Sökning: "deep structured models"

Visar resultat 1 - 5 av 14 avhandlingar innehållade orden deep structured models.

1. 1. Geometric Supervision and Deep Structured Models for Image Segmentation

   Författare :Måns Larsson; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; conditional random fields; convolutional neural networks; deep structured models; Semantic segmentation; self-supervised learning; supervised learning; semi-supervised learning;

   Sammanfattning : The task of semantic segmentation aims at understanding an image at a pixel level. Due to its applicability in many areas, such as autonomous vehicles, robotics and medical surgery assistance, semantic segmentation has become an essential task in image analysis. LÄS MER

3. 2. End-to-End Learning of Deep Structured Models for Semantic Segmentation

   Författare :Måns Larsson; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Semantic segmentation; deep structured models; supervised learning; convolutional neural networks; conditional random fields;

   Sammanfattning : The task of semantic segmentation aims at understanding an image at a pixel level. This means assigning a label to each pixel of an image, describing the object it is depicting. LÄS MER

4. 3. Structured Representations for Explainable Deep Learning

   Författare :Federico Baldassarre; Hossein Azizpour; Josephine Sullivan; Kevin Smith; Hamed Pirsiavash; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Explainable AI; Deep Learning; Self-supervised Learning; Transformers; Graph Networks; Computer Vision; Explainable AI; Deep Learning; Self-supervised Learning; Transformers; Graph Networks; Computer Vision; Datalogi; Computer Science;

   Sammanfattning : Deep learning has revolutionized scientific research and is being used to take decisions in increasingly complex scenarios. With growing power comes a growing demand for transparency and interpretability. The field of Explainable AI aims to provide explanations for the predictions of AI systems. LÄS MER

5. 4. Visual Representations and Models: From Latent SVM to Deep Learning

   Författare :Hossein Azizpour; Stefan Carlsson; Barbara Caputo; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Computer Vision; Machine Learning; Artificial Intelligence; Deep Learning; Learning Representation; Deformable Part Models; Discriminative Latent Variable Models; Convolutional Networks; Object Recognition; Object Detection; Computer Science; Datalogi;

   Sammanfattning : Two important components of a visual recognition system are representation and model. Both involves the selection and learning of the features that are indicative for recognition and discarding those features that are uninformative. LÄS MER

7. 5. Deep Evidential Doctor

   Författare :Awais Ashfaq; Sławomir Nowaczyk; Mark Dougherty; Cristina Soguero Ruiz; Högskolan i Halmstad; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES;

   Sammanfattning : Recent years have witnessed an unparalleled surge in deep neural networks (DNNs) research, surpassing traditional machine learning (ML) and statistical methods on benchmark datasets in computer vision, audio processing and natural language processing (NLP). Much of this success can be attributed to the availability of numerous open-source datasets, advanced computational resources and algorithms. LÄS MER