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Found 6 papers, 1 papers with code
Diff4Steer: Steerable Diffusion Prior for Generative Music Retrieval with Semantic Guidance
Modern music retrieval systems often rely on fixed representations of user preferences, limiting their ability to capture users' diverse and uncertain retrieval needs.
Item-Language Model for Conversational Recommendation
Another difficulty is user interaction signals often have a different pattern from natural language text, and it is currently unclear if the LLM training setup can learn more non-trivial knowledge from interaction signals compared with traditional recommender system methods.
V2Meow: Meowing to the Visual Beat via Video-to-Music Generation
Video-to-music generation demands both a temporally localized high-quality listening experience and globally aligned video-acoustic signatures.
Multi-Task End-to-End Training Improves Conversational Recommendation
In this paper, we analyze the performance of a multitask end-to-end transformer model on the task of conversational recommendations, which aim to provide recommendations based on a user's explicit preferences expressed in dialogue.
MAQA: A Multimodal QA Benchmark for Negation
Multimodal learning can benefit from the representation power of pretrained Large Language Models (LLMs).
MuLan: A Joint Embedding of Music Audio and Natural Language
Music tagging and content-based retrieval systems have traditionally been constructed using pre-defined ontologies covering a rigid set of music attributes or text queries.
