Context Interaction

Context interaction research focuses on understanding and modeling how the surrounding context influences the interpretation and processing of information, whether it's an action in a video, an object's attributes, or a word's meaning in a sentence. Current research explores this through various approaches, including adversarial training, graph neural networks for high-order interactions, and novel architectures like implicit function spaces and context interaction units within neural networks. These advancements aim to improve the performance of various machine learning tasks, such as zero-shot learning, image super-resolution, and click-through rate prediction, by more effectively leveraging contextual information, ultimately leading to more robust and accurate models.