Dense Object Detection

Dense object detection aims to accurately locate and classify multiple objects within an image or other data modality, such as point clouds or event streams. Current research emphasizes improving accuracy and efficiency through novel loss functions (e.g., adaptive losses focusing on difficult samples), multi-task learning (e.g., jointly detecting objects and segmenting backgrounds), and refined label assignment strategies (e.g., one-to-few assignments). These advancements are driving progress in various applications, including autonomous driving (e.g., LiDAR-based vehicle detection), robotics (e.g., point cloud registration), and video analysis (e.g., action spotting). The development of more robust and efficient dense object detectors is crucial for advancing these fields.