Paper Summary27 Mesh Graphormer, ICCV’21 논문 링크 : https://openaccess.thecvf.com/content/ICCV2021/papers/Lin_Mesh_Graphormer_ICCV_2021_paper.pdf1. IntroductionTransformers are good at modeling long-range dependencies on the input tokens, but they are less efficient at capturing fine-grained local information.Convolution layers, on the other hand, are useful for extracting local features, but many layers are required to capture global con.. 2023. 3. 26. Actor-Transformers for Group Activity Recognition, CVPR’20 논문 링크 : https://openaccess.thecvf.com/content_CVPR_2020/papers/Gavrilyuk_Actor-Transformers_for_Group_Activity_Recognition_CVPR_2020_paper.pdf1. IntroductionWe hypothesize a transformer network can also better model relations between actors and combine actor-level information for group activity recognition compared to models that require explicit spatial and temporal constraints.A key enabler is.. 2023. 3. 26. End-to-end Recovery of Human Shape and Pose, CVPR’18 논문 링크 : https://openaccess.thecvf.com/content_cvpr_2018/papers/Kanazawa_End-to-End_Recovery_of_CVPR_2018_paper.pdf1. IntroductionWe present an end-to-end framework for recovering a full 3D mesh of a human body from a single RGB image. We use the generative human body model, SMPL, which parameterizes the mesh by 3D joint angles and a low-dimensional linear shape space.A key insight is that there .. 2023. 3. 26. 이전 1 ··· 4 5 6 7 다음