Pith. sign in

REVIEW

2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1906.03990 v2 pith:KYWOUALW submitted 2019-06-10 cs.CV

classification cs.CV
keywords recognitionlandmarkretrievalplaceresultschallengecompetitionfeature
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

We present a retrieval based system for landmark retrieval and recognition challenge.There are five parts in retrieval competition system, including feature extraction and matching to get candidates queue; database augmentation and query extension searching; reranking from recognition results and local feature matching. In recognition challenge including: landmark and non-landmark recognition, multiple recognition results voting and reranking using combination of recognition and retrieval results. All of models trained and predicted by PaddlePaddle framework. Using our method, we achieved 2nd place in the Google Landmark Recognition 2019 and 2nd place in the Google Landmark Retrieval 2019 on kaggle. The source code is available at here.

Discussion (0). Continue with ORCID to comment.

Pith tools