[FEATURE_REQUEST] Adding the 2020 SOTA paper for image retrieval (similarity scenario)

[Mohammadhossein Zarei]

Description

The current SOTA paper for image retrieval was published in 2020. It's called "Combination of Multiple Global Descriptors for Image Retrieval," and you can find it here.

Here's what the results table would look like with the addition of this paper:

Paper	Year	Uses triplet learning	Recall@1 CARS196	Recall@1 CUB200-2011	Recall@1 SOP
Deep Metric Learning via Lifted Structured Feature Embedding	CVPR 2016		49%	47%	62%
Deep Metric learning with angular loss	ICCV 2017	Yes	71%	55%	71%
Sampling Matters in Deep Embedding Learning	ICCV 2017	Yes	80%	64%	73%
No Fuss Distance Metric Learning using Proxies	ICCV 2017	Yes	73%	49%	74%
Deep metric learning with hierarchical triplet loss	ECCV 2018	Yes	81%	57%	75%
Classification is a Strong Baseline for DeepMetric Learning (Implemented in this repository)	BMVC 2019	No	84% (512-dim) 89% (2048-dim)	61% (512-dim) 65% (2048-dim)	78% (512-dim) 80% (2048-dim)
Combination of Multiple Global Descriptors for Image Retrieval	2020	Yes	94.8% (1536-dim)	79.2% (1536-dim)	84.2% (1536-dim)

Although the paper uses the triplet loss (which is somewhat against the idea of this section), I believe the improvements are so considerable that it is worth ignoring that fact.

Also, the implementation code is available here, but it needs a few adjustments to be suitable for this repo. I was wondering if I could be helpful by doing so.

Expected behavior with the suggested feature

Other Comments

[PatrickBue]

Thanks for the suggestion. Next time we update the code/table we will also take this reference into account.