Machine Heart Reports Editor: Zhao Yang Recently, a paper was published on arXiv, providing a new interpretation of the mathematical principles behind Transformers. The content is extensive and rich in knowledge, and I highly recommend reading the original. In 2017, Vaswani et al. published “Attention Is All You Need,” marking a significant milestone in the … Read more
This article aims to introduce the Transformer model. Originally developed for machine translation, this model has since been widely applied in various fields such as computer recognition and multimodal tasks. The Transformer model introduces self-attention mechanisms and positional encoding, and its architecture mainly consists of an input part, an output part, and encoders and decoders. … Read more
Click the card below to follow the WeChat public account “Python for Beginners” Object detection is a core task in computer vision that drives the development of technologies ranging from autonomous vehicles to real-time video surveillance. It involves detecting and locating objects within an image, and recent advances in deep learning have made this task … Read more
Introduction In recent years, deep learning has made tremendous progress in the field of Natural Language Processing (NLP), and the Transformer model is undoubtedly one of the best. Since the Google research team proposed the Transformer model in their paper “Attention is All You Need” in 2017, it has become the cornerstone for many NLP … Read more
Click the card below to follow the 「Intelligent Book Boy」 public account Click to join👉「Intelligent Book Boy」 group chat Want to learn more: Cutting-edge AI visual perception full-stack knowledge👉「Classification, Detection, Segmentation, Key Points, Lane Line Detection, 3D Vision (Segmentation, Detection), Multi-modal, Object Tracking, NerF」 Industry Technical Solutions👉「AI Security, AI Healthcare, AI Autonomous Driving」 AI Model … 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
AliMei Guide Learn about Transformer, and come write one with the author. As an engineering student, when learning about Transformer, it always feels like understanding is not solid enough unless I write one myself. Knowledge gained from books is often superficial; true understanding requires practice, so take time to debug a few times! Note: No … Read more
Object detection is a fundamental task in computer vision that requires us to locate and classify objects. The groundbreaking R-CNN family[1]-[3] and ATSS[4], RetinaNet[5], FCOS[6], PAA[7], and a series of variants[8][10] have made significant breakthroughs in the object detection task. One-to-many label assignment is the core solution, which assigns each ground truth box as a … Read more
This is the fourth translation in the Illustrated Transformer series. The series is authored by Ketan Doshi and published on Medium. During the translation process, I modified some illustrations and optimized and supplemented some descriptions based on the code provided in Li Mu’s “Hands-On Deep Learning with Pytorch”. The original article link can be found … Read more
Background: From May 23 to 24, the Tencent “Cloud + Future” summit was held in Guangzhou with the theme “Rejuvenation”, where leaders from various government agencies in Guangdong Province, domestic and foreign academic experts, industry leaders, and technical experts gathered to discuss innovation and development in cloud computing and the digital industry. Dr. Wang Caihua, … Read more