GRITv2: Efficient and Light-weight Social Relation Recognition
<|reference_start|>GRITv2: Efficient and Light-weight Social Relation Recognition: 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.<|reference_end|>
@article{reddy2024gritv2:,
    title={GRITv2: Efficient and Light-weight Social Relation Recognition},
    author={N K Sagar Reddy, Neeraj Kasera, Avinash Thakur},
    journal={arXiv preprint arXiv:2403.06895},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06895},
    primaryClass={cs.CV}
}