Multimedia Retrieval

Multimedia retrieval aims to efficiently search and retrieve relevant multimedia content (images, videos, etc.) based on user queries, often involving text and visual information. Current research focuses on improving the accuracy and efficiency of retrieval using techniques like contrastive learning (e.g., CLIP adaptations), large language models (LLMs) for query refinement and iterative feedback, and multi-view hashing methods to handle heterogeneous data. These advancements are crucial for managing the ever-growing volume of multimedia data and enabling applications such as AI-assisted video creation and improved patent searching.