# Image Retrieval using Multi-scale CNN Features Pooling

arXiv: https://arxiv.org/abs/2004.09695 · HTML: https://mlsanity.com/paper/2004.09695/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Exploiting CLIP-based Multi-modal Approach for Artwork Classification and Retrieval](https://mlsanity.com/paper/2309.12110/) (2023)
- [Identical Image Retrieval using Deep Learning](https://mlsanity.com/paper/2205.04883/) (2022)

## Abstract (arXiv)

In this paper, we address the problem of image retrieval by learning images representation based on the activations of a Convolutional Neural Network. We present an end-to-end trainable network architecture that exploits a novel multi-scale local pooling based on NetVLAD and a triplet mining procedure based on samples difficulty to obtain an effective image representation. Extensive experiments show that our approach is able to reach state-of-the-art results on three standard datasets.