based clinical consultation systems
Randy L Teach and Edward H Shortliffe · 1981
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Learning to detect unseen object classes by between-class attribute transfer
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
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Describable visual attributes for face verification and image search
Neeraj Kumar, Alexander C. Berg, Peter N. Belhumeur, and Shree K. Nayar · 2011
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Sun attribute database: Discovering, annotating, and recognizing scene attributes
Genevieve Patterson and James Hays · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
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Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Social lstm: Human trajectory prediction in crowded spaces
Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Generating visual explanations
Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell · 2016
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Human attribute recognition by deep hierarchical contexts
Yining Li, Chen Huang, Chen Change Loy, and Xiaoou Tang · 2016
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Deepfashion: Powering robust clothes recognition and retrieval with rich annotations
Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang · 2016
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“what happens if…” learning to predict the effect of forces in images
Roozbeh Mottaghi, Mohammad Rastegari, Abhinav Gupta, and Ali Farhadi · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Anh Mai Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune · 2016
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COCO attributes: Attributes for people, animals, and objects
Genevieve Patterson and James Hays · 2016
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”why should I trust you?”: Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2016
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Explanation and justification in machine learning: A survey
Or Biran and Courtenay Cotton · 2017
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A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
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Plug & play generative networks: Conditional iterative generation of images in latent space
Anh Nguyen, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, and Jason Yosinski · 2017
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Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda B. Viégas, and Martin Wattenberg · 2017
Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers
Hila Chefer, Shir Gur, and Lior Wolf · 2021
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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, Jakob Uszkoreit, and Neil Houlsby · 2021
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Learning to predict visual attributes in the wild
Khoi Pham, Kushal Kafle, Zhe Lin, Zhihong Ding, Scott Cohen, Quan Tran, and Abhinav Shrivastava · 2021
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Learning to predict visual attributes in the wild
Khoi Pham, Kushal Kafle, Zhe Lin, Zhihong Ding, Scott Cohen, Quan Tran, and Abhinav Shrivastava · 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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Zero-shot learning - the good, the bad and the ugly
Yongqin Xian, Bernt Schiele, and Zeynep Akata · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2018
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Learning to act properly: Predicting and explaining affordances from images
Ching-Yao Chuang, Jiaman Li, Antonio Torralba, and Sanja Fidler · 2018
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Textual explanations for self-driving vehicles
Jinkyu Kim, Anna Rohrbach, Trevor Darrell, John Canny, and Zeynep Akata · 2018
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Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment
Sérgio Pereira, Raphael Meier, Victor Alves, Mauricio Reyes, and Carlos A. Silva · 2018
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Exploring visual prompts for adapting large-scale models
H Bahng, A Jahanian, S Sankaranarayanan, and P Isola · 2022
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Visual prompting via image inpainting
Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Globerson, and Alexei A. Efros · 2022
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Efficient transfer learning for visual tasks via continuous optimization of prompts
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Ego4d: Around the world in 3,000 hours of egocentric video
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Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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Causal transportability for visual recognition
Chengzhi Mao, Kevin Xia, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, and Carl Vondrick · 2022
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Visual classification via description from large language models
Sachit Menon and Carl Vondrick · 2022
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Fine-tuning image transformers using learnable memory
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K-lite: Learning transferable visual models with external knowledge
Sheng Shen, Chunyuan Li, Xiaowei Hu, Yujia Xie, Jianwei Yang, Pengchuan Zhang, Anna Rohrbach, Zhe Gan, Lijuan Wang, Lu Yuan, et al · 2022
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Flava: A foundational language and vision alignment model
Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela · 2022
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Salient imagenet: How to discover spurious features in deep learning?
Sahil Singla and Soheil Feizi · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou · 2022
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Socratic models: Composing zero-shot multimodal reasoning with language
Andy Zeng, Adrian Wong, Stefan Welker, Krzysztof Choromanski, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, et al · 2022
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Prompt consistency for zero-shot task generalization
Chunting Zhou, Junxian He, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig · 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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