The proposed augmentation methods can be extended to other cross-modal retrieval tasks beyond video-text, such as image-text or audio-text retrieval, by adapting the techniques to the specific characteristics of the modalities involved. For image-text retrieval, the augmentation methods can be modified to generate semantically similar images and texts. This can involve using image generation models to create variations of the original images and leveraging large language models for text paraphrasing to enrich the textual data. Similarly, for audio-text retrieval, the augmentation methods can be tailored to generate diverse audio samples and corresponding text descriptions. By utilizing audio generation models and language models, the audio data can be augmented with variations and additional context to enhance the retrieval performance. Overall, the key is to adapt the augmentation techniques to the unique features and requirements of each modality while maintaining the goal of enriching the data for improved cross-modal retrieval.