분류 전체보기28 Learning Actor Relation Graphs for Group Activity Recognition, CVPR’19 논문 링크 : https://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_Learning_Actor_Relation_Graphs_for_Group_Activity_Recognition_CVPR_2019_paper.pdf1. IntroductionTo understand the scene of multiple persons, the model needs to not only describe the individual action of each actor in the context, but also infer their collective activity.However, modeling the relation between actors is challenging,.. 2023. 3. 26. 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
