Object Query

Object query methods are transforming various computer vision tasks by representing objects as learnable parameters within neural networks, primarily transformer architectures. Current research focuses on improving the efficiency and accuracy of object queries in applications like object detection, segmentation, and cross-modal retrieval, often addressing challenges such as catastrophic forgetting and limited model capacity through techniques like dynamic query generation and adaptive query propagation. These advancements lead to more robust and efficient models for tasks ranging from document analysis to 3D scene understanding, impacting fields like autonomous driving, medical image analysis, and information retrieval.