Recent progress in video-text retrieval has been driven largely by advancements in model architectures and training strategies. However, the representation learning capabilities of video-text retrieval models remain constrained by low-quality and limited training data annotations. To address this issue, we present a novel Video-Text Retrieval Paradigm with Relevance-based Augmentation, namely dReAm, which enhances video and text data using large foundation models to learn more generalized features. Specifically, we first adopt a simple augmentation method, which generates self-similar data by randomly duplicating or dropping subwords and frames. In addition, inspired by the recent advancement in visual and language generative models, we propose a more robust augmentation method through textual paraphrasing and video stylization using large language models (LLMs) and visual generative models (VGMs). To further enrich video and text information, we propose a relevance-based augmentation method, where LLMs and VGMs generate and integrate new relevant information into the original data. Leveraging this enriched data, extensive experiments on several video-text retrieval benchmarks demonstrate the superiority of dReAm over existing methods. Code will be available upon acceptance.

Robots are often deployed in remote locations for tasks such as exploration, where users cannot directly perceive the agent and its environment. For Human-In-The-Loop applications, operators must have a comprehensive understanding of the robot’s current state and its environment to take necessary actions and effectively assist the agent. In this work, we compare different explanation styles to determine the most effective way to convey real-time updates to users. Additionally, we formulate these explanation styles as separate fine-tuning tasks and assess the effectiveness of large language models in delivering in-mission updates to maintain situation awareness. The code and dataset for this work are available at:———

This paper presents a study to understand the issues related to using NLG to humanise explanations from a popular interpretable machine learning framework called LIME. Our study shows that self-reported rating of NLG explanation was higher than that for a non-NLG explanation. However, when tested for comprehension, the results were not as clear-cut showing the need for performing more studies to uncover the factors responsible for high-quality NLG explanations.