Scene Classification

Scene classification, the task of automatically assigning semantic labels to images, aims to enable computers to "understand" visual scenes. Current research emphasizes improving accuracy and efficiency across diverse domains, focusing on model architectures like convolutional neural networks (CNNs), graph convolutional networks (GCNs), transformers, and variational autoencoders (VAEs), often incorporating techniques like multi-task learning, contrastive learning, and self-supervised learning to address challenges such as limited data, noisy labels, and domain adaptation. This field is crucial for applications ranging from autonomous driving and remote sensing to robotics and assistive technologies, with ongoing efforts to mitigate biases and enhance the interpretability of these powerful models.