Fetching the paper…
Reading the bibliography…
Samuel, A.L.: Some studies in machine learning using the game of checkers. IBM Journal of Research and Development 3
1959
Earlier work this paper cites.
Tesauro, G.: Temporal difference learning and td-gammon. Commun. ACM 38
1995
Earlier work this paper cites.
Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. In: Text summarization branches out. pp. 74–81 (2004)
2004
Earlier work this paper cites.
2006
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E.A., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML (2015)
2015
Earlier work this paper cites.
Kirkpatrick, J., Pascanu, R., Rabinowitz, N.C., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., Hadsell, R.: Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences 114
2016
Earlier work this paper cites.
Lu, C., Krishna, R., Bernstein, M., Fei-Fei, L.: Visual relationship detection with language priors. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. pp. 852–869. Springer (2016)
2016
Earlier work this paper cites.
Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D.: Mastering the game of Go with deep neural networks and tree search. Nature 529
2016
Earlier work this paper cites.
Krause, J., Johnson, J., Krishna, R., Fei-Fei, L.: A hierarchical approach for generating descriptive image paragraphs. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 317–325 (2017)
2017
Earlier work this paper cites.
Liu, S., Liang, Y., Gitter, A.: Loss-balanced task weighting to reduce negative transfer in multi-task learning. In: AAAI Conference on Artificial Intelligence (AAAI) (2019), https://api.semanticscholar.org/CorpusID:84836014
2019
Earlier work this paper cites.
Pont-Tuset, J., Uijlings, J., Changpinyo, S., Soricut, R., Ferrari, V.: Connecting vision and language with localized narratives. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part V 16. pp. 647–664. Springer (2020)
2020
Earlier work this paper cites.
Singh, S.P., Jaggi, M.: Model fusion via optimal transport. Conference on Neural Information Processing Systems (NeurIPS) (2020), https://api.semanticscholar.org/CorpusID:204512191
2020
Earlier work this paper cites.
Ho, J., Salimans, T.: Classifier-free diffusion guidance. In: NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications (2021), https://openreview.net/forum?id=qw8AKxfYbI
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Earlier work this paper cites.
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I.: Zero-shot text-to-image generation. In: International Conference on Machine Learning. pp. 8821–8831. PMLR (2021)
2021
Earlier work this paper cites.
Delmas, G., Weinzaepfel, P., Lucas, T., Moreno-Noguer, F., Rogez, G.: Posescript: 3d human poses from natural language. In: European Conference on Computer Vision. pp. 346–362. Springer (2022)
2022
Earlier work this paper cites.
Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. ICLR (2022)
2022
Earlier work this paper cites.
Li, C., Xu, H., Tian, J., Wang, W., Yan, M., Bi, B., Ye, J., Chen, H., Xu, G., Cao, Z., et al.: mplug: Effective and efficient vision-language learning by cross-modal skip-connections. ACL (2022)
2022
Earlier work this paper cites.
Li, J., Li, D., Xiong, C., Hoi, S.: Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International Conference on Machine Learning. pp. 12888–12900. PMLR (2022)
2022
Earlier work this paper cites.
Liu, N., Li, S., Du, Y., Torralba, A., Tenenbaum, J.B.: Compositional visual generation with composable diffusion models. In: European Conference on Computer Vision. pp. 423–439. Springer (2022)
2022
Earlier work this paper cites.
von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M., Wolf, T.: Diffusers: State-of-the-art diffusion models. https://github.com/huggingface/diffusers (2022)
2022
Earlier work this paper cites.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684–10695 (2022)
2022
Earlier work this paper cites.
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E.L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al.: Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems 35
2022
Earlier work this paper cites.
Ainsworth, S.K., Hayase, J., Srinivasa, S.S.: Git re-basin: Merging models modulo permutation symmetries. The International Conference on Learning Representations (ICLR) (2023), https://api.semanticscholar.org/CorpusID:252199400
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
Betker, J., Goh, G., Jing, L., Brooks, T., Wang, J., Li, L., Ouyang, L., Zhuang, J., Lee, J., Guo, Y., et al.: Improving image generation with better captions. Computer Science. https://cdn. openai. com/papers/dall-e-3. pdf 2
2023
Earlier work this paper cites.
Bitton-Guetta, N., Bitton, Y., Hessel, J., Schmidt, L., Elovici, Y., Stanovsky, G., Schwartz, R.: Breaking common sense: Whoops! a vision-and-language benchmark of synthetic and compositional images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2616–2627 (2023)
2023
Earlier work this paper cites.
2023
Cited alongside, same era.
Caffagni, D., Barraco, M., Cornia, M., Baraldi, L., Cucchiara, R.: Synthcap: Augmenting transformers with synthetic data for image captioning. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds.) Image Analysis and Processing – ICIAP 2023. pp. 112–123. Springer Nature Switzerland, Cham (2023)
2023
Cited alongside, same era.
Chang, H., Zhang, H., Barber, J., Maschinot, A., Lezama, J., Jiang, L., Yang, M.H., Murphy, K., Freeman, W.T., Rubinstein, M., et al.: Muse: Text-to-image generation via masked generative transformers. ICML (2023)
2023
Cited alongside, same era.
Chen, X., Wang, X., Changpinyo, S., Piergiovanni, A., Padlewski, P., Salz, D., Goodman, S., Grycner, A., Mustafa, B., Beyer, L., et al.: Pali: A jointly-scaled multilingual language-image model. ICLR (2023)
2023
Later among the works it cites.
Sung, Y.L., Li, L., Lin, K., Gan, Z., Bansal, M., Wang, L.: An empirical study of multimodal model merging. Empirical Methods in Natural Language Processing (Findings) (2023)
2023
Later among the works it cites.
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., Hashimoto, T.B.: Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/stanford_alpaca (2023)
2023
Later among the works it cites.
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…
2023
Cited alongside, same era.
Cho, J., Zala, A., Bansal, M.: Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models. In: ICCV (2023)
2023
Cited alongside, same era.
Cho, J., Zala, A., Bansal, M.: Visual programming for text-to-image generation and evaluation. In: NeurIPS (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Fan, Y., Watkins, O., Du, Y., Liu, H., Ryu, M., Boutilier, C., Abbeel, P., Ghavamzadeh, M., Lee, K., Lee, K.: Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models. In: NeurIPS (2023)
2023
Cited alongside, same era.
Feng, W., He, X., Fu, T.J., Jampani, V., Akula, A., Narayana, P., Basu, S., Wang, X.E., Wang, W.Y.: Training-free structured diffusion guidance for compositional text-to-image synthesis. ICLR (2023)
2023
Cited alongside, same era.
Hu, Y., Liu, B., Kasai, J., Wang, Y., Ostendorf, M., Krishna, R., Smith, N.A.: Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering. ICCV (2023)
2023
Cited alongside, same era.
Ilharco, G., Ribeiro, M.T., Wortsman, M., Gururangan, S., Schmidt, L., Hajishirzi, H., Farhadi, A.: Editing models with task arithmetic. The International Conference on Learning Representations (ICLR) (2023), https://api.semanticscholar.org/CorpusID:254408495
2023
Cited alongside, same era.
Turc, I., Nemade, G.: Midjourney user prompts & generated images (250k) (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Wang, Z.J., Montoya, E., Munechika, D., Yang, H., Hoover, B., Chau, D.H.: Diffusiondb: A large-scale prompt gallery dataset for text-to-image generative models. ACL (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Wu, X., Huang, S., Wei, F.: Mole: Mixture of lora experts. In: The Twelfth International Conference on Learning Representations (2023)
2023
Later among the works it cites.
Xie, J., Li, Y., Huang, Y., Liu, H., Zhang, W., Zheng, Y., Shou, M.Z.: Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7452–7461 (2023)
2023
Later among the works it cites.
Xu, J., Liu, X., Wu, Y., Tong, Y., Li, Q., Ding, M., Tang, J., Dong, Y.: Imagereward: Learning and evaluating human preferences for text-to-image generation. NeurIPS (2023)
2023
Later among the works it cites.
Yadav, P., Tam, D., Choshen, L., Raffel, C., Bansal, M.: Ties-merging: Resolving interference when merging models. In: Advances in Neural Information Processing Systems (NeurIPS) (2023)
2023
Later among the works it cites.
Yang, Z., Wang, J., Gan, Z., Li, L., Lin, K., Wu, C., Duan, N., Liu, Z., Liu, C., Zeng, M., et al.: Reco: Region-controlled text-to-image generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14246–14255 (2023)
2023
Later among the works it cites.
Yu, J., Xu, Y., Koh, J.Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B.K., et al.: Scaling autoregressive models for content-rich text-to-image generation. TMLR 2
2023
Later among the works it cites.
2023
Later among the works it cites.
Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3836–3847 (2023)
2023
Later among the works it cites.
Black, K., Janner, M., Du, Y., Kostrikov, I., Levine, S.: Training diffusion models with reinforcement learning. The International Conference on Learning Representations (ICLR) (2024)
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Cho, J., Hu, Y., Garg, R., Anderson, P., Krishna, R., Baldridge, J., Bansal, M., Pont-Tuset, J., Wang, S.: Davidsonian scene graph: Improving reliability in fine-grained evaluation for text-image generation. ICLR (2024)
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., Rombach, R.: Sdxl: Improving latent diffusion models for high-resolution image synthesis. ICLR (2024)
2024
Closest in time.
Rassin, R., Hirsch, E., Glickman, D., Ravfogel, S., Goldberg, Y., Chechik, G.: Linguistic binding in diffusion models: Enhancing attribute correspondence through attention map alignment. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Tuo, Y., Xiang, W., He, J.Y., Geng, Y., Xie, X.: Anytext: Multilingual visual text generation and editing. ICLR (2024)
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.