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20222026
most citedMulti-direction and Multi-scale Pyramid in Transformer for Video-based Pedestrian Retrieval

99 citations · 126 across the 6 of their papers we have counts for

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7 papers · 1 filter

cs.CV202622 cited

Overview and Comparison of AVS Point Cloud Compression Standard

Wei Gao, Wenxu Gao, Xingming Mu +2

Point cloud is a prevalent 3D data representation format with significant application values in immersive media, autonomous driving, digital heritage protection, etc. However, the…

cs.CV2024

Stochasticity-aware No-Reference Point Cloud Quality Assessment

Songlin Fan, Wei Gao, Zhineng Chen +3

The evolution of point cloud processing algorithms necessitates an accurate assessment for their quality. Previous works consistently regard point cloud quality assessment (PCQA) a…

cs.CV2023

Point Cloud Semantic Segmentation with Sparse and Inhomogeneous Annotations

Zhiyi Pan, Nan Zhang, Wei Gao +2

Utilizing uniformly distributed sparse annotations, weakly supervised learning alleviates the heavy reliance on fine-grained annotations in point cloud semantic segmentation tasks.…

cs.CV2023

Mug-STAN: Adapting Image-Language Pretrained Models for General Video Understanding

Ruyang Liu, Jingjia Huang, Wei Gao +2

Large-scale image-language pretrained models, e.g., CLIP, have demonstrated remarkable proficiency in acquiring general multi-modal knowledge through web-scale image-text data. Des…

cs.CV2022

Deep Geometry Post-Processing for Decompressed Point Clouds

Xiaoqing Fan, Ge Li, Dingquan Li +3

Point cloud compression plays a crucial role in reducing the huge cost of data storage and transmission. However, distortions can be introduced into the decompressed point clouds d…

cs.CV20225 cited

Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation

Wenbo Zhao, Xianming Liu, Zhiwei Zhong +4

Point clouds upsampling is a challenging issue to generate dense and uniform point clouds from the given sparse input. Most existing methods either take the end-to-end supervised l…