Learning Super-Features for Image Retrieval
Authors: Philippe Weinzaepfel, Thomas Lucas, Diane Larlus, Yannis Kalantidis
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on common landmark retrieval benchmarks validate that Super-features substantially outperform state-of-the-art methods when using the same number of features, and only require a significantly smaller memory footprint to match their performance. |
| Researcher Affiliation | Industry | Philippe Weinzaepfel, Thomas Lucas, Diane Larlus, and Yannis Kalantidis NAVER LABS Europe, Grenoble, France |
| Pseudocode | No | The paper describes the proposed method but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and models are available at: https://github.com/naver/FIRe. |
| Open Datasets | Yes | We use the Sf M-120k dataset (Radenovi c et al., 2018b) following the 551/162 3D model train/val split from Tolias et al. (2020). For testing, we evaluate instance-level search on the ROxford (Philbin et al., 2007) and the RParis (Philbin et al., 2008) datasets in their revisited version (Radenovi c et al., 2018a), with and without the 1 million distractor set called R1M. |
| Dataset Splits | Yes | We use the Sf M-120k dataset (Radenovi c et al., 2018b) following the 551/162 3D model train/val split from Tolias et al. (2020). |
| Hardware Specification | No | On our server, it took 157 seconds for HOW and 172 for FIRe, i.e. extraction for Super-features only requires 10% more wall-clock time. |
| Software Dependencies | No | The paper mentions building the codebase on HOW and using an Adam optimizer and ResNet50, but does not provide specific version numbers for software dependencies like Python, PyTorch/TensorFlow, or CUDA. |
| Experiment Setup | Yes | We train our model for 200 epochs on Sf M-120k using an initial learning rate of 3.10 5 and random flipping as data augmentation. We multiply the learning rate by a factor of 0.99 at each epoch, and use an Adam optimizer with a weight decay of 10 4. We use µ1 1.1 in Eq.(8) and weight Lsuper and Lattn with 0.02 and 0.1, respectively. |