Search Results for author: Antoine Veillard

Found 7 papers, 0 papers with code

Compression of Deep Neural Networks for Image Instance Retrieval

no code implementations18 Jan 2017 Vijay Chandrasekhar, Jie Lin, Qianli Liao, Olivier Morère, Antoine Veillard, Ling-Yu Duan, Tomaso Poggio

One major drawback of CNN-based {\it global descriptors} is that uncompressed deep neural network models require hundreds of megabytes of storage making them inconvenient to deploy in mobile applications or in custom hardware.

Image Instance Retrieval Model Compression +2

Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval

no code implementations15 Mar 2016 Olivier Morère, Jie Lin, Antoine Veillard, Vijay Chandrasekhar, Tomaso Poggio

The first one is Nested Invariance Pooling (NIP), a method inspired from i-theory, a mathematical theory for computing group invariant transformations with feed-forward neural networks.

Image Instance Retrieval Retrieval +1

Group Invariant Deep Representations for Image Instance Retrieval

no code implementations9 Jan 2016 Olivier Morère, Antoine Veillard, Jie Lin, Julie Petta, Vijay Chandrasekhar, Tomaso Poggio

Based on a thorough empirical evaluation using several publicly available datasets, we show that our method is able to significantly and consistently improve retrieval results every time a new type of invariance is incorporated.

Dimensionality Reduction Image Classification +3

Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing

no code implementations10 Nov 2015 Jie Lin, Olivier Morère, Julie Petta, Vijay Chandrasekhar, Antoine Veillard

Then, triplet networks, a rank learning scheme based on weight sharing nets is used to fine-tune the binary embedding functions to retain as much as possible of the useful metric properties of the original space.

Image Classification Image Retrieval +2

Co-Regularized Deep Representations for Video Summarization

no code implementations30 Jan 2015 Olivier Morère, Hanlin Goh, Antoine Veillard, Vijay Chandrasekhar, Jie Lin

A comprehensive user study is conducted comparing our proposed method to a variety of schemes, including the summarization currently in use by one of the most popular video sharing websites.

Informativeness Video Summarization

DeepHash: Getting Regularization, Depth and Fine-Tuning Right

no code implementations20 Jan 2015 Jie Lin, Olivier Morere, Vijay Chandrasekhar, Antoine Veillard, Hanlin Goh

This work focuses on representing very high-dimensional global image descriptors using very compact 64-1024 bit binary hashes for instance retrieval.

Retrieval

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