| A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping D Li, H Wu, J Zhang, K Huang Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 148 | 2018 |
| Universal adversarial perturbations against object detection D Li, J Zhang, K Huang Pattern Recognition 110, 107584, 2021 | 64 | 2021 |
| Fast a3rl: Aesthetics-aware adversarial reinforcement learning for image cropping D Li, H Wu, J Zhang, K Huang IEEE Transactions on Image Processing 28 (10), 5105-5120, 2019 | 50 | 2019 |
| Composing good shots by exploiting mutual relations D Li, J Zhang, K Huang, MH Yang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 39 | 2020 |
| Learning to learn cropping models for different aspect ratio requirements D Li, J Zhang, K Huang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 25 | 2020 |
| Diffusion-rwkv: Scaling rwkv-like architectures for diffusion models Z Fei, M Fan, C Yu, D Li, J Huang arXiv preprint arXiv:2404.04478, 2024 | 22 | 2024 |
| Dimba: Transformer-mamba diffusion models Z Fei, M Fan, C Yu, D Li, Y Zhang, J Huang arXiv preprint arXiv:2406.01159, 2024 | 20 | 2024 |
| Scaling diffusion transformers to 16 billion parameters Z Fei, M Fan, C Yu, D Li, J Huang arXiv preprint arXiv:2407.11633, 2024 | 11 | 2024 |
| Video Diffusion Transformers are In-Context Learners Z Fei, D Qiu, C Yu, D Li, M Fan, X Wen arXiv preprint arXiv:2412.10783, 2024 | 2 | 2024 |
| OTAMatch: Optimal Transport Assignment With PseudoNCE for Semi-Supervised Learning J Zhang, J Liu, D Li, Q Huang, J Chen, D Huang IEEE Transactions on Image Processing, 2024 | 2 | 2024 |
| Ingredients: Blending Custom Photos with Video Diffusion Transformers Z Fei, D Li, D Qiu, C Yu, M Fan arXiv preprint arXiv:2501.01790, 2025 | 1 | 2025 |
Junge ZHANGNational Laboratory of Pattern Recognition, CASIA在 nlpr.ia.ac.cn 的电子邮件经过验证
