This document discusses using object detection models like Faster R-CNN and Mask R-CNN to extract embeddings from images for image retrieval purposes. It proposes a student-teacher training paradigm where an object detection model acts as the student and learns to transform its output feature map into the feature space of a classification teacher model through knowledge distillation in order to generate more semantically meaningful embeddings. The goal is to perform efficient image retrieval based on these object-level embeddings.
Slides by Miriam Bellver from the Computer Vision Reading Group at the Universitat Politecnica de Catalunya about the paper:
Lu, Yongxi, Tara Javidi, and Svetlana Lazebnik. "Adaptive Object Detection Using Adjacency and Zoom Prediction." CVPR 2016
Abstract:
State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we propose to use a search strategy that adaptively directs computational resources to sub-regions likely to contain objects. Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small. Our approach is comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach while using two orders of magnitude fewer anchors on average. Code is publicly available.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
DetectoRS for Object Detection/Segmentation
On COCO test-dev, DetectoRS achieves state-of-the art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation.
(2020.07)
Slides by Miriam Bellver from the Computer Vision Reading Group at the Universitat Politecnica de Catalunya about the paper:
Lu, Yongxi, Tara Javidi, and Svetlana Lazebnik. "Adaptive Object Detection Using Adjacency and Zoom Prediction." CVPR 2016
Abstract:
State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we propose to use a search strategy that adaptively directs computational resources to sub-regions likely to contain objects. Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small. Our approach is comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach while using two orders of magnitude fewer anchors on average. Code is publicly available.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
DetectoRS for Object Detection/Segmentation
On COCO test-dev, DetectoRS achieves state-of-the art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation.
(2020.07)
Content based image retrieval Projects.pdfrupaymts
Hello students!! Here I came up with new ideas about the Content Based Image Retrieval Project, Takeoff Edu group gives you an Innovative CBIR projects for final year students. Here we provide a CBIR and also all kinds of final year projects to you.
Content Based Image retrieval is not only enhances the efficiency of search engines but also opens up new avenues for image-based knowledge discovery and exploration. It has advanced algorithms and computer vision techniques to analyse and understand the visual content of images, allowing users to search for similar or related images based on visual similarities rather than textual descriptions.
https://imatge.upc.edu/web/publications/region-oriented-convolutional-networks-object-retrieval
BSc thesis by Eduard Fontdevila advised by Amaia Salvador and Xavier Giró-i-Nieto.
EET UPC, June 2015.
[PR-325] Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Tran...Sunghoon Joo
PR-325: Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers
paper link: https://arxiv.org/abs/2004.00849
youtube link: https://youtu.be/Kgh88DLHHTo
http://imatge-upc.github.io/retrieval-2017-cam/
Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or fine-tuning a model every time new images are added to the database is neither efficient nor scalable.
Convolutional neural networks trained for image classification over large datasets have been proven effective feature extractors when transferred to the task of image retrieval. The most successful approaches are based on encoding the activations of convolutional layers as they convey the image spatial information. Our proposal goes beyond and aims at a local-aware encoding of these features depending on the predicted image semantics, with the advantage of using only of the knowledge contained inside the network.
In particular, we employ Class Activation Maps (CAMs) to obtain the most discriminative regions from a semantic perspective. Additionally, CAMs are also used to generate object proposals during an unsupervised re-ranking stage after a first fast search.
Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate that our system is competitive and even outperforms the current state-of-the-art when using off-the-shelf models trained on the object classes of ImageNet.
Learning a Joint Embedding Representation for Image Search using Self-supervi...Sujit Pal
Image search interfaces either prompt the searcher to provide a search image (image-to-image search) or a text description of the image (text-to-image search). Image to Image search is generally implemented as a nearest neighbor search in a dense image embedding space, where the embedding is derived from Neural Networks pre-trained on a large image corpus such as ImageNet. Text to image search can be implemented via traditional (TF/IDF or BM25 based) text search against image captions or image tags.
In this presentation, we describe how we fine-tuned the OpenAI CLIP model (available from Hugging Face) to learn a joint image/text embedding representation from naturally occurring image-caption pairs in literature, using contrastive learning. We then show this model in action against a dataset of medical image-caption pairs, using the Vespa search engine to support text based (BM25), vector based (ANN) and hybrid text-to-image and image-to-image search.
Evolving a Medical Image Similarity SearchSujit Pal
Slides for talk at Haystack Conference 2018. Covers evolution of an Image Similarity Search Proof of Concept built to identify similar medical images. Discusses various image vectorizing techniques that were considered in order to convert images into searchable entities, an evaluation strategy to rank these techniques, as well as various indexing strategies to allow searching for similar images at scale.
https://telecombcn-dl.github.io/dlmm-2017-dcu/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Slides by Issey Masuda about the paper:
Zhu, Yuke, Oliver Groth, Michael Bernstein, and Li Fei-Fei. "Visual7W: Grounded Question Answering in Images." CVPR 2016.
We have seen great progress in basic perceptual tasks such as object recognition and detection. However, AI models still fail to match humans in high-level vision tasks due to the lack of capacities for deeper reasoning. Recently the new task of visual question answering (QA) has been proposed to evaluate a model's capacity for deep image understanding. Previous works have established a loose, global association between QA sentences and images. However, many questions and answers, in practice, relate to local regions in the images. We establish a semantic link between textual descriptions and image regions by object-level grounding. It enables a new type of QA with visual answers, in addition to textual answers used in previous work. We study the visual QA tasks in a grounded setting with a large collection of 7W multiple-choice QA pairs. Furthermore, we evaluate human performance and several baseline models on the QA tasks. Finally, we propose a novel LSTM model with spatial attention to tackle the 7W QA tasks.
Vision and Multimedia Reading Group: DeCAF: a Deep Convolutional Activation F...Simone Ercoli
I presented an interesting paper during the Vision and Multimedia Reading Group about DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition (pdf).
It is a complete evaluation about features extracted from the activation of a deep convolutional network trained with a large scale dataset.
This a work of Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell from Berkeley University
Hierarchical deep learning architecture for 10 k objects classificationcsandit
Evolution of visual object recognition architectures based on Convolutional Neural Networks &
Convolutional Deep Belief Networks paradigms has revolutionized artificial Vision Science.
These architectures extract & learn the real world hierarchical visual features utilizing
supervised & unsupervised learning approaches respectively. Both the approaches yet cannot
scale up realistically to provide recognition for a very large number of objects as high as 10K.
We propose a two level hierarchical deep learning architecture inspired by divide & conquer
principle that decomposes the large scale recognition architecture into root & leaf level model
architectures. Each of the root & leaf level models is trained exclusively to provide superior
results than possible by any 1-level deep learning architecture prevalent today. The proposed
architecture classifies objects in two steps. In the first step the root level model classifies the
object in a high level category. In the second step, the leaf level recognition model for the
recognized high level category is selected among all the leaf models. This leaf level model is
presented with the same input object image which classifies it in a specific category. Also we
propose a blend of leaf level models trained with either supervised or unsupervised learning
approaches. Unsupervised learning is suitable whenever labelled data is scarce for the specific
leaf level models. Currently the training of leaf level models is in progress; where we have
trained 25 out of the total 47 leaf level models as of now. We have trained the leaf models with
the best case top-5 error rate of 3.2% on the validation data set for the particular leaf models.
Also we demonstrate that the validation error of the leaf level models saturates towards the
above mentioned accuracy as the number of epochs are increased to more than sixty. The top-5
error rate for the entire two-level architecture needs to be computed in conjunction with the
error rates of root & all the leaf models. The realization of this two level visual recognition
architecture will greatly enhance the accuracy of the large scale object recognition scenarios
demanded by the use cases as diverse as drone vision, augmented reality, retail, image search
& retrieval, robotic navigation, targeted advertisements etc.
Content based image retrieval Projects.pdfrupaymts
Hello students!! Here I came up with new ideas about the Content Based Image Retrieval Project, Takeoff Edu group gives you an Innovative CBIR projects for final year students. Here we provide a CBIR and also all kinds of final year projects to you.
Content Based Image retrieval is not only enhances the efficiency of search engines but also opens up new avenues for image-based knowledge discovery and exploration. It has advanced algorithms and computer vision techniques to analyse and understand the visual content of images, allowing users to search for similar or related images based on visual similarities rather than textual descriptions.
https://imatge.upc.edu/web/publications/region-oriented-convolutional-networks-object-retrieval
BSc thesis by Eduard Fontdevila advised by Amaia Salvador and Xavier Giró-i-Nieto.
EET UPC, June 2015.
[PR-325] Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Tran...Sunghoon Joo
PR-325: Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers
paper link: https://arxiv.org/abs/2004.00849
youtube link: https://youtu.be/Kgh88DLHHTo
http://imatge-upc.github.io/retrieval-2017-cam/
Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or fine-tuning a model every time new images are added to the database is neither efficient nor scalable.
Convolutional neural networks trained for image classification over large datasets have been proven effective feature extractors when transferred to the task of image retrieval. The most successful approaches are based on encoding the activations of convolutional layers as they convey the image spatial information. Our proposal goes beyond and aims at a local-aware encoding of these features depending on the predicted image semantics, with the advantage of using only of the knowledge contained inside the network.
In particular, we employ Class Activation Maps (CAMs) to obtain the most discriminative regions from a semantic perspective. Additionally, CAMs are also used to generate object proposals during an unsupervised re-ranking stage after a first fast search.
Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate that our system is competitive and even outperforms the current state-of-the-art when using off-the-shelf models trained on the object classes of ImageNet.
Learning a Joint Embedding Representation for Image Search using Self-supervi...Sujit Pal
Image search interfaces either prompt the searcher to provide a search image (image-to-image search) or a text description of the image (text-to-image search). Image to Image search is generally implemented as a nearest neighbor search in a dense image embedding space, where the embedding is derived from Neural Networks pre-trained on a large image corpus such as ImageNet. Text to image search can be implemented via traditional (TF/IDF or BM25 based) text search against image captions or image tags.
In this presentation, we describe how we fine-tuned the OpenAI CLIP model (available from Hugging Face) to learn a joint image/text embedding representation from naturally occurring image-caption pairs in literature, using contrastive learning. We then show this model in action against a dataset of medical image-caption pairs, using the Vespa search engine to support text based (BM25), vector based (ANN) and hybrid text-to-image and image-to-image search.
Evolving a Medical Image Similarity SearchSujit Pal
Slides for talk at Haystack Conference 2018. Covers evolution of an Image Similarity Search Proof of Concept built to identify similar medical images. Discusses various image vectorizing techniques that were considered in order to convert images into searchable entities, an evaluation strategy to rank these techniques, as well as various indexing strategies to allow searching for similar images at scale.
https://telecombcn-dl.github.io/dlmm-2017-dcu/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Slides by Issey Masuda about the paper:
Zhu, Yuke, Oliver Groth, Michael Bernstein, and Li Fei-Fei. "Visual7W: Grounded Question Answering in Images." CVPR 2016.
We have seen great progress in basic perceptual tasks such as object recognition and detection. However, AI models still fail to match humans in high-level vision tasks due to the lack of capacities for deeper reasoning. Recently the new task of visual question answering (QA) has been proposed to evaluate a model's capacity for deep image understanding. Previous works have established a loose, global association between QA sentences and images. However, many questions and answers, in practice, relate to local regions in the images. We establish a semantic link between textual descriptions and image regions by object-level grounding. It enables a new type of QA with visual answers, in addition to textual answers used in previous work. We study the visual QA tasks in a grounded setting with a large collection of 7W multiple-choice QA pairs. Furthermore, we evaluate human performance and several baseline models on the QA tasks. Finally, we propose a novel LSTM model with spatial attention to tackle the 7W QA tasks.
Vision and Multimedia Reading Group: DeCAF: a Deep Convolutional Activation F...Simone Ercoli
I presented an interesting paper during the Vision and Multimedia Reading Group about DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition (pdf).
It is a complete evaluation about features extracted from the activation of a deep convolutional network trained with a large scale dataset.
This a work of Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell from Berkeley University
Hierarchical deep learning architecture for 10 k objects classificationcsandit
Evolution of visual object recognition architectures based on Convolutional Neural Networks &
Convolutional Deep Belief Networks paradigms has revolutionized artificial Vision Science.
These architectures extract & learn the real world hierarchical visual features utilizing
supervised & unsupervised learning approaches respectively. Both the approaches yet cannot
scale up realistically to provide recognition for a very large number of objects as high as 10K.
We propose a two level hierarchical deep learning architecture inspired by divide & conquer
principle that decomposes the large scale recognition architecture into root & leaf level model
architectures. Each of the root & leaf level models is trained exclusively to provide superior
results than possible by any 1-level deep learning architecture prevalent today. The proposed
architecture classifies objects in two steps. In the first step the root level model classifies the
object in a high level category. In the second step, the leaf level recognition model for the
recognized high level category is selected among all the leaf models. This leaf level model is
presented with the same input object image which classifies it in a specific category. Also we
propose a blend of leaf level models trained with either supervised or unsupervised learning
approaches. Unsupervised learning is suitable whenever labelled data is scarce for the specific
leaf level models. Currently the training of leaf level models is in progress; where we have
trained 25 out of the total 47 leaf level models as of now. We have trained the leaf models with
the best case top-5 error rate of 3.2% on the validation data set for the particular leaf models.
Also we demonstrate that the validation error of the leaf level models saturates towards the
above mentioned accuracy as the number of epochs are increased to more than sixty. The top-5
error rate for the entire two-level architecture needs to be computed in conjunction with the
error rates of root & all the leaf models. The realization of this two level visual recognition
architecture will greatly enhance the accuracy of the large scale object recognition scenarios
demanded by the use cases as diverse as drone vision, augmented reality, retail, image search
& retrieval, robotic navigation, targeted advertisements etc.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
9. • Teacher network:
Classification model
Student-teacher training paradigm (Knowledge distillation)
• Object detection model
Student network
Transforms the
feature map from
the object detection
model to the teacher
model
