Domain Adaptation for Visual Understanding
暫譯: 視覺理解的領域適應
Singh, Richa, Vatsa, Mayank, Patel, Vishal M.
- 出版商: Springer
- 出版日期: 2020-01-09
- 售價: $4,510
- 貴賓價: 9.5 折 $4,285
- 語言: 英文
- 頁數: 144
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3030306704
- ISBN-13: 9783030306700
海外代購書籍(需單獨結帳)
商品描述
This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition.
Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods.
This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.
商品描述(中文翻譯)
這本獨特的著作回顧了在視覺理解的機器學習演算法訓練中,領域適應的最新進展,並提供了來自國際專家的寶貴見解。文本呈現了一系列多樣化的新技術,涵蓋了物體識別、臉部識別以及行為和事件識別的應用。
主題與特點:回顧了可用於視覺理解的基於領域適應的機器學習演算法,並提供了一種深度度量學習方法;介紹了一種新穎的無監督圖像到圖像轉換方法,以及一種利用集成學習的視頻片段檢索模型;提出了一種獨特的方法來確定哪個數據集在基礎訓練中最有用,以提高深度神經網絡的可轉移性;描述了一種定量方法,用於估計源數據和目標數據之間的差異,以增強圖像分類性能;呈現了一種增強面部動作識別的多模態融合技術,以及一個在領域適應中進行直覺學習的框架;檢視了一種基於插值的原創方法,以解決基於相關濾波器的方法中模型退化的問題。
這部權威著作將成為對於有興趣於基於機器學習的視覺識別和理解的研究人員和實務工作者的重要參考資料。
作者簡介
Dr. Richa Singh is a Professor at Indraprastha Institute of Information Technology, Delhi, India. Dr. Mayank Vatsa is a Professor at the same institution. Dr. Vishal M. Patel is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, Baltimore, MD, USA. Dr. Nalini Ratha is a Research Staff Member at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA.
作者簡介(中文翻譯)
Richa Singh 博士 是印度德里印德拉普拉斯塔資訊科技學院的教授。Mayank Vatsa 博士 也是該機構的教授。Vishal M. Patel 博士 是美國約翰霍普金斯大學電機與計算機工程系的助理教授,位於馬里蘭州巴爾的摩。Nalini Ratha 博士 是美國IBM托馬斯·J·沃森研究中心的研究人員,位於紐約州約克鎮高地。