EditAR: Unified Conditional Generation with Autoregressive Models
Jiteng Mu, Nuno Vasconcelos, Xiaolong Wang
2025年份
9顶会引用
摘要
Change the horse to golden. Change to Cartoon. Remove the bee over the flowers. Change sky to sunset. Change the cat to tiger.
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引用它的顶会 Paper9
- Visual Autoregressive Modeling for Instruction-Guided Image EditingQingyang Mao, Qi Cai, Yehao Li, Yingwei Pan 等ICLR 2026 · 被引用 21 次
- EditMGT: Unleashing Potentials of Masked Generative Transformers in Image EditingWei Chow, Linfeng Li, Lingdong Kong, Zefeng Li 等CVPR 2026 · 被引用 14 次
- Semantic Context Matters: Improving Conditioning for Autoregressive ModelsDongyang Jin, Ryan Xu, Jianhao Zeng, Rui Lan 等CVPR 2026 · 被引用 12 次
- NEP: Autoregressive Image Editing via Next Editing Token PredictionHuimin Wu, Xiaojian (Shawn) Ma, Haozhe Zhao, Yanpeng Zhao 等NeurIPS 2025 · 被引用 8 次
- OmniGen-AR: AutoRegressive Any-to-Image GenerationJunke Wang, Xun Wang, Qiushan Guo, Peize Sun 等NeurIPS 2025 · 被引用 7 次
它引用的顶会 Paper45
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 被引用 64,255 次
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 被引用 35,902 次
- Training language models to follow instructions with human feedbackLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida 等NeurIPS 2022 · 被引用 24,707 次
- Diffusion Models Beat GANs on Image SynthesisPrafulla Dhariwal, Alexander Quinn NicholNeurIPS 2021 · 被引用 13,211 次
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