# GRITv2: Efficient and Light-weight Social Relation Recognition

arXiv: https://arxiv.org/abs/2403.06895 · HTML: https://mlsanity.com/paper/2403.06895/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 28 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [TinyViT: Fast Pretraining Distillation for Small Vision Transformers](https://mlsanity.com/paper/2207.10666/) (2022)
- [Understanding and Overcoming the Challenges of Efficient Transformer Quantization](https://mlsanity.com/paper/2109.12948/) (2021)
- [MiniViT: Compressing Vision Transformers with Weight Multiplexing](https://mlsanity.com/paper/2204.07154/) (2022)
- [Contrastive Learning for Image Captioning](https://mlsanity.com/paper/1710.02534/) (2017)
- [Deep Reasoning with Knowledge Graph for Social Relationship Understanding](https://mlsanity.com/paper/1807.00504/) (2018)
- [Dual-Glance Model for Deciphering Social Relationships](https://mlsanity.com/paper/1708.00634/) (2017)
- [Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues](https://mlsanity.com/paper/1501.05703/) (2015)
- [Graph-Based Social Relation Reasoning](https://mlsanity.com/paper/2007.07453/) (2020)
- [An End-to-End Network for Generating Social Relationship Graphs](https://mlsanity.com/paper/1903.09784/) (2019)
- [Multi-Granularity Reasoning for Social Relation Recognition from Images](https://mlsanity.com/paper/1901.03067/) (2019)

## Abstract (arXiv)

Our research focuses on the analysis and improvement of the Graph-based Relation Inference Transformer (GRIT), which serves as an important benchmark in the field. We conduct a comprehensive ablation study using the PISC-fine dataset, to find and explore improvement in efficiency and performance of GRITv2. Our research has provided a new state-of-the-art relation recognition model on the PISC relation dataset. We introduce several features in the GRIT model and analyse our new benchmarks in two versions: GRITv2-L (large) and GRITv2-S (small). Our proposed GRITv2-L surpasses existing methods on relation recognition and the GRITv2-S is within 2% performance gap of GRITv2-L, which has only 0.0625x the model size and parameters of GRITv2-L. Furthermore, we also address the need for model compression, an area crucial for deploying efficient models on resource-constrained platforms. By applying quantization techniques, we efficiently reduced the GRITv2-S size to 22MB and deployed it on the flagship OnePlus 12 mobile which still surpasses the PISC-fine benchmarks in performance, highlighting the practical viability and improved efficiency of our model on mobile devices.