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Yunchao Gong
Researcher at University of North Carolina at Chapel Hill
Publications - 32
Citations - 7556
Yunchao Gong is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Binary code & Embedding. The author has an hindex of 17, co-authored 29 publications receiving 6759 citations. Previous affiliations of Yunchao Gong include Facebook & Google.
Papers
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Journal ArticleDOI
Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
TL;DR: This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections by proposing a simple and efficient alternating minimization algorithm, dubbed iterative quantization (ITQ), and demonstrating an application of ITQ to learning binary attributes or "classemes" on the ImageNet data set.
Proceedings ArticleDOI
Iterative quantization: A procrustean approach to learning binary codes
Yunchao Gong,Svetlana Lazebnik +1 more
TL;DR: A simple and efficient alternating minimization scheme for finding a rotation of zero- centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube is proposed.
Posted Content
Compressing Deep Convolutional Networks using Vector Quantization
TL;DR: This paper is able to achieve 16-24 times compression of the network with only 1% loss of classification accuracy using the state-of-the-art CNN, and finds in terms of compressing the most storage demanding dense connected layers, vector quantization methods have a clear gain over existing matrix factorization methods.
Book ChapterDOI
Multi-scale Orderless Pooling of Deep Convolutional Activation Features
TL;DR: Multi-scale orderless pooling (MOP-CNN) as discussed by the authors extracts CNN activations for local patches at multiple scale levels, performs orderless VLAD pooling of these activations at each level separately, and concatenates the result.
Journal ArticleDOI
A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics
TL;DR: This paper starts with canonical correlation analysis (CCA), a popular and successful approach for mapping visual and textual features to the same latent space, and incorporates a third view capturing high-level image semantics, represented either by a single category or multiple non-mutually-exclusive concepts.