Researchers from Shanghai AI Laboratory, Fudan University, Northwestern Polytechnical University, and The Hong Kong University of Science and Technology have collaborated to develop a 3D Gaussian representation-based Simultaneous Localization and Mapping (SLAM) system named GS-SLAM. The goal of the plan is to achieve a balance between accuracy and efficiency. GS-SLAM uses a real-time differentiable splatting…

Following another vehicle is the most common and basic driving activity. Following other cars safely lessens collisions and makes traffic flow more predictable. When drivers follow other vehicles on the road, the appropriate car-following model represents this behavior mathematically or computationally. The availability of real-world driving data and developments in machine learning have largely contributed…

OpenAI has been at the forefront of the latest advancements in AI, with its highly competent models like GPT and DALLE. When released, GPT-3 was a one-of-its-kind model with great language processing capabilities such as text summarization, sentence completion, and many others. The release of its successor, GPT-4, marked a significant shift in how we…

In the quickly advancing field of Artificial Intelligence (AI), Deep Learning is becoming significantly more popular and stepping into every industry to make lives easier. Simultaneous Localization and Mapping (SLAM) in AI, which is an essential component of robots, driverless vehicles, and augmented reality systems, has been experiencing revolutionary advancements recently. SLAM involves reconstructing the…

MeshGPT is proposed by researchers from the Technical University of Munich, Politecnico di Torino, AUDI AG as a method for autoregressive generating triangle meshes, leveraging a GPT-based architecture trained on a learned vocabulary of triangle sequences. This approach uses a geometric vocabulary and latent geometric tokens to represent triangles, producing coherent, clean, compact meshes with…

The development of commercial mixed reality platforms and the quick advancement of 3D graphics technology have made the creation of high-quality 3D scenes one of the main challenges in computer vision. This calls for the capacity to convert any input text, RGB, and RGBD pictures, for example, into a variety of realistic and varied 3D…

Big vision-language models, or LVLMs, can interpret visual cues and provide easy replies for users to interact with. This is accomplished by skillfully fusing large language models (LLMs) with large-scale visual instruction finetuning. Nevertheless, LVLMs only need hand-crafted or LLM-generated datasets for alignment by supervised fine-tuning (SFT). Although it works well to change LVLMs from…

FER is pivotal in human-computer interaction, sentiment analysis, affective computing, and virtual reality. It helps machines understand and respond to human emotions. Methodologies have advanced from manual extraction to CNNs and transformer-based models. Applications include better human-computer interaction and improved emotional response in robots, making FER crucial in human-machine interface technology. State-of-the-art methodologies in FER…

Researchers from Google Research and UIUC propose ZipLoRA, which addresses the issue of limited control over personalized creations in text-to-image diffusion models by introducing a new method that merges independently trained style and subject Linearly Recurrent Attentions (LoRAs). It allows for greater control and efficacy in generating any matter. The study emphasizes the importance of…