Author Archives: Nikita Shiledarbaxi - Page 7
Introduction Visual Recognition and Language Understanding are two of the challenging tasks in the domain of Artificial Intelligence. A great deal of vision-and-language research focuses on a small number of independent tasks of different types. Also, it supports an isolated analysis of each of the datasets involved. But the visually dependent language comprehension skills needed…
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Introduction BenchmarkDotNet is a powerful, open-source, lightweight library extensively used by .NET developers for benchmarking their code. It was introduced by the .NET Foundation. Its current maintainers are Andrey Akinshin (Project Lead) and Adam Sitnik. (Have a look at the BenchmarkDotNet team here). Before going into the details of BenchmarkDotNet, let us understand in short,…
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Guide To TAPAS (TAble PArSing) – A Technique To Retrieve Information From Tabular Data Using NLP
One of the most common forms of data that exists today is tabular data (structured data).In order to extract information from tabular data, you use Python libraries like Pandas or SQL-like languages. Google has recently open-sourced one of their models called ‘TAPAS’ (for TAble PArSing) wherein you can ask questions about your data in natural…
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Introduction Infer.NET is a framework for making Bayesian inference on graphical models. The user specifies the factors and variables of a graphical model. Infer.NET analyses them and creates a schedule for making inference on the model. The model can then be queried for marginal distributions. Microsoft Research and .NET Foundation developed Infer.NET. It is also…
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Introduction Style-based Age Manipulation (SAM) is a method used to perform fine-grained age transformation in digital image processing and computer vision tasks using a single facial image as an input. It was introduced by Yuval Alalu, Or Patashnik and Daneil Cohen-Or of Tel-Aviv University in February 2021 (research paper). This article gives an overview of…
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Introduction AllenAct is an open-source project and a modular learning framework designed for researchers and technophiles associated with the domain of Embodied AI. It provides state-of-the-art reproductions of numerous embodied AI models. It also supports the expanding collection of embodied AI tasks, algorithms used to accomplish those tasks as well as environments to run them…
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Image segmentation forms the basis of numerous Computer Vision projects. It segments the visual input in order to process it for tasks such as image classification and object detection. However, all the segmentation techniques may not delineate the objects in an image factory with equally satisfying accuracy. Some may be capable of merely identifying the…
The post Semantic vs Instance vs Panoptic: Which Image Segmentation Technique To Choose appeared first on Analytics India Magazine.
Panoptic segmentation is an image segmentation method used for Computer Vision tasks. It unifies two distinct concepts used to segment images namely, semantic segmentation and instance segmentation. Panoptic segmentation technique was introduced by Kaiming He, Ross Girshick and Piotr Dollar of Facebook AI Research (FAIR), Carsten Rother of HCI/IWR, Heidelberg University (Germany) as well as…
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Introduction SOLO (segment objects by locations) is a simple and flexible framework applied for accomplishing instance segmentation in digital image processing and computer vision tasks. It is based on the notion of “instance categories” for instance segmentation in which each pixel within an instance of an object is assigned a category based on its location…
The post Guide to SOLO and SOLOv2: Ways To Implement Instance Segmentation By Location appeared first on Analytics India Magazine.
AI Habitat is a simulation platform developed with an intent to advance research in the domain of Embodied AI. It trains embodied agents (such as virtual robots and egocentric assistants) in highly photo-realistic 3D environments. It embeds within the trained agents crucial features such as active perception, long-term planning and interactive learning which distinguish them…
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