Videoprompter: an ensemble of foundational models for zero-shot video understanding

TL;DR

This paper introduces Videoprompter, a framework combining vision-language models with generative video-to-text and text-to-text models, enhancing zero-shot video understanding by generating descriptive cues and context-aware prompts.

Contribution

It proposes a novel approach that integrates descriptive video conversion and hierarchical prompts to improve zero-shot video classification and retrieval tasks.

Findings

Consistent performance improvements across multiple benchmarks.

Effective enhancement of zero-shot action recognition.

Improved video-to-text and text-to-video retrieval results.

Abstract

Vision-language models (VLMs) classify the query video by calculating a similarity score between the visual features and text-based class label representations. Recently, large language models (LLMs) have been used to enrich the text-based class labels by enhancing the descriptiveness of the class names. However, these improvements are restricted to the text-based classifier only, and the query visual features are not considered. In this paper, we propose a framework which combines pre-trained discriminative VLMs with pre-trained generative video-to-text and text-to-text models. We introduce two key modifications to the standard zero-shot setting. First, we propose language-guided visual feature enhancement and employ a video-to-text model to convert the query video to its descriptive form. The resulting descriptions contain vital visual cues of the query video, such as what objects are present and their spatio-temporal interactions. These descriptive cues provide additional semantic knowledge to VLMs to enhance their zeroshot performance. Second, we propose video-specific prompts to LLMs to generate more meaningful descriptions to enrich class label representations. Specifically, we introduce prompt techniques to create a Tree Hierarchy of Categories for class names, offering a higher-level action context for additional visual cues, We demonstrate the effectiveness of our approach in video understanding across three different zero-shot settings: 1) video action recognition, 2) video-to-text and textto-video retrieval, and 3) time-sensitive video tasks. Consistent improvements across multiple benchmarks and with various VLMs demonstrate the effectiveness of our proposed framework. Our code will be made publicly available.