In this paper we present SA-CNN, a hierarchical and lightweight self-attention based encoding and decoding architecture for representation learning of point cloud data. The proposed SA-CNN introduces convolution and transposed convolution stacks to capture and generate contextual information among unordered 3D points. Following conventional hierarchical pipeline, the encoding process extracts feature in local-to-global manner, while the decoding process generates feature and point cloud in coarse-to-fine, multi-resolution stages. We demonstrate that SA-CNN is capable of a wide range of applications, namely classification, part segmentation, reconstruction, shape retrieval, and unsupervised classification. While achieving the state-of-the-art or comparable performance in the benchmarks, SA-CNN maintains its model complexity several order of magnitude lower than the others. In term of qualitative results, we visualize the multi-stage point cloud reconstructions and latent walks on rigid objects as well as deformable non-rigid human and robot models.
Sparse-View Interpretable 3D Animal Behavior Representations for Neural Encoding and Decoding
Harvard University（哈佛大学）；University of Cambridge（剑桥大学）；Northwestern University（西北大学）；Cold Spring Harbor Laboratory（冷泉港实验室）；Columbia University（哥伦比亚大学）；University of Science and Technology of China（中国科学技术大学）
PIC 4th Challenge: Semantic-Assisted Multi-Feature Encoding and Multi-Head Decoding for Dense Video Captioning
University of Chinese Academy of Sciences（中国科学院大学）；CASIA（中国科学院自动化研究所）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks
Department of Advanced Computing Sciences (DACS), University of Maastricht (UM)（先进计算科学系（DACS）、马斯特里赫特大学（UM））；Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH)（信息科技研究所（ITI）、希腊研究中心与技术中心（CERTH））
EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification
FPT Software AI Center（FPT软件人工智能中心）；John von Neumann Institute, Vietnam National University（越南国家大学冯·诺依曼研究院）；My Duc Phu Nhuan Hospital（美德富润医院）；Vinmec Central Park International Hospital（Vinmec中央公园国际医院）；Arkansas Economic Development Commission（阿肯色州经济发展委员会）；University of Science, VNU-HCM（越南国家大学胡志明市分校科学大学）；University of Arkansas（阿肯色大学）；My Duc Hospital（美德医院）
Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)
Inria Bordeaux（法国国家信息与自动化研究所波尔多分所）；LaBRI, Université de Bordeaux（波尔多大学拉布里实验室）；Université de Bordeaux（波尔多大学）；TU Berlin（柏林工业大学）；National University of Singapore（新加坡国立大学）；Microsoft（微软公司）；International Institute of Information Technology Hyderabad（海得拉巴国际信息技术研究所）；CEA（法国原子能和替代能源委员会）；CNRS（法国国家科学研究中心）
