Multi Category Object Detection
Multi-category object detection aims to accurately identify and locate multiple object types within an image or video, a crucial task in various applications like autonomous driving and robotics. Current research emphasizes improving both the speed and accuracy of detection, exploring advancements in model architectures such as YOLO-based approaches and incorporating techniques like asymmetrical receptive fields. Furthermore, a growing focus lies on developing more robust and generalizable methods, including open-vocabulary detection that can identify objects without prior training on specific classes and class-agnostic distance estimation using monocular vision. These improvements are driving significant advancements in computer vision and its practical applications.