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Textural Inversion, a prompt learning method, learns a singular text embedding for a new "word" to represent image style and appearance, allowing it to be integrated into natural language sentences to generate novel synthesised images.
Visualizing data using t-sne
Van der Maaten, L. and Hinton, G · 2008
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Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
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The multimodal brain tumor image segmentation benchmark (brats)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al · 2014
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Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late gadolinium enhancement mr images
Karim, R., Bhagirath, P., Claus, P., Housden, R. J., Chen, Z., Karimaghaloo, Z., Sohn, H.-M., Rodríguez, L. L., Vera, S., Albà, X., et al · 2016
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Scene parsing through ade20k dataset
Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., and Torralba, A · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports
Johnson, A. E., Pollard, T. J., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-y., Mark, R. G., and Horng, S · 2019
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Emidec: a database usable for the automatic evaluation of myocardial infarction from delayed-enhancement cardiac mri
Lalande, A., Chen, Z., Decourselle, T., Qayyum, A., Pommier, T., Lorgis, L., de La Rosa, E., Cochet, A., Cottin, Y., Ginhac, D., et al · 2020
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Stylespace analysis: Disentangled controls for stylegan image generation, 2020
Wu, Z., Lischinski, D., and Shechtman, E · 2020
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Emerging properties in self-supervised vision transformers, 2021
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
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Per-pixel classification is not all you need for semantic segmentation
Cheng, B., Schwing, A., and Kirillov, A · 2021
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Learning transferable visual models from natural language supervision, 2021
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
Cited alongside, same era.
The medical segmentation decathlon
Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., et al · 2022
Cited alongside, same era.
An image is worth one word: Personalizing text-to-image generation using textual inversion, 2022
Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A. H., Chechik, G., and Cohen-Or, D · 2022
Cited alongside, same era.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al · 2023
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Multi-concept customization of text-to-image diffusion
Kumari, N., Zhang, B., Zhang, R., Shechtman, E., and Zhu, J.-Y · 2023
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Cones: Concept neurons in diffusion models for customized generation
Liu, Z., Feng, R., Zhu, K., Zhang, Y., Zheng, K., Liu, Y., Zhao, D., Zhou, J., and Cao, Y · 2023
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Segment anything in medical images
Ma, J. and Wang, B · 2023
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Dinov2: Learning robust visual features without supervision
Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al · 2023
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Prompt-to-prompt image editing with cross attention control
Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., and Cohen-Or, D · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., and Aberman, K · 2022
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Scientific Discovery
Schickore, J · 2022
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Break-a-scene: Extracting multiple concepts from a single image
Avrahami, O., Aberman, K., Fried, O., Cohen-Or, D., and Lischinski, D · 2023
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Svdiff: Compact parameter space for diffusion fine-tuning
Han, L., Li, Y., Zhang, H., Milanfar, P., Metaxas, D., and Yang, F · 2023
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Localizing object-level shape variations with text-to-image diffusion models, 2023
Patashnik, O., Garibi, D., Azuri, I., Averbuch-Elor, H., and Cohen-Or, D · 2023
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Key-locked rank one editing for text-to-image personalization
Tewel, Y., Gal, R., Chechik, G., and Atzmon, Y · 2023
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Plug-and-play diffusion features for text-driven image-to-image translation
Tumanyan, N., Geyer, M., Bagon, S., and Dekel, T · 2023
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Concept decomposition for visual exploration and inspiration
Vinker, Y., Voynov, A., Cohen-Or, D., and Shamir, A · 2023
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Scientific discovery in the age of artificial intelligence
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al · 2023
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Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation
Wei, Y., Zhang, Y., Ji, Z., Bai, J., Zhang, L., and Zuo, W · 2023
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