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20232026
most citedAttention-based Point Cloud Edge Sampling

3 citations · 14 across the 52 of their papers we have counts for

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Showing 2023 · cs.CVShow all

6 papers · 2 filters

cs.CV2023★ 2 cited

Navigating Open Set Scenarios for Skeleton-based Action Recognition

Kunyu Peng, Cheng Yin, Junwei Zheng +7

In real-world scenarios, human actions often fall outside the distribution of training data, making it crucial for models to recognize known actions and reject unknown ones. Howeve…

cs.CV2023

Exploring Self-supervised Skeleton-based Action Recognition in Occluded Environments

Yifei Chen, Kunyu Peng, Alina Roitberg +7

To integrate action recognition into autonomous robotic systems, it is essential to address challenges such as person occlusions-a common yet often overlooked scenario in existing…

cs.CV2023

Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision

Yiping Wei, Kunyu Peng, Alina Roitberg +6

Self-supervised representation learning for human action recognition has developed rapidly in recent years. Most of the existing works are based on skeleton data while using a mult…

cs.CV2023★ 1 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.CV2023

Attention-based Part Assembly for 3D Volumetric Shape Modeling

Chengzhi Wu, Junwei Zheng, Julius Pfrommer +1

Modeling a 3D volumetric shape as an assembly of decomposed shape parts is much more challenging, but semantically more valuable than direct reconstruction from a full shape repres…

cs.CV2023★ 3 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…