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Vision-language models (VLMs) offer a promising paradigm for image classification by comparing the similarity between images and class embeddings.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Fei-Fei, L., Fergus, R., and Perona, P · 2004
Earlier work this paper cites.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Fei-Fei, L., Fergus, R., and Perona, P · 2004
Earlier work this paper cites.
Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A · 2008
Earlier work this paper cites.
Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A. R., and Shah, M · 2012
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A. R., and Shah, M · 2012
Earlier work this paper cites.
Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A · 2014
Earlier work this paper cites.
Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A · 2014
Earlier work this paper cites.
Sun database: Exploring a large collection of scene categories
Xiao, J., Ehinger, K. A., Hays, J., Torralba, A., and Oliva, A · 2016
Earlier work this paper cites.
Sun database: Exploring a large collection of scene categories
Xiao, J., Ehinger, K. A., Hays, J., Torralba, A., and Oliva, A · 2016
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Earlier work this paper cites.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Helber, P., Bischke, B., Dengel, A., and Borth, D · 2019
Earlier work this paper cites.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Helber, P., Bischke, B., Dengel, A., and Borth, D · 2019
Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
Earlier work this paper cites.
Clip-adapter: Better vision-language models with feature adapters
Gao, P., Geng, S., Zhang, R., Ma, T., Fang, R., Zhang, Y., Li, H., and Qiao, Y · 2021
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Earlier work this paper cites.
Clip-adapter: Better vision-language models with feature adapters
Gao, P., Geng, S., Zhang, R., Ma, T., Fang, R., Zhang, Y., Li, H., and Qiao, Y · 2021
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Earlier work this paper cites.
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al · 2022
Cited alongside, same era.
PaLM: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., and Fiedel, N · 2022
Cited alongside, same era.
Unsupervised prompt learning for vision-language models
Huang, T., Chu, J., and Wei, F · 2022
Cited alongside, same era.
Visual prompt tuning
Jia, M., Tang, L., Chen, B.-C., Cardie, C., Belongie, S., Hariharan, B., and Lim, S.-N · 2022
Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
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Next-gpt: Any-to-any multimodal llm
Wu, S., Fei, H., Qu, L., Ji, W., and Chua, T.-S · 2023
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Learning concise and descriptive attributes for visual recognition
Yan, A., Wang, Y., Zhong, Y., Dong, C., He, Z., Lu, Y., Wang, W. Y., Shang, J., and McAuley, J · 2023
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Woodpecker: Hallucination correction for multimodal large language models
Yin, S., Fu, C., Zhao, S., Xu, T., Wang, H., Sui, D., Shen, Y., Li, K., Sun, X., and Chen, E · 2023
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Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D · 2022
Cited alongside, same era.
Tip-adapter: Training-free clip-adapter for better vision-language modeling
Zhang, R., Fang, R., Zhang, W., Gao, P., Li, K., Dai, J., Qiao, Y., and Li, H · 2022
Cited alongside, same era.
Conditional prompt learning for vision-language models
Zhou, K., Yang, J., Loy, C. C., and Liu, Z · 2022
Cited alongside, same era.
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al · 2022
Cited alongside, same era.
PaLM: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., and Fiedel, N · 2022
Cited alongside, same era.
Unsupervised prompt learning for vision-language models
Huang, T., Chu, J., and Wei, F · 2022
Cited alongside, same era.
Visual prompt tuning
Jia, M., Tang, L., Chen, B.-C., Cardie, C., Belongie, S., Hariharan, B., and Lim, S.-N · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D · 2022
Cited alongside, same era.
Yu, W., Gileadi, N., Fu, C., Kirmani, S., Lee, K.-H., Arenas, M. G., Chiang, H.-T. L., Erez, T., Hasenclever, L., Humplik, J., et al · 2023
Closest in time.
Graphtext: Graph reasoning in text space
Zhao, J., Zhuo, L., Shen, Y., Qu, M., Liu, K., Bronstein, M., Zhu, Z., and Tang, J · 2023
Closest in time.
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Zhu, D., Chen, J., Shen, X., Li, X., and Elhoseiny, M · 2023
Closest in time.
Holistic analysis of hallucination in gpt-4v(ision): Bias and interference challenges, 2023
Cui, C., Zhou, Y., Yang, X., Wu, S., Zhang, L., Zou, J., and Yao, H · 2023
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Language is not all you need: Aligning perception with language models
Huang, S., Dong, L., Wang, W., Hao, Y., Singhal, S., Ma, S., Lv, T., Cui, L., Mohammed, O. K., Liu, Q., et al · 2023
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Mathprompter: Mathematical reasoning using large language models
Imani, S., Du, L., and Shrivastava, H · 2023
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BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models
Li, J., Li, D., Savarese, S., and Hoi, S · 2023
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Drugchat: towards enabling chatgpt-like capabilities on drug molecule graphs
Liang, Y., Zhang, R., Zhang, L., and Xie, P · 2023
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The flan collection: Designing data and methods for effective instruction tuning
Longpre, S., Hou, L., Vu, T., Webson, A., Chung, H. W., Tay, Y., Zhou, D., Le, Q. V., Zoph, B., Wei, J., et al · 2023
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Eureka: Human-level reward design via coding large language models
Ma, Y. J., Liang, W., Wang, G., Huang, D.-A., Bastani, O., Jayaraman, D., Zhu, Y., Fan, L., and Anandkumar, A · 2023
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Enhancing clip with gpt-4: Harnessing visual descriptions as prompts
Maniparambil, M., Vorster, C., Molloy, D., Murphy, N., McGuinness, K., and O’Connor, N. E · 2023
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Visual classification via description from large language models
Menon, S. and Vondrick, C · 2023
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Chils: Zero-shot image classification with hierarchical label sets
Novack, Z., Garg, S., McAuley, J., and Lipton, Z · 2023
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OpenAI · 2023
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What does a platypus look like? generating customized prompts for zero-shot image classification
Pratt, S., Covert, I., Liu, R., and Farhadi, A · 2023
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Waffling around for performance: Visual classification with random words and broad concepts, 2023
Roth, K., Kim, J. M., Koepke, A. S., Vinyals, O., Schmid, C., and Akata, Z · 2023
Closest in time.
Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
Closest in time.
Next-gpt: Any-to-any multimodal llm
Wu, S., Fei, H., Qu, L., Ji, W., and Chua, T.-S · 2023
Closest in time.
Learning concise and descriptive attributes for visual recognition
Yan, A., Wang, Y., Zhong, Y., Dong, C., He, Z., Lu, Y., Wang, W. Y., Shang, J., and McAuley, J · 2023
Closest in time.
Woodpecker: Hallucination correction for multimodal large language models
Yin, S., Fu, C., Zhao, S., Xu, T., Wang, H., Sui, D., Shen, Y., Li, K., Sun, X., and Chen, E · 2023
Closest in time.
Language to rewards for robotic skill synthesis
Yu, W., Gileadi, N., Fu, C., Kirmani, S., Lee, K.-H., Arenas, M. G., Chiang, H.-T. L., Erez, T., Hasenclever, L., Humplik, J., et al · 2023
Closest in time.
Graphtext: Graph reasoning in text space
Zhao, J., Zhuo, L., Shen, Y., Qu, M., Liu, K., Bronstein, M., Zhu, Z., and Tang, J · 2023
Closest in time.
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Zhu, D., Chen, J., Shen, X., Li, X., and Elhoseiny, M · 2023
Closest in time.