Unseen Video

"Unseen video" research focuses on developing methods for handling video data where the model encounters novel situations or objects during testing that were absent during training. Current efforts concentrate on improving generalization capabilities using techniques like contrastive learning, relative pose regression, and graph convolutional networks, often within the context of specific applications such as object navigation, image restoration, and fake video detection. These advancements are crucial for building more robust and adaptable AI systems capable of operating effectively in real-world scenarios characterized by unpredictable variations in data. The ultimate goal is to create AI that can reason about and interact with unseen aspects of the world, improving the reliability and applicability of computer vision and robotics.

Papers