# Care about you: towards large-scale human-centric visual relationship detection

arXiv: https://arxiv.org/abs/1705.09892 · HTML: https://mlsanity.com/paper/1705.09892/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2017.

## Built on by (most cited first, 6 of 6)

- [HAKE: A Knowledge Engine Foundation for Human Activity Understanding](https://mlsanity.com/paper/2202.06851/) (2022)
- [Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos](https://mlsanity.com/paper/2207.09425/) (2022)
- [Reconstructing In-the-Wild Open-Vocabulary Human-Object Interactions](https://mlsanity.com/paper/2503.15898/) (2025)
- [2.5D Visual Relationship Detection](https://mlsanity.com/paper/2104.12727/) (2021)
- [Transferable Interactiveness Knowledge for Human-Object Interaction Detection](https://mlsanity.com/paper/2101.10292/) (2021)
- [Spatial Parsing and Dynamic Temporal Pooling networks for Human-Object Interaction detection](https://mlsanity.com/paper/2206.03061/) (2022)

## Abstract (arXiv)

Visual relationship detection aims to capture interactions between pairs of objects in images. Relationships between objects and humans represent a particularly important subset of this problem, with implications for challenges such as understanding human behaviour, and identifying affordances, amongst others. In addressing this problem we first construct a large-scale human-centric visual relationship detection dataset (HCVRD), which provides many more types of relationship annotation (nearly 10K categories) than the previous released datasets. This large label space better reflects the reality of human-object interactions, but gives rise to a long-tail distribution problem, which in turn demands a zero-shot approach to labels appearing only in the test set. This is the first time this issue has been addressed. We propose a webly-supervised approach to these problems and demonstrate that the proposed model provides a strong baseline on our HCVRD dataset.