Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Token-Based Representation for Image Retrieval
Authors: Hui Wu, Min Wang, Wengang Zhou, Yang Hu, Houqiang Li2703-2711
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted to evaluate our approach, which outperforms the state-of-the-art methods on the Revisited Oxford and Paris datasets. |
| Researcher Affiliation | Academia | 1 CAS Key Laboratory of GIPAS, University of Science and Technology of China 2 Institute of Artificial Intelligence, Hefei Comprehensive National Science Center EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes its method using text and mathematical equations, and includes a block diagram (Figure 2), but no pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | The clean version of Google landmarks dataset V2 (GLDv2-clean) (Weyand et al. 2020) is used for training. |
| Dataset Splits | Yes | We randomly divide it into two subsets train / val with 80%/20% split. The train split is used for training model, and the val split is used for validation. |
| Hardware Specification | Yes | We use a batch size of 128 to train our model on 4 NVIDIA RTX 3090 GPUs for 30 epochs... on a single thread GPU (RTX 3090) / CPU (Intel Xeon CPU E5-2640 v4 @ 2.40GHz). |
| Software Dependencies | No | The paper mentions using SGD as the optimizer but does not specify version numbers for any software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages. |
| Experiment Setup | Yes | We use a batch size of 128 to train our model on 4 NVIDIA RTX 3090 GPUs for 30 epochs... SGD is used to optimize the model, with an initial learning rate of 0.01, a weight decay of 0.0001, and a momentum of 0.9. ... The dimension d of the global feature is set as 1024. For the Arc Face margin loss, we empirically set the margin m as 0.2 and the scale γ as 32.0. Refinement block number N is set to 2. |