278 citations · 1.7k across the 69 of their papers we have counts for
9 papers · 2 filters
MM-NeRF: Multimodal-Guided 3D Multi-Style Transfer of Neural Radiance Field
Zijiang Yang, Zhongwei Qiu, Chang Xu +1
3D style transfer aims to generate stylized views of 3D scenes with specified styles, which requires high-quality generating and keeping multi-view consistency. Existing methods st…
Efficient Transfer Learning in Diffusion Models via Adversarial Noise
Xiyu Wang, Baijiong Lin, Daochang Liu +1
Diffusion Probabilistic Models (DPMs) have demonstrated substantial promise in image generation tasks but heavily rely on the availability of large amounts of training data. Previo…
Boosting Diffusion Models with an Adaptive Momentum Sampler
Xiyu Wang, Anh-Dung Dinh, Daochang Liu +1
Diffusion probabilistic models (DPMs) have been shown to generate high-quality images without the need for delicate adversarial training. However, the current sampling process in D…
What Can Simple Arithmetic Operations Do for Temporal Modeling?
Wenhao Wu, Yuxin Song, Zhun Sun +3
Temporal modeling plays a crucial role in understanding video content. To tackle this problem, previous studies built complicated temporal relations through time sequence thanks to…
Model Synthesis for Zero-Shot Model Attribution
Tianyun Yang, Juan Cao, Danding Wang +1
Nowadays, generative models are shaping various fields such as art, design, and human-computer interaction, yet accompanied by challenges related to copyright infringement and cont…
Learning Structure-Guided Diffusion Model for 2D Human Pose Estimation
Zhongwei Qiu, Qiansheng Yang, Jian Wang +7
One of the mainstream schemes for 2D human pose estimation (HPE) is learning keypoints heatmaps by a neural network. Existing methods typically improve the quality of heatmaps by c…
Stable Diffusion is Unstable
Chengbin Du, Yanxi Li, Zhongwei Qiu +1
Recently, text-to-image models have been thriving. Despite their powerful generative capacity, our research has uncovered a lack of robustness in this generation process. Specifica…
VanillaKD: Revisit the Power of Vanilla Knowledge Distillation from Small Scale to Large Scale
Zhiwei Hao, Jianyuan Guo, Kai Han +3
The tremendous success of large models trained on extensive datasets demonstrates that scale is a key ingredient in achieving superior results. Therefore, the reflection on the rat…
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense
Zunzhi You, Daochang Liu, Bohyung Han +1
Recent advancements in masked image modeling (MIM) have made it a prevailing framework for self-supervised visual representation learning. The MIM pretrained models, like most deep…