Computer Vision – ACCV 2016

Table of Contents

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   Book Overview
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   Chapter 1 Divide and Conquer: Efficient Density-Based Tracking of 3D Sensors in Manhattan Worlds
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   Chapter 2 Visual Saliency Detection for RGB-D Images with Generative Model
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   Chapter 3 A Coarse-to-Fine Indoor Layout Estimation (CFILE) Method
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   Chapter 4 Unifying Algebraic Solvers for Scaled Euclidean Registration from Point, Line and Plane Constraints
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   Chapter 5 Generalized Fusion Moves for Continuous Label Optimization
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   Chapter 6 Learning to Describe E-Commerce Images from Noisy Online Data
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   Chapter 7 phi-LSTM: A Phrase-Based Hierarchical LSTM Model for Image Captioning
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   Chapter 8 Deep Relative Attributes
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    Chapter 9 Fast Fashion Guided Clothing Image Retrieval: Delving Deeper into What Feature Makes Fashion
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    Chapter 10 Using Gaussian Processes to Improve Zero-Shot Learning with Relative Attributes
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    Chapter 11 MARVEL: A Large-Scale Image Dataset for Maritime Vessels
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    Chapter 12 ‘Part’ly First Among Equals: Semantic Part-Based Benchmarking for State-of-the-Art Object Recognition Systems
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    Chapter 13 End-to-End Training of Object Class Detectors for Mean Average Precision
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    Chapter 14 R-CNN for Small Object Detection
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    Chapter 15 Image Set Classification via Template Triplets and Context-Aware Similarity Embedding
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    Chapter 16 Object-Centric Representation Learning from Unlabeled Videos
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    Chapter 17 Visual Concept Recognition and Localization via Iterative Introspection
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    Chapter 18 Aggregating Local Context for Accurate Scene Text Detection
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    Chapter 19 Bilinear Discriminant Analysis Hashing: A Supervised Hashing Approach for High-Dimensional Data
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    Chapter 20 Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching
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    Chapter 21 Unsupervised Crowd Counting
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    Chapter 22 Long-Term Activity Forecasting Using First-Person Vision
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    Chapter 23 Video Summarization Using Deep Semantic Features
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    Chapter 24 Towards Segmenting Consumer Stereo Videos: Benchmark, Baselines and Ensembles
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    Chapter 25 No-Reference Video Shakiness Quality Assessment
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    Chapter 26 Learning to Extract Motion from Videos in Convolutional Neural Networks