Navigating Hallucinations for Reasoning of Unintentional Activities (2024)

Abstract: In this work we present a novel task of understanding unintentional human activities in videos. We formalize this problem as a reasoning task under zero-shot scenario, where given a video of an unintentional activity we want to know why it transitioned from intentional to unintentional. We first evaluate the effectiveness of current state-of-the-art Large Multimodal Models on this reasoning task and observe that they suffer from hallucination. We further propose a novel prompting technique, termed as Dream of Thoughts (DoT), which allows the model to navigate through hallucinated thoughts to achieve better reasoning. To evaluate the performance on this task, we also introduce three different specialized metrics designed to quantify the models reasoning capability. We perform our experiments on three datasets, OOPs, UCF-Crimes, and ReUAct, and our findings show that DOT prompting technique is able to outperform standard prompting, while minimizing hallucinations.

## introduction automatic understanding of human activities in videos remains a challenging problem with significant real-world applications in fields such as healthcare, security, robotics, and elderly care. while recent advancements have been made in recognizing intentional human actions in videos @xcite , the recognition and understanding of unintentional activities are equally crucial @xcite . beyond mere recognition, understanding the reasoning behind these unintentional actions-specifically, identifying the point of failure-can help in mitigating or correcting mistakes. in this work, we focus on the task of understanding unintentional activities in videos. multimodal foundation models have demonstrated impressive zero-shot generalization across various tasks and domains @xcite @xcite . in this work, we investigate the reasoning capabilities of large multimodal models (lmms) concerning action intentionality. our analysis reveals that conventional prompting approaches often lead to hallucinations and struggle to accurately reason through transitions from intentional to unintentional actions. these models frequently produce generic explanations that fail to fully leverage the available visual context. although chain of thought prompting @xcite offers a structured approach for generating specific explanations, it too suffers from hallucinations when applied to the reasoning of unintentional actions. to address these challenges, we propose a multistep solution grounded in two key observations: (1) allowing the model to hallucinate multiple responses can sometimes generate correct answers, and (2) framing the task as a multiple-choice problem helps guide the model toward the correct reasoning. our method, dream of thoughts (dot) prompting, capitalizes on the model's hallucinations by treating them as multiple choices, thereby enabling the model to navigate through these options to arrive at more accurate conclusions. we conduct experiments on three datasets: oops @xcite , ucf-crimes @xcite , and reuact. the oops dataset focuses on unintentional activities in daily life, while ucf-crimes highlights anomalous events. to complement these, we introduce reuact, a new dataset that captures unintentional activities while minimizing overlap with pretraining datasets. our extensive evaluations demonstrate the effectiveness of dot prompting in improving reasoning over unintentional actions. we make the following contributions in this work, prompting techniques: advancements such as chain of thought (cot) prompting @xcite , automatic chain of thought @xcite , and self-consistent chain of thought @xcite have significantly improved llms' zero-shot performance. multimodal chain of thought @xcite extended this approach to incorporate both textual and visual data, while wang et al. @xcite refined cot with self-consistency. additional developments include the tree of thought @xcite and the graph of thought @xcite , which expanded on cot concepts. few-shot learning approaches, which incorporate examples, have also enhanced llm performance @xcite , with further improvements by @xcite @xcite . in our work, we analyze and compare lmm reasoning using these techniques alongside our proposed method. reasoning abilities of llm's: @xcite demonstrated that models like gpt-4 exhibit significant analogical reasoning abilities, while @xcite pointed out their limitations when dealing with out-of-distribution data and complex tasks. @xcite further analyzed deep models' analytical reasoning on raven's progressive matrices @xcite . in the visual question answering (vqa) domain, notable advancements have been made by studies such as @xcite , @xcite , @xcite , and @xcite , which have improved vqa methodologies. additionally, works like @xcite , @xcite , @xcite , @xcite , @xcite , @xcite , and @xcite have been instrumental in enhancing deep models' understanding of dynamic visual content. furthermore, studies such as @xcite , @xcite , @xcite , and @xcite have explored object reasoning in videos through grounding. to the best of our knowledge, no prior work has addressed lmms' ability to reason about unintentional activities in videos. hallucination in llm's: hallucination in foundational models refers to the generation of inconsistent or fabricated responses. @xcite explored the underlying causes of hallucinations in llms, while @xcite drew parallels between hallucinations and adversarial examples. @xcite extended these findings to large vision models (lvms). to tackle hallucination issues, approaches like self-checking and self-verification were introduced by @xcite and @xcite , aiming to ensure more consistent outputs. in this work, we take a different approach by leveraging hallucinations to enhance the model's reasoning capability through multi-step navigation. ## experiments datasets we performed our experiments on three different datasets, oops @xcite , ucf-crimes @xcite and reuact. oops: we conduct detailed experimental analysis using the validation subset of the oops dataset. this subset comprises 3,500 youtube videos, each portraying a variety of failures in diverse real-world scenarios. along with this, the oops dataset also contains natural language descriptions for each video. these descriptions provide insights into the original intentions behind the videos and the circumstances leading to the deviation from planned actions. ucf-crimes further, we also conduct experiments on ucf-crimes dataset. it consists of long and untrimmed real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery, etc. we use the validation set of this dataset to evaluate our approach, where we select only anomalous videos. these videos have length ranging from 1-3 minutes and there are a total of 65 videos in this evaluation set. we provide natural language descriptions for the crime occurring in the videos from this new test set to evaluate our approach. reuact: we also release a new dataset of recent youtube videos to avoid potential data leakage into the training datasets for large multimodal models. this dataset consists of 100 videos featuring actions failing for various reasons, similar to the oops dataset. baselines and models for the evaluation and benchmark, we utilize the officially released versions of several state-of-the-art models, namely video chatgpt @xcite , video llama @xcite , video chat @xcite , video llava @xcite , vila @xcite and video llamav2 @xcite . along with these video-based models, we also use image based model, open flamingo @xcite . these models serve as comprehensive baselines in our analysis. further, we also evaluate different prompting strategies including standard prompting, and the proposed dot prompt. each of these models is built upon the llama-7b billion language model, endowing them with substantial capabilities in text generation from video inputs. ## conclusion in this work, we present a novel task regarding understanding of unintentional activities in videos where we formalize it as a zero shot reasoning task. we first analyze the reasoning capabilities of existing lmm models and prompting techniques and then also propose a novel dot prompting technique which navigates through hallucinations introduced by llm's to obtain the reasoning. we propose different metrics to quantify the models performance and also analyze hallucinations of the responses. we further demonstrate that the proposed method outperforms existing prompting techniques.