Residual Attention: A Simple but Effective Method for Multi-Label Recognition K Zhu, J Wu Proceedings of the IEEE/CVF International Conference on Computer Vision, 184-193, 2021 | 153 | 2021 |
DTL: Disentangled Transfer Learning for Visual Recognition M Fu, K Zhu, J Wu Proceedings of the 38th AAAI Conference on Artificial Intelligence, 2024, 2023 | 6 | 2023 |
Multi-Label Self-Supervised Learning with Scene Images K Zhu, M Fu, J Wu Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 5 | 2023 |
Quantized Feature Distillation for Network Quantization K Zhu, YY He, J Wu Proceedings of the 37th AAAI Conference on Artificial Intelligence, 2023, 2023 | 5 | 2023 |
Low-rank Attention Side-Tuning for Parameter-Efficient Fine-Tuning N Tang, M Fu, K Zhu, J Wu arXiv preprint arXiv:2402.04009, 2024 | 2 | 2024 |
Coarse Is Better? A New Pipeline Towards Self-Supervised Learning with Uncurated Images K Zhu, YY He, J Wu arXiv preprint arXiv:2306.04244, 2023 | 2 | 2023 |
Instance-based Max-margin for Practical Few-shot Recognition M Fu, K Zhu* Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 2 | 2023 |
DiffuLT: How to Make Diffusion Model Useful for Long-tail Recognition J Shao, K Zhu, H Zhang, J Wu arXiv preprint arXiv:2403.05170, 2024 | 1 | 2024 |
Rectify the Regression Bias in Long-Tailed Object Detection K Zhu, M Fu, J Shao, T Liu, J Wu Accepted by ECCV2024, 2024 | 1 | 2024 |
Self-Supervised Visual Preference Alignment K Zhu, L Zhao, Z Ge, X Zhang Accepted by MM2024 (oral), 2024 | | 2024 |