NetVLAD: CNN architecture for weakly supervised place recognition

TL;DR

This paper introduces NetVLAD, a CNN architecture with a novel VLAD layer, trained with a weakly supervised ranking loss, achieving state-of-the-art results in large-scale visual place recognition and image retrieval.

Contribution

The paper presents NetVLAD, a trainable CNN with a new VLAD layer and a weakly supervised training method for improved place recognition.

Findings

Outperforms non-learned image representations.

Achieves superior results on place recognition benchmarks.

Improves over existing compact image retrieval methods.

Abstract

We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the following three principal contributions. First, we develop a convolutional neural network (CNN) architecture that is trainable in an end-to-end manner directly for the place recognition task. The main component of this architecture, NetVLAD, is a new generalized VLAD layer, inspired by the "Vector of Locally Aggregated Descriptors" image representation commonly used in image retrieval. The layer is readily pluggable into any CNN architecture and amenable to training via backpropagation. Second, we develop a training procedure, based on a new weakly supervised ranking loss, to learn parameters of the architecture in an end-to-end manner from images depicting the same places over time downloaded from Google Street View Time Machine. Finally, we show that the proposed architecture significantly outperforms non-learnt image representations and off-the-shelf CNN descriptors on two challenging place recognition benchmarks, and improves over current state-of-the-art compact image representations on standard image retrieval benchmarks.