Visual Recognition

Visual recognition research aims to enable computers to understand and interpret images and videos, mirroring human visual perception. Current efforts focus on improving the efficiency and robustness of various model architectures, including Vision Transformers, convolutional neural networks, and hybrid approaches, often incorporating techniques like parameter-efficient transfer learning and attention mechanisms to enhance performance on diverse tasks such as image classification, object detection, and segmentation. These advancements are crucial for applications ranging from autonomous driving and medical image analysis to robotics and accessibility technologies, driving progress in both fundamental computer vision and practical real-world deployments.