Here, we show that human-object interactions can be seen as trajectories lying on a low-dimensional subspace, and which can in turn be recovered by subspace ...

Abstract. In this paper, we approach the problem of recognizing human- object interactions from video data. Using only motion trajectories as in-.

We developed a compositional learning model to recognize seen and unseen human-object interactions by learning and leveraging their spatial relations. We ...

Apr 1, 2022 · In this paper, we approach the problem of recognizing human-object interactions from video data. Using only motion trajectories as input, we ...

We propose a method based on sparse representation (SR) to cluster data drawn from multiple low-dimensional linear or affine subspaces embedded in a ...

Jun 21, 2020 · Abstract—This paper tackles the problem of human action recognition, defined as classifying which action is displayed in.

In this paper, we propose a new hierarchical sparse subspace-based clustering algorithm (HESSC), which handles the aforementioned problems in a robust and fast ...

Bogun, Ivan, and Eraldo Ribeiro. "Recognizing Human-Object Interactions Using Sparse Subspace Clustering." In Computer Analysis of Images and Patterns, 409–16.

Subspace clustering refers to the task of separating the high-dimensional data into multiple low-dimensional subspaces according to their latent common patterns ...

Abstract. This paper proposes a novel Subdivision-Fusion Model (SFM) to recognize human actions. In most action recognition tasks, overlapping feature ...