Graph Convolution Based Efficient Re-Ranking for Visual Retrieval, submitted to TMM

The official repository for GCR rerank, a GCN-based reranking method for image re-ID, video re-ID, and image retrieval.

Environment

We use python 3.7/torch 1.6/torchvision 0.7.0.

Datasets

image re-ID: Market, Duke, MSMT, CUHK03

video re-ID: MARS

image retrieval: ROxford, RParis

Extracted features

We provide Market1501/MARS features from reid-strong-baseline at Google Drive.

Command Lines

Run GCRV rerank with basic settings on Market1501

python eval_rerank.py --config_file=config/market.yml

Run PVG only

python eval_rerank.py --config_file=config/market.yml PVG.ENABLE_PVG True GCR.ENABLE_GCR False

Run GCR only

python eval_rerank.py --config_file=config/market.yml PVG.ENABLE_PVG False GCR.ENABLE_GCR True

RUN GCRV on video reid dataset(MARS)

python eval_rerank.py --config_file=config/mars.yml

Run other rerank methods: (baseline, k_reciprocal, ecn, ecn_orig, lbr, qe)

python eval_rerank.py --config_file=config/market.yml COMMON.RERANK_TYPE baseline

Thanks

State-of-the-art reranking method inlucidng K_reciprocal, ECN, LBR