Multi Object

Multi-object research focuses on enabling robots and computer vision systems to effectively perceive, manipulate, and understand scenes containing multiple objects. Current research emphasizes robust object detection and segmentation, even in cluttered or occluded environments, often employing deep learning models like convolutional neural networks (CNNs), transformers, and graph neural networks (GNNs) for tasks such as grasp planning, pose estimation, and scene understanding. These advancements are crucial for improving robotic manipulation in diverse applications, such as warehouse automation, assistive robotics, and autonomous systems, by enabling more efficient and reliable interaction with complex real-world scenarios. The development of large-scale, attribute-rich datasets is also a key focus to improve model generalization and robustness.