VIVA : A Benchmark for Vision-Grounded Decision-Making with Human Values (2024)

Abstract: This paper introduces VIVA, a benchmark for VIsion-grounded decision-making driven by human VAlues. While most large vision-language models (VLMs) focus on physical-level skills, our work is the first to examine their multimodal capabilities in leveraging human values to make decisions under a vision-depicted situation. VIVA contains 1,062 images depicting diverse real-world situations and the manually annotated decisions grounded in them. Given an image there, the model should select the most appropriate action to address the situation and provide the relevant human values and reason underlying the decision. Extensive experiments based on VIVA show the limitation of VLMs in using human values to make multimodal decisions. Further analyses indicate the potential benefits of exploiting action consequences and predicted human values.

## introduction imagine an elderly person falling on the ground, as in figure 1 : bystanders must recognize the fall (perception), assess the situation (reasoning and comprehension), and take decisive action by calling emergency services (action). similarly, if someone is seen struggling in the water, it is imperative to recognize their distress and respond promptly by providing assistance, such as locating and deploying a flotation device. these reflect human values -fundamental principles that guide how people evaluate situations and make decisions aimed at fostering a harmonious society by promoting the well- ). the best decision is in the blue box. meanwhile, recent large vision language models (vlms) have demonstrated remarkable intelligence and proficiency across diverse tasks @xcite . as vlm-powered intelligent agents become increasingly integrated into our daily lives, e.g., embodied robots, it presents a pressing need for vlms to gain human values for coexistence and collaboration between humans and future ai agents in society @xcite . for this reason, exploring vlms' abilities in making vital decisions with the consideration of society-level human values is an important criterion for progress toward artificial general intelligence (agi) @xcite . however, it is challenging for vlms to understand human values and make vision-grounded decisions accordingly because the task requires a deep, cross-modal comprehension of the scene and the underlying human values @xcite . for instance, viewing a person struggling in the water in figure 1 , the model must infer the potential risk of drowning and the urgency of assistance. here, a nuanced understanding of the situation (the person in distress) and human values (the duty to help others in need while maintaining personal safety) should jointly inform the best decision (employing a flotation device). given this challenge, we present viva, a pioneering benchmark aimed at evaluating the visiongrounded decision-making capabilities of vlms with human values for real-world scenarios. our benchmark targets fundamental scenarios where universal values are at stake, highlighting essential considerations in human-centered decision-making. although human values are gaining increasing attention in nlp communities, most work focuses on language-only scenarios @xcite , ignoring their impact in vision-grounded applications. moreover, most vlm studies center primarily on the physical-level capabilities @xcite @xcite . as a result, existing vlms may lack sufficient coverage of in-depth social-level reasoning and humancentered decision-making abilities. while @xcite examine the existence of ethical issues in images, viva covers a broader range of human values and takes a step further by incorporating these values into multimodal decision-making. to the best of our knowledge, our work is the first to explore multimodal decision-making with an awareness of human values. we present the first benchmark for this task with a comprehensive experimental study to assess the capabilities of vlms in predicting surface actions and underlying values in vision-depicted situations. the findings will provide valuable insights into the development of socially responsible and human-centered ai, which will be highly beneficial to the agi advancement. concretely, viva contains 1,240 images covering a broad spectrum of real-life situations pertinent to human values, e.g., providing assistance, handling emergencies, addressing social challenges, and safeguarding vulnerable populations. each image is meticulously annotated with potential courses of action, pertinent human values influencing decision-making, and accompanying reasons. building upon this dataset, we devise tasks structured at two levels. level-1: given an image depicting a situation, the model must select the most suitable action from distractions, demonstrating a nuanced understanding and reasoned analysis of the scenario. level-2: the model is prompted to articulate the underlying human values and reasons supporting the previously chosen action. our bench-level-1 task: action selection select the most appropriate course of initial action to take: a. providing them with additional items to put in the dishwasher to engage them in a helpful activity. b. encourage them to continue standing on the door for fun c. take a photo to record the cute moment. d. guide the child off the dishwasher door. e. no action is necessary given the situation depicted in the image. answer: d level-2 task: value inference ✅ responsibility for caregiving: intervening to protect the child from dangerous situations reflects a sense of duty. ❌ promotion of curiosity: encouraging children to explore can lead to positive learning experiences. ## data construction based on the task design in § 2, we construct our viva dataset through a multi-step annotation pipeline. it involves image collection, annotation of level-1 and level-2 tasks, and quality verification. the complete pipeline is depicted in figure 3 . ## conclusion this study presents viva, a pioneering benchmark crafted to evaluate vision-grounded decisionmaking in real-world situations with human values. our benchmark encompasses diverse real-life scenarios, featuring tasks structured at two levels: action selection within vision-grounded contexts and the subsequent inference of underlying values and reason. we conduct experiments with recent vlms and provide comprehensive analyses. the results reveal the ongoing challenge for current vlms in making reliable decisions while considering human values. moreover, the in-depth analysis shows that integrating the predicted action consequences and human values enhances decision-making efficacy.