  Analyzing the interactions between humans and objects from a video includes
identification of the relationships between humans and the objects present in
the video. It can be thought of as a specialized version of Visual Relationship
Detection, wherein one of the objects must be a human. While traditional
methods formulate the problem as inference on a sequence of video segments, we
present a hierarchical approach, LIGHTEN, to learn visual features to
effectively capture spatio-temporal cues at multiple granularities in a video.
Unlike current approaches, LIGHTEN avoids using ground truth data like depth
maps or 3D human pose, thus increasing generalization across non-RGBD datasets
as well. Furthermore, we achieve the same using only the visual features,
instead of the commonly used hand-crafted spatial features. We achieve
state-of-the-art results in human-object interaction detection (88.9% and
92.6%) and anticipation tasks of CAD-120 and competitive results on image based
HOI detection in V-COCO dataset, setting a new benchmark for visual features
based approaches. Code for LIGHTEN is available at
https://github.com/praneeth11009/LIGHTEN-Learning-Interactions-with-Graphs-and-Hierarchical-TEmporal-Networks-for-HOI
