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Found 10 papers, 1 papers with code
Embodiment: Self-Supervised Depth Estimation Based on Camera Models
Depth estimation is a critical topic for robotics and vision-related tasks.
Dual-Task Vision Transformer for Rapid and Accurate Intracerebral Hemorrhage CT Image Classification
In addition, we propose a neural network structure, dual-task vision transformer (DTViT), for the automated classification and diagnosis of ICH images.
LLMs for Coding and Robotics Education
To teach young children how to code and compete in robot challenges, large language models are being utilized for robot code explanation, generation, and modification.
Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges
Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas.
SAM for Poultry Science
This study aims to assess the zero-shot segmentation performance of SAM on representative chicken segmentation tasks, including part-based segmentation and the use of infrared thermal images, and to explore chicken-tracking tasks by using SAM as a segmentation tool.
AGI: Artificial General Intelligence for Education
AGI, driven by the recent large pre-trained models, represents a significant leap in the capability of machines to perform tasks that require human-level intelligence, such as reasoning, problem-solving, decision-making, and even understanding human emotions and social interactions.
AGI for Agriculture
Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education.
Fast and Accurate: Video Enhancement using Sparse Depth
This paper presents a general framework to build fast and accurate algorithms for video enhancement tasks such as super-resolution, deblurring, and denoising.
Unsupervised Simultaneous Depth-from-defocus and Depth-from-focus
The proposed network is able to learn optimal depth mapping from the information contained in the blurring of a single image, generate a simulated image focal stack and all-in-focus image, and train a depth estimator from an image focal stack.
Localize Me Anywhere, Anytime: A Multi-Task Point-Retrieval Approach
The main contribution of our paper is that we use a 3D model reconstructed by a short video as the query to realize 3D-to-3D localization under a multi-task point retrieval framework.
