Recent advances in video-text retrieval (VTR) have largely relied on supervised learning and fine-tuning. In this paper, we introduce , a novel zero-shot VTR framework that leverages off-the-shelf video captioners, large language models (LLMs), and text retrieval methods—entirely without additional training or annotated data. Due to the limited power of captioning methods, the captions often miss important content in the video, resulting in unsatisfactory retrieval performance. To translate more information into video captions, we first generates initial captions for videos, then enhances them using a relevance-boosted captioning strategy powered by LLMs, enriching video descriptions with salient details. To further emphasize key content, we propose structural information extraction, organizing visual elements such as objects, events, and attributes into structured templates, further boosting the retrieval performance. Benefiting from the enriched captions and structuralized information, extensive experiments on several video-text retrieval benchmarks demonstrate the superiority of over existing fine-tuned and pretraining methods without any data. They also show that the enriched captions capture key details from the video with minimal noise. Code and data will be released to facilitate future research.

Layout detection is an essential step for accurately extracting structured contents from historical documents. The intricate and varied layouts present in these document images make it expensive to label the numerous layout regions that can be densely arranged on each page. Current active learning methods typically rank and label samples at the image level, where the annotation budget is not optimally spent due to the overexposure of common objects per image. Inspired by recent progress in semi-supervised learning and self-training, we propose OLALA, an Object-Level Active Learning framework for efficient document layout Annotation. OLALA aims to optimize the annotation process by selectively annotating only the most ambiguous regions within an image, while using automatically generated labels for the rest. Central to OLALA is a perturbation-based scoring function that determines which objects require manual annotation. Extensive experiments show that OLALA can significantly boost model performance and improve annotation efficiency, facilitating the extraction of masses of structured text for downstream NLP applications.