SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization

TL;DR

A simple yet well-crafted framework is presented, which combines the perception capability of Vision Foundation Models (VFMs) and the reasoning capability of Large Language Models (LLMs) within a modular framework, providing a strong baseline for social relation recognition.

Abstract

Social relation reasoning aims to identify relation categories such as friends, spouses, and colleagues from images. While current methods adopt the paradigm of training a dedicated network end-to-end using labeled image data, they are limited in terms of generalizability and interpretability. To address these issues, we first present a simple yet well-crafted framework named {\name}, which combines the perception capability of Vision Foundation Models (VFMs) and the reasoning capability of Large Language Models (LLMs) within a modular framework, providing a strong baseline for social relation recognition. Specifically, we instruct VFMs to translate image content into a textual social story, and then utilize LLMs for text-based reasoning. {\name} introduces systematic design principles to adapt VFMs and LLMs separately and bridge their gaps. Without additional model training, it achieves competitive zero-shot results on two databases while offering interpretable answers, as LLMs can generate language-based explanations for the decisions. The manual prompt design process for LLMs at the reasoning phase is tedious and an automated prompt optimization method is desired. As we essentially convert a visual classification task into a generative task of LLMs, automatic prompt optimization encounters a unique long prompt optimization issue. To address this issue, we further propose the Greedy Segment Prompt Optimization (GSPO), which performs a greedy search by utilizing gradient information at the segment level. Experimental results show that GSPO significantly improves performance, and our method also generalizes to different image styles. The code is available at https://github.com/Mengzibin/SocialGPT.

Figures (11)

• Figure 1: (a) End-to-end learning-based framework for social relation reasoning. A dedicated neural network is trained end-to-end with full training data. (b) We propose a modular framework with foundation models for social relation reasoning. Our proposed SocialGPT first employs VFMs to extract visual information into textual format, and then perform text-based reasoning with LLMs, using either our manually designed SocialPrompt or optimized prompts.
• Figure 2: The framework of SocialGPT, which follows the "perception with VFMs, reasoning with LLMs" paradigm. SocialGPT converts an image into a social story in the perception phase, and then employs LLMs to generate explainable answers in the reasoning phase with SocialPrompt.
• Figure 3: An example of social story generation.
• Figure 4: Visualization results of interpretability. We show the SocialGPT perception and reasoning process. We see that our model predicts correct social relationships with plausible explanations.
• Figure 5: Results when applying SocialGPT to sketch and cartoon images. The images are generated by GPT-4V. Our method generalizes well on these novel image styles.