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..
Fine-grained Image Classification by Visual-Semantic Embedding
Authors: Huapeng Xu, Guilin Qi, Jingjing Li, Meng Wang, Kang Xu, Huan Gao
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a challenging large-scale UCSD Bird-200-2011 dataset verify that our approach outperforms several stateof-the-art methods with significant advances. |
| Researcher Affiliation | Academia | 1 Southeast University, Nanjing, China 2 University of Electronic Science and Technology of China, Chendu, China 3 Xi an Jiaotong University, Xi an, China 4 Nanjing University of Posts and Telecommunications, Nanjing, China |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. Procedures are described in narrative text and mathematical equations. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We choose DBpedia [Lehmann et al., 2015] (KB) and English-language Wikipedia (text) from 06.01.2016 as external knowledge. Word2Vec and Trans R (described in Section 4) are used to get the class embedding. In this section, we present the experimental settings and show experimental results of our proposed model on the widely-used benchmark Caltech-UCSD Bird-200-2011 [Wah et al., 2011]. |
| Dataset Splits | No | The paper mentions using "Caltech-UCSD Bird-200-2011" and training, but does not specify the exact training/validation/test splits (e.g., percentages or sample counts) used for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU model, CPU type) used for running the experiments. |
| Software Dependencies | No | The paper mentions several deep learning architectures and techniques (e.g., Alex Net, VGG, Google Net, Res Net, Word2Vec, Trans R, batch-normalization, dropout), but does not provide specific version numbers for any underlying software dependencies (e.g., Python, TensorFlow, PyTorch). |
| Experiment Setup | Yes | We train our model using stochastic gradient descent with mini-batches 40 and learning rate 0.0015. The hyperparameter α of Eq. 7 is set to be 0.85 with cross-validation. |