Re-ranking for image retrieval and transductive few-shot classification
Authors: Xi SHEN, Yang Xiao, Shell Xu Hu, Othman Sbai, Mathieu Aubry
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3 Experiments In this section, we cover our experimental setups and results for image retrieval and few-shot image classification. Since these two problems are different in data processing and performance evaluation, we separate the discussions into two sub-sections followed by a joint ablation study. |
| Researcher Affiliation | Collaboration | Xi Shen1, Yang Xiao2, Shell Xu Hu3, Othman Sbai4, and Mathieu Aubry5 1, 2, 4, 5LIGM (UMR 8049), École des Ponts Paris Tech 3Samsung AI Center, Cambridge |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://imagine.enpc.fr/~shenx/SSR/. |
| Open Datasets | Yes | We consider five image retrieval datasets, namely, CUB [67], CARS [31], SOP [62], r Oxford5K [49] and r Paris6K [49]. mini-Image Net [66], tiered-Image Net [51], and CIFAR-FS [4]. |
| Dataset Splits | Yes | for CUB, the first 100 species (5,864 images) are used for training and the remaining 100 species (5,924 images) are used for testing; for CARS, the first 98 classes (8,054 images) are used for training and the other 98 classes (8,131 images) are kept for testing; for SOP, the dataset is separated into 11,318 training classes (59,551 images) and 11,316 testing classes (60 502 images). mini-Image Net contains 100 classes and 600 images per class. It is split into 64 classes for training, 16 for validation and 10 for testing. tiered-Image Net contains a larger subset of Image Net with 608 classes and 1 300 images per class. It is split into 351 classes for training, 97 for validation and 160 for testing. CIFAR-FS was created by dividing the original CIFAR-100 [32] into 64 training classes, 16 validation classes and 20 testing classes. |
| Hardware Specification | Yes | The entire training on CUB [67] takes 6 hours on a single Ge Force 1080 Ti GPU. For few-shot classification, we first train for 30K iterations with T = 1: the learning rate is set to 0.1 for 5K iterations then to 0.01 for another 25K iterations. Then, keeping a learning rate of 0.01, we train for 10K iterations with T = 2 and 10K more with T = 3. We find that T = 3 leads to the most stable improvement and include this analysis in the supplementary material. The whole training process on mini-Image Net [66] takes 20 hours on a single Ge Force 1080 Ti GPU. |
| Software Dependencies | No | The paper mentions software components like 'Re LU activations' and 'Instance Normalization' but does not specify version numbers for any libraries or frameworks used (e.g., PyTorch, TensorFlow). |
| Experiment Setup | Yes | Each subgraph update in our SSR module is performed by a three-layer perceptron with constant hidden-layer size 1,024 for image retrieval and 4,096 for few-shot classification. We optimize our networks using SGD with momentum 0.9. The batch size is set to 1. For image retrieval, we use a single update of the model (T = 1) and training converges in 10K iterations with a fixed learning rate of 1e-5. For few-shot classification, we first train for 30K iterations with T = 1: the learning rate is set to 0.1 for 5K iterations then to 0.01 for another 25K iterations. Then, keeping a learning rate of 0.01, we train for 10K iterations with T = 2 and 10K more with T = 3. |