Deduce and Select Evidences with Language Models for Training-Free Video Goal Inference

We introduce ViDSE, a Video framework that Deduce and Selects visual Evidence for training-free video goal inference using language models. Unlike approaches that directly apply vision-language models (VLM) or combine VLM+LLM to process dense video visuals, ViDSE explicitly selects relevant visual evidence (e.g., frames) based on the hypothesis deduced by the LLM. This approach not only im-proves accuracy but also reveals the logical process behind the model's decisions, enhancing explainability. Our exper-iments demonstrate that this selection process significantly reduces ambiguity in the subsequent inference reasoning stage and outperforms VLM-only and VLM+LLM models on goal inference tasks such as CrossTask and COIN. We further validate ViDSE’ s generalizability and robustness on action recognition tasks, such as ActivityNet and UCF-101, under training-free and open-vocabulary conditions. We observe that ViDSE easily generalizes to other video tasks (e.g., action recognition) requiring filtering of redundant and irrelevant information.