Yunchao Gong

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.

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.

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.

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.

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.