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Cover of Computer Vision – ECCV 2018 Workshops

Computer Vision – ECCV 2018 Workshops

Written by Laura Leal-Taixé,Stefan Roth

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777 pages, about 16 hours of reading

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About this book

The six-volume set comprising the LNCS volumes 11129-11134 constitutes the refereed proceedings of the workshops that took place in conjunction with the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.43 workshops from 74 workshops proposals were selected for inclusion in the proceedings. The workshop topics present a good orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and novel applications.

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Reading guide

Themes, characters and key ideas in Computer Vision – ECCV 2018 Workshops, written by Chaptra AI.

  • about 40 hours
  • advanced
  • Informative
  • Rigorous
  • Cutting-edge

This six-volume set, LNCS 11129-11134, compiles the refereed proceedings of 43 workshops held concurrently with the 15th European Conference on Computer Vision (ECCV 2018) in Munich. It serves as a comprehensive snapshot of the cutting-edge research and emerging trends in computer vision as of 2018, showcasing diverse topics from fundamental technologies to novel applications. The collection reflects the dynamic evolution of the field, bridging traditional issues with new methodologies like deep learning, and fostering interdisciplinary connections with neighboring scientific domains. It is an essential reference for researchers and practitioners in computer vision and related areas.

"This paper explores a novel approach to unsupervised domain adaptation using adversarial learning for semantic segmentation in adverse weather conditions."

Key themes

Deep Learning for Computer Vision
This overarching theme explores the pervasive application of deep neural networks across almost all facets of computer vision, including image classification, object detection, semantic segmentation, generative models, and more. It highlights the paradigm shift from traditional hand-crafted features to learned representations.
Robustness, Fairness, and Explainability in AI
This theme addresses the critical need for computer vision systems to be robust against adversarial attacks, fair in their decision-making processes, and explainable in their reasoning. It reflects a growing awareness of the societal impact and potential biases of AI technologies.
3D Vision and Scene Understanding
This theme focuses on the reconstruction, interpretation, and understanding of three-dimensional environments from various sensor inputs (e.g., stereo cameras, LiDAR, RGB-D sensors). It encompasses topics like 3D object detection, scene reconstruction, SLAM (Simultaneous Localization and Mapping), and human pose estimation in 3D space.

Worth discussing

What are the most significant advancements in computer vision presented in these workshops, and how have they evolved since 2018?

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