
Domain Adaptation in Computer Vision Applications
Description
This authoritative volume will be of great interest to a broad audience ranging from researchers and practitioners, to students involved in computer vision, pattern recognition and machine learning.
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Content
A Comprehensive Survey on Domain Adaptation for Visual Applications.- A Deeper Look at Dataset Bias.- Part I: Shallow Domain Adaptation Methods .- Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation.- Unsupervised Domain Adaptation based on Subspace Alignment.- Learning Domain Invariant Embeddings by Matching Distributions.- Adaptive Transductive Transfer Machines: A Pipeline for Unsupervised Domain Adaptation.- What To Do When the Access to the Source Data is Constrained?.- Part II: Deep Domain Adaptation Methods .- Correlation Alignment for Unsupervised Domain Adaptation.- Simultaneous Deep Transfer Across Domains and Tasks.- Domain-Adversarial Training of Neural Networks.- Part III: Beyond Image Classification .- Unsupervised Fisher Vector Adaptation for Re-Identification.- Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA.- From Virtual to Real World Visual Perception using Domain Adaptation - The DPM as Example.- Generalizing Semantic Part Detectors Across Domains.- Part IV: Beyond Domain Adaptation: Unifying Perspectives .- A Multi-Source Domain Generalization Approach to Visual Attribute Detection.- Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives.