The proposed zero-shot reranking methods can be extended to various multimedia retrieval tasks by leveraging the inherent flexibility of large language models (LLMs) and their ability to process diverse input types. For instance, in image retrieval, the same zero-shot reranking framework can be applied by using image captions or descriptions as queries, allowing the model to rank images based on their relevance to the textual input. Similarly, in video retrieval, the methods can utilize video metadata, transcripts, or even visual features extracted from the video frames to perform reranking.
Moreover, the zero-shot approach can be adapted to tasks such as sentiment analysis in multimedia content, where the model can evaluate the emotional tone of video clips or audio segments based on user-defined queries. By employing embeddings generated from various modalities (text, audio, and visual), the reranking methods can effectively assess the relevance of multimedia content across different domains. This adaptability not only enhances the retrieval performance but also reduces the dependency on task-specific training data, making it a versatile solution for a wide range of multimedia retrieval applications.