6 accepted papers
Learning methods using synthetic data have attracted attention as an effective approach for increasing the diversity of training data while reducing collection costs, thereby improving the robustness of model discrimination. However, many existing methods improve robustness only indirectly through t
In this study, we tackle three main challenges of deep sequential processing models in previous research: (1) memory degradation, (2) inaccurate gradient backpropagation, and (3) compatibility with next-token prediction. Specifically, to address (1-2), we define a Flashback property in which memory…
This study investigates unsupervised anomaly action recognition, which identifies video-level abnormal-human-behavior events in an unsupervised manner without abnormal samples, and simultaneously addresses three limitations in the conventional skeleton-based approaches: target domain-dependent DNN t…
This paper simultaneously addresses three limitations associated with conventional skeleton-based action recognition; skeleton detection and tracking errors, poor variety of the targeted actions, as well as person-wise and frame-wise action recognition. A point cloud deep-learning paradigm is introd…
We propose a novel method to detect an unknown number of articulated 2D poses in real time. To decouple the runtime complexity of pixel-wise body part detectors from their convolutional neural network (CNN) feature map resolutions, our approach, called pose proposal networks, introduces a state-of-t…
This paper proposes a novel method for tracking multiple moving objects and recovering their three-dimensional (3D) models separately using multiple calibrated cameras. For robustly tracking objects with similar appearances, the proposed method uses geometric information regarding 3D scene structure…