COCO Benchmark

The COCO benchmark is a widely used dataset for evaluating computer vision models, particularly in object detection, instance segmentation, and image captioning. Current research focuses on improving model accuracy and efficiency, addressing challenges like class imbalance, catastrophic forgetting in incremental learning, and the need for real-time performance. This involves developing novel architectures such as DETR variants and employing techniques like knowledge distillation and semi-supervised learning to enhance model generalization and reduce annotation requirements. The COCO benchmark's impact stems from its role in driving advancements in object recognition and related fields, leading to improved performance in various applications, including autonomous driving and robotics.