Transformers shine in the field of computer vision, and the Detection Transformer (DETR) is a successful application of Transformers in object detection. By utilizing the attention mechanism in Transformers, it effectively models long-range dependencies in images, simplifying the object detection pipeline and constructing an end-to-end object detector. Object detection can be understood as a set … Read more
Report by Machine Heart Contributors: Racoon, Du Wei, Zhang Qian Since its introduction in 2017, the Transformer has swept the entire NLP field, with popular models like BERT and GPT-2 adopting Transformer-based architectures.Since it is so effective, why not apply it to CV?Recently, researchers at Facebook AI have attempted this by applying Transformers to object … Read more
Source: Heart of Autonomous Driving Editor: Deep Blue Academy Since the emergence of VIT, Transformers have sparked a revolution in the CV field, leading to significant advancements in various upstream and downstream tasks. Today, we will review the end-to-end object detection algorithms based on Transformers! Original Transformer Detector DETR (ECCV2020) The pioneering work! DETR! Code … Read more
Click the “CVer” above to add it to your “Favorites” list. Essential insights delivered promptly. This article is reprinted from: Smarter Since the introduction of the Transformer, it has dominated the NLP field. However, its impact in the CV domain has been moderate, with initial thoughts suggesting it was unsuitable for CV until recently. A … Read more
Original by Machine Heart Author: Chen Ping Since the introduction of the Transformer, it has swept through the entire NLP field. In fact, it can also be used for object detection. Researchers at Facebook AI first launched the visual version of the Transformer—Detection Transformer (DETR), filling the gap of using Transformer for object detection, surpassing … Read more
Inspired by these studies, Shilong Liu and others conducted an in-depth study on the cross-attention module in the Transformer decoder and proposed using 4D box coordinates (x, y, w, h) as queries in DETR, namely anchor boxes. By updating layer by layer, this new query method introduces better spatial priors in the cross-attention module, simplifying … Read more