# LIGHTEN: Learning Interactions with Graph and Hierarchical TEmporal Networks for HOI in videos

arXiv: https://arxiv.org/abs/2012.09402 · HTML: https://mlsanity.com/paper/2012.09402/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 14 of 14)

- [Select, Substitute, Search: A New Benchmark for Knowledge-Augmented Visual Question Answering](https://mlsanity.com/paper/2103.05568/) (2021)
- [ST-HOI: A Spatial-Temporal Baseline for Human-Object Interaction Detection in Videos](https://mlsanity.com/paper/2105.11731/) (2021)
- [Social Fabric: Tubelet Compositions for Video Relation Detection](https://mlsanity.com/paper/2108.08363/) (2021)
- [Video-based Human-Object Interaction Detection from Tubelet Tokens](https://mlsanity.com/paper/2206.01908/) (2022)
- [Human-Object Interaction Prediction in Videos through Gaze Following](https://mlsanity.com/paper/2306.03597/) (2023)
- [Spatio-Temporal Interaction Graph Parsing Networks for Human-Object Interaction Recognition](https://mlsanity.com/paper/2108.08633/) (2021)
- [Enhancing Human-Centered Dynamic Scene Understanding via Multiple LLMs Collaborated Reasoning](https://mlsanity.com/paper/2403.10107/) (2024)
- [From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos](https://mlsanity.com/paper/2407.00917/) (2024)
- [VHOI: Controllable Video Generation of Human-Object Interactions from Sparse Trajectories via Motion Densification](https://mlsanity.com/paper/2512.09646/) (2025)
- [Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph Generation](https://mlsanity.com/paper/2306.10122/) (2023)
- [IMPACT-HOI: Supervisory Control for Onset-Anchored Partial HOI Event Construction](https://mlsanity.com/paper/2605.01666/) (2026)
- [Distillation of Human-Object Interaction Contexts for Action Recognition](https://mlsanity.com/paper/2112.09448/) (2021)
- [Spatial Parsing and Dynamic Temporal Pooling networks for Human-Object Interaction detection](https://mlsanity.com/paper/2206.03061/) (2022)
- [Prototype Embedding Optimization for Human-Object Interaction Detection in Livestreaming](https://mlsanity.com/paper/2505.22011/) (2025)

## Abstract (arXiv)

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