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
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
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
Selected from interconnects Author: Nathan Lambert Translated by Machine Heart Machine Heart Editorial Team State-space models are on the rise; has attention reached its end? In recent weeks, there has been a hot topic in the AI community: implementing language modeling with attention-free architectures. In short, this refers to a long-standing research direction in the … Read more
Machine Heart reports Machine Heart Editorial Team Do language models plan for future tokens? This paper gives you the answer. “Don’t let Yann LeCun see this.” Yann LeCun said it was too late; he has already seen it. Today, we introduce the paper that “LeCun insists on seeing,” which explores the question: Is the Transformer … Read more
LLM: The Transformer of Natural Language Processing In today’s digital age, Large Language Models (LLM) are key technologies in the field of artificial intelligence, profoundly changing the landscape of natural language processing at an unprecedented pace. LLMs are based on deep learning and can understand and generate human language. Their core principles and architecture are … Read more
Source: ZHUAN ZHI This article is approximately 1000 words, recommended reading time is 5 minutes. We explore three paths to introduce language into computer vision systems for open set recognition. The visual world is vast and constantly evolving. Additionally, due to the long-tail nature of data collection, computer vision systems cannot observe all visual concepts … Read more
Abstract: With the rapid development of communication technology today, the electromagnetic space environment has become increasingly complex, and the types of signals in the electromagnetic space have also diversified. Faced with various interferences in the electromagnetic space, accurately and effectively distinguishing the types of electromagnetic signals has become more challenging. To address this issue, a … Read more
MLNLP ( Machine Learning Algorithms and Natural Language Processing ) community is a well-known natural language processing community both domestically and internationally, covering NLP graduate students, university teachers, and corporate researchers. The vision of the community is to promote communication between the academic and industrial circles of natural language processing and machine learning, as well … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The community’s vision is to promote communication and progress among the academic and industrial circles of natural language processing and machine learning, especially for beginners. Reprinted from | … Read more