most citedAttention-based Point Cloud Edge Sampling

3 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Comb, Prune, Distill: Towards Unified Pruning for Vision Model Compression

Jonas Schmitt, Ruiping Liu, Junwei Zheng +2

Lightweight and effective models are essential for devices with limited resources, such as intelligent vehicles. Structured pruning offers a promising approach to model compression…

cs.CV2024

Rethinking Attention Module Design for Point Cloud Analysis

Chengzhi Wu, Kaige Wang, Zeyun Zhong +5

In recent years, there have been significant advancements in applying attention mechanisms to point cloud analysis. However, attention module variants featured in various research…

cs.CV2024

Fourier Prompt Tuning for Modality-Incomplete Scene Segmentation

Ruiping Liu, Jiaming Zhang, Kunyu Peng +6

Integrating information from multiple modalities enhances the robustness of scene perception systems in autonomous vehicles, providing a more comprehensive and reliable sensory fra…

cs.CV20231 cited

Open Scene Understanding: Grounded Situation Recognition Meets Segment Anything for Helping People with Visual Impairments

Ruiping Liu, Jiaming Zhang, Kunyu Peng +5

Grounded Situation Recognition (GSR) is capable of recognizing and interpreting visual scenes in a contextually intuitive way, yielding salient activities (verbs) and the involved…

cs.CV20233 cited

Attention-based Point Cloud Edge Sampling

Chengzhi Wu, Junwei Zheng, Julius Pfrommer +1

Point cloud sampling is a less explored research topic for this data representation. The most commonly used sampling methods are still classical random sampling and farthest point…