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Researchers from the University of Oxford Developed a Deep Learning-Based Software for Precision Tracking of Fish Movement in Complex Environments

Marktechpost

Addressing these challenges, a UK-based research team introduced a hybrid method, merging deep learning and traditional computer vision techniques to enhance tracking accuracy for fish in complex experiments. The deep learning part involves the use of object detection and tracking.

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UCLA Researchers Propose PhyCV: A Physics-Inspired Computer Vision Python Library

Marktechpost

Artificial intelligence is making noteworthy strides in the field of computer vision. One key area of development is deep learning, where neural networks are trained on huge datasets of images to recognize and classify objects, scenes, and events. All Credit For This Research Goes To the Researchers on This Project.

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A New AI Research Proposes VanillaNet: A Novel Neural Network Architecture Emphasizing the Elegance and Simplicity of Design while Retaining Remarkable Performance in Computer Vision Tasks

Marktechpost

ResNet expands on this achievement by including identity mappings through shortcut connections, enabling the training of deep neural networks with good performance across various computer vision applications, including image classification, object identification, and semantic segmentation.

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Why Deep Learning is Always Done on Array Data? New AI Research Introduces ‘Spatial Functa,’ Where From Data to Functa is Treated Like One

Marktechpost

Recently, neural fields have gained a lot of traction in computer vision as a means of representing signals like pictures, 3D shapes/scenes, movies, music, medical images, and weather data. Prior functa work demonstrated that deep learning on neural fields is possible for many different modalities, even with relatively small datasets.

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Detectron2: A Rundown of Meta’s Computer Vision Framework

Viso.ai

The developers of Detectron2 are Meta’s Facebook AI Research (FAIR) team, who have stated that “Our goal with Detectron2 is to support the wide range of cutting-edge object detection and segmentation models available today, but also to serve the ever-shifting landscape of cutting-edge research.”

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This AI Paper from MIT Explores the Scaling of Deep Learning Models for Chemistry Research

Marktechpost

To efficiently handle hyperparameter optimization (HPO) for deep chemical models, the paper introduces a technique called Training Performance Estimation (TPE), adapting it from a method used in computer vision architectures. All credit for this research goes to the researchers of this project.

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How Can Automated Retail Checkouts Recognize Unlabeled Produce? Meet the PseudoAugment Computer Vision Approach

Marktechpost

With the advancements in machine learning and deep learning techniques, there has also been an increase in automation of various dimensions. To overcome this problem, researchers from Skoltech and other institutions have devised a new way to distinguish weighted goods at a supermarket.