30 citations · 85 across the 10 of their papers we have counts for
5 papers · 1 filter
PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter
Yaohua Zha, Yanzi Wang, Hang Guo +7
Applying pre-trained models to assist point cloud understanding has recently become a mainstream paradigm in 3D perception. However, existing application strategies are straightfor…
ReFIR: Grounding Large Restoration Models with Retrieval Augmentation
Hang Guo, Tao Dai, Zhihao Ouyang +4
Recent advances in diffusion-based Large Restoration Models (LRMs) have significantly improved photo-realistic image restoration by leveraging the internal knowledge embedded withi…
LCM: Locally Constrained Compact Point Cloud Model for Masked Point Modeling
Yaohua Zha, Naiqi Li, Yanzi Wang +6
The pre-trained point cloud model based on Masked Point Modeling (MPM) has exhibited substantial improvements across various tasks. However, these models heavily rely on the Transf…
MambaIR: A Simple Baseline for Image Restoration with State-Space Model
Hang Guo, Jinmin Li, Tao Dai +3
Recent years have seen significant advancements in image restoration, largely attributed to the development of modern deep neural networks, such as CNNs and Transformers. However,…
Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters
Haoyu Liang, Zhihao Ouyang, Yuyuan Zeng +5
Convolutional neural networks (CNNs) have been successfully used in a range of tasks. However, CNNs are often viewed as "black-box" and lack of interpretability. One main reason is…