Combination of Multiple Global Descriptors for Image Retrieval

arXiv 2019 HeeJae JunByungsoo KoYoungjoon KimInsik KimJongtack Kim

Recent studies in image retrieval task have shown that ensembling different models and combining multiple global descriptors lead to performance improvement. However, training different models for the ensemble is not only difficult but also inefficient with respect to time and memory... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Image Retrieval CARS196 CGD (MG/SG) [email protected] 94.8 # 1
Image Retrieval CUB-200-2011 CGD (MG/SG) [email protected] 79.2 # 1
Image Retrieval In-Shop CGD (SG/GS) [email protected] 91.9 # 1
Image Retrieval SOP CGD (SG/GS) [email protected] 84.2 # 1

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet