3 citations · 9 across the 10 of their papers we have counts for
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Images Speak Louder Than Scores: Failure Mode Escape for Enhancing Generative Quality
Jie Shao, Ke Zhu, Minghao Fu +2
Diffusion models have achieved remarkable progress in class-to-image generation. However, we observe that despite impressive FID scores, state-of-the-art models often generate dist…
QwT-v2: Practical, Effective and Efficient Post-Training Quantization
Ningyuan Tang, Minghao Fu, Hao Yu +1
Network quantization is arguably one of the most practical network compression approaches for reducing the enormous resource consumption of modern deep neural networks. They usuall…
Quantization without Tears
Minghao Fu, Hao Yu, Jie Shao +3
Deep neural networks, while achieving remarkable success across diverse tasks, demand significant resources, including computation, GPU memory, bandwidth, storage, and energy. Netw…
Minimal Interaction Separated Tuning: A New Paradigm for Visual Adaptation
Ningyuan Tang, Minghao Fu, Jianxin Wu
The rapid scaling of large vision pretrained models makes fine-tuning tasks more and more difficult on devices with low computational resources. We explore a new visual adaptation…
Low-rank Attention Side-Tuning for Parameter-Efficient Fine-Tuning
Ningyuan Tang, Minghao Fu, Ke Zhu +1
In finetuning a large pretrained model to downstream tasks, parameter-efficient fine-tuning (PEFT) methods can effectively finetune pretrained models with few trainable parameters,…
Rectify the Regression Bias in Long-Tailed Object Detection
Ke Zhu, Minghao Fu, Jie Shao +2
Long-tailed object detection faces great challenges because of its extremely imbalanced class distribution. Recent methods mainly focus on the classification bias and its loss func…