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Computer vision, a field of artificial intelligence (AI) that enables computers to derive information from images, videos, and other visual inputs, has been rapidly advancing in recent years. With the rise of deep learning, neural networks, and other machine learning techniques, computer vision has become a critical component of many applications in business, entertainment, transportation, healthcare, and everyday life.
Computer Vision (CV) has undergone a year teeming with extraordinary innovation and technological leaps. This article will delve into the latest trends, tools, and career opportunities in this rapidly evolving field.
Fri May 3, 2024
SAM (Segment Anything Model): Developed by Meta AI, SAM revolutionised pixel-level classification, enabling the segmentation of virtually anything in an image. This development opened new avenues for complex segmentation tasks across various datasets.
Multimodal Large Language Models (LLMs): Models like GPT-4 bridged the gap between text and visual data, providing AI with the ability to understand and interpret complex multimodal inputs. They played a crucial role in enhancing the capabilities of AI to process and react to a combination of text and visual cues.
YOLOv8: This iteration of the YOLO series set new standards in object detection with its enhanced speed and accuracy. YOLOv8’s advancements have made it a preferred choice for real-time applications that require quick and precise object detection.
DINOv2 (Self-supervised Learning Model): DINOv2 marked a significant step in self-supervised learning within CV. By reducing the reliance on large annotated datasets, it demonstrated the potential of self-supervised approaches to train high-quality models with fewer labelled images.
Text-to-Image (T2I) Models: These models have dramatically improved the quality and realism of AI-generated images from textual descriptions. They have facilitated creative applications like digital art generation, making AI an invaluable tool for artists and designers.
Deep Learning and Neural Networks: Deep learning and neural networks have revolutionised computer vision, enabling more accurate and efficient image and video analysis. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning architectures have significantly improved image classification, object detection, and segmentation.
Transfer Learning and Pre-trained Models: Transfer learning and pre-trained models have become popular techniques in computer vision, enabling faster and more efficient model development. These techniques involve using pre-trained models on large datasets, such as ImageNet, and fine-tuning them for specific tasks or datasets.
Real-time Computer Vision: Real-time computer vision has become increasingly important, with applications in autonomous vehicles, robotics, and augmented reality. Techniques such as object detection, tracking, and segmentation in real-time have been made possible through the use of efficient deep learning architectures and hardware acceleration.
Explainable AI and Interpretability: Explainable AI and interpretability have become critical in computer vision, with the need to understand and explain the decisions made by AI systems. Techniques such as saliency maps, heat maps, and visual explanations have been developed to provide insights into the decision-making process of AI systems.
VoxelGPT is a FiftyOne Plugin that combines the power of GPT-3.5 with FiftyOne’s computer vision query language. This enables you to filter, sort, and semantically slice your data with natural language. It’s capable of handling any of the following types of queries:
Maximo Visual Inspection includes tools and interfaces for anyone who has limited skills in deep learning technologies. It enables you to label images and videos that can be used to train and validate a model.
Google Cloud Vision API helps in quick and easy integration of basic vision features. Prebuilt features like image labelling, face and landmark detection, OCR and safe search make it extremely helpful, while also being cost-effective, by using a pay-per-use model, for individuals.
OpenCV is a library of programming functions mainly for real-time computer vision. The library is cross-platform and licensed as free and open-source software under Apache License.
TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.
PyTorch is a machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, originally developed by Meta AI and now part of the Linux Foundation umbrella.
The field of computer vision offers a plethora of career opportunities, such as:
Computer vision has become a critical component of many applications in business, entertainment, transportation, healthcare, and everyday life. With the rise of deep learning, neural networks, and other machine learning techniques, computer vision has become more accurate, efficient, and accessible. The latest trends in computer vision technologies, tools, and career opportunities provide exciting opportunities for developers, researchers, and professionals to contribute to this rapidly growing field.
To keep up with such a field, platforms such as Learnsector release various courses and blogs for all interested individuals to keep them updated and future-ready.
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