Cross Modal Task

Cross-modal tasks involve processing and integrating information from multiple data modalities, such as text, images, audio, and video, to achieve a shared objective. Current research focuses on developing efficient model architectures, including sequence-to-sequence models and those employing cross-attention mechanisms, to overcome challenges like limited aligned data and modality gaps. These advancements aim to improve zero-shot generalization and parameter efficiency in tasks ranging from image captioning and speech translation to visual question answering, impacting fields like natural language processing, computer vision, and multimodal learning. The ultimate goal is to build more robust and versatile AI systems capable of understanding and interacting with the world in a more human-like way.