JS calculation of time difference from the current time technology sharing

StableDiffusion3’s paper is finally here! This model was released two weeks ago and uses the same DiT (DiffusionTransformer) architecture as Sora. It caused quite a stir once it was released. Compared with the previous version, the quality of the images generated by StableDiffusion3 has been significantly improved. It now supports multi-theme prompts, and the text writing effect has also been improved, and garbled characters no longer appear. StabilityAI pointed out that StableDiffusion3 is a series of models with parameter sizes ranging from 800M to 8B. This parameter range means that the model can be run directly on many portable devices, significantly reducing the use of AI

Trajectory prediction plays an important role in autonomous driving. Autonomous driving trajectory prediction refers to predicting the future driving trajectory of the vehicle by analyzing various data during the vehicle's driving process. As the core module of autonomous driving, the quality of trajectory prediction is crucial to downstream planning control. The trajectory prediction task has a rich technology stack and requires familiarity with autonomous driving dynamic/static perception, high-precision maps, lane lines, neural network architecture (CNN&GNN&Transformer) skills, etc. It is very difficult to get started! Many fans hope to get started with trajectory prediction as soon as possible and avoid pitfalls. Today I will take stock of some common problems and introductory learning methods for trajectory prediction! Introductory related knowledge 1. Are the preview papers in order? A: Look at the survey first, p

How to calculate the number of days between dates in PHP: Use the date_diff() function to obtain the DateInterval object. Extract the days property in the diff array from the DateInterval object. This property contains the number of days between two dates.

This paper explores the problem of accurately detecting objects from different viewing angles (such as perspective and bird's-eye view) in autonomous driving, especially how to effectively transform features from perspective (PV) to bird's-eye view (BEV) space. Transformation is implemented via the Visual Transformation (VT) module. Existing methods are broadly divided into two strategies: 2D to 3D and 3D to 2D conversion. 2D-to-3D methods improve dense 2D features by predicting depth probabilities, but the inherent uncertainty of depth predictions, especially in distant regions, may introduce inaccuracies. While 3D to 2D methods usually use 3D queries to sample 2D features and learn the attention weights of the correspondence between 3D and 2D features through a Transformer, which increases the computational and deployment time.

JavaScript tutorial: How to get HTTP status code, specific code examples are required. Preface: In web development, data interaction with the server is often involved. When communicating with the server, we often need to obtain the returned HTTP status code to determine whether the operation is successful, and perform corresponding processing based on different status codes. This article will teach you how to use JavaScript to obtain HTTP status codes and provide some practical code examples. Using XMLHttpRequest

Please note that this square man is frowning, thinking about the identities of the "uninvited guests" in front of him. It turned out that she was in a dangerous situation, and once she realized this, she quickly began a mental search to find a strategy to solve the problem. Ultimately, she decided to flee the scene and then seek help as quickly as possible and take immediate action. At the same time, the person on the opposite side was thinking the same thing as her... There was such a scene in "Minecraft" where all the characters were controlled by artificial intelligence. Each of them has a unique identity setting. For example, the girl mentioned before is a 17-year-old but smart and brave courier. They have the ability to remember and think, and live like humans in this small town set in Minecraft. What drives them is a brand new,

In September 23, the paper "DeepModelFusion:ASurvey" was published by the National University of Defense Technology, JD.com and Beijing Institute of Technology. Deep model fusion/merging is an emerging technology that combines the parameters or predictions of multiple deep learning models into a single model. It combines the capabilities of different models to compensate for the biases and errors of individual models for better performance. Deep model fusion on large-scale deep learning models (such as LLM and basic models) faces some challenges, including high computational cost, high-dimensional parameter space, interference between different heterogeneous models, etc. This article divides existing deep model fusion methods into four categories: (1) "Pattern connection", which connects solutions in the weight space through a loss-reducing path to obtain a better initial model fusion

Written above & The author’s personal understanding is that image-based 3D reconstruction is a challenging task that involves inferring the 3D shape of an object or scene from a set of input images. Learning-based methods have attracted attention for their ability to directly estimate 3D shapes. This review paper focuses on state-of-the-art 3D reconstruction techniques, including generating novel, unseen views. An overview of recent developments in Gaussian splash methods is provided, including input types, model structures, output representations, and training strategies. Unresolved challenges and future directions are also discussed. Given the rapid progress in this field and the numerous opportunities to enhance 3D reconstruction methods, a thorough examination of the algorithm seems crucial. Therefore, this study provides a comprehensive overview of recent advances in Gaussian scattering. (Swipe your thumb up