In-Shop

Overview edit

Variants: In-Shop

Associated Benchmarks

This dataset is used in 2 benchmarks:

• Image Retrieval - Metrics: R@1
• Metric Learning - Metrics: R@1

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning (2024) -
• Center Contrastive Loss for Metric Learning (2023) -
• STIR: Siamese Transformer for Image Retrieval Postprocessing (2023) -
• Unicom: Universal and Compact Representation Learning for Image Retrieval (2023) -
• Fashion Image Retrieval with Multi-Granular Alignment (2023) -
• Hyperbolic Vision Transformers: Combining Improvements in Metric Learning (2022) -
• Dissecting the impact of different loss functions with gradient surgery (2022) -
• It Takes Two to Tango: Mixup for Deep Metric Learning (2021) -
• Hard negative examples are hard, but useful (2020) -
• Calibrated neighborhood aware confidence measure for deep metric learning (2020) -
• ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis (2020) -
• A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses (2020) -
• Cross-Batch Memory for Embedding Learning (2019) -
• Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning (2019) -
• Improved Embeddings with Easy Positive Triplet Mining (2019) -
• Combination of Multiple Global Descriptors for Image Retrieval (2019) -
• Classification is a Strong Baseline for Deep Metric Learning (2018) -
• Attention-based Ensemble for Deep Metric Learning (2018) -