Review of Attention Mechanisms in Object Detection
The superior performance of Transformer in natural language processing has inspired researchers to explore their applications in computer vision tasks.The Transformer-based object detection model,Detection Transformer(DETR),treats object detection as a set prediction problem,introducing the Transformer model to address this task and eliminating the proposal generation and post-processing steps that are typical of traditional methods.The original DETR model encounters issues related to slow training convergence and inefficiency in detecting small objects.To address these challenges,researchers have implemented various improvements to enhance DETR performance.This study conducts an in-depth investigation of both the basic and enhanced modules of DETR,including modifications to the backbone architecture,query design strategies,and improvements to the attention mechanism.Furthermore,it provides a comparative analysis of various detectors and evaluates their performance and network architecture.The potential and application prospects of DETR in computer vision tasks are discussed herein,along with its current limitations and challenges.Finally,this study analyzes and summarizes related models,assesses the advantages and limitations of attention models in the context of object detection,and outlines future research directions in this field.
万方数据
维普
