Robust and Decomposable Average Precision for Image Retrieval
Authors: Elias Ramzi, Nicolas THOME, Clément Rambour, Nicolas Audebert, Xavier Bitot
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on three image retrieval datasets show that ROADMAP outperforms several recent AP approximation methods and highlight the importance of our two contributions. Finally, using ROADMAP for training deep models yields very good performances, outperforming state-of-the-art results on the three datasets. |
| Researcher Affiliation | Collaboration | 1CEDRIC, Conservatoire National des Arts et Métiers, Paris, France 2Coexya, Paris, France |
| Pseudocode | No | The paper describes its methods using mathematical equations and textual explanations, but no pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | Code and instructions to reproduce our results will be made publicly available at https://github.com/elias-ramzi/ROADMAP. |
| Open Datasets | Yes | We evaluate ROADMAP on the following three image retrieval datasets: CUB-200-2011 [37]... Stanford Online Product (SOP) [31]... INaturalist-2018 [36]. |
| Dataset Splits | Yes | We follow the standard protocol and use the first (resp. last) 100 classes for training (resp. evaluation). We use the reference train and test splits from [31]. We use the splits from [2] with 70% of the classes in the train set and the rest in the test set. |
| Hardware Specification | No | The paper mentions 'HPC resources of IDRIS under the allocation 2021AD011012645 made by GENCI' but does not provide specific hardware details like GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper mentions using 'Adam', 'AdamW', and 'PyTorch implementations' but does not specify version numbers for any of these software components. |
| Experiment Setup | Yes | For all experiments in Section 4.1 and Section 4.2, we use λ = 0.5 for LROADMAP in Eq. (2), τ = 0.01 and ρ = 100 for LSup AP in Eq. (5), α = 0.9 and β = 0.6 for Lcalibr. in Eq. (7). All models are trained in the same setting (Res Net-50 backbone, embedding size 512, batch size 64). |