27 citations · 67 across the 7 of their papers we have counts for
6 papers · 1 filter
Compression of Deep Neural Networks for Image Instance Retrieval
Vijay Chandrasekhar, Jie Lin, Qianli Liao +4
Image instance retrieval is the problem of retrieving images from a database which contain the same object. Convolutional Neural Network (CNN) based descriptors are becoming the do…
Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval
Olivier Morère, Jie Lin, Antoine Veillard +2
The goal of this work is the computation of very compact binary hashes for image instance retrieval. Our approach has two novel contributions. The first one is Nested Invariance Po…
Group Invariant Deep Representations for Image Instance Retrieval
Olivier Morère, Antoine Veillard, Jie Lin +3
Most image instance retrieval pipelines are based on comparison of vectors known as global image descriptors between a query image and the database images. Due to their success in…
A Practical Guide to CNNs and Fisher Vectors for Image Instance Retrieval
Vijay Chandrasekhar, Jie Lin, Olivier Morère +2
With deep learning becoming the dominant approach in computer vision, the use of representations extracted from Convolutional Neural Nets (CNNs) is quickly gaining ground on Fisher…
Co-Regularized Deep Representations for Video Summarization
Olivier Morère, Hanlin Goh, Antoine Veillard +2
Compact keyframe-based video summaries are a popular way of generating viewership on video sharing platforms. Yet, creating relevant and compelling summaries for arbitrarily long v…
DeepHash: Getting Regularization, Depth and Fine-Tuning Right
Jie Lin, Olivier Morere, Vijay Chandrasekhar +2
This work focuses on representing very high-dimensional global image descriptors using very compact 64-1024 bit binary hashes for instance retrieval. We propose DeepHash: a hashing…