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..
Vocabulary-free Image Classification
Authors: Alessandro Conti, Enrico Fini, Massimiliano Mancini, Paolo Rota, Yiming Wang, Elisa Ricci
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark datasets validate that Ca SED outperforms other complex vision-language frameworks, while being efficient with much fewer parameters, paving the way for future research in this direction1. We experiment on several datasets, considering both coarse(e.g. Caltech-101 [14], UCF101 [55]) and fine-grained (e.g. FGVC-Aircraft [40], Flowers-102 [43]) classification tasks. |
| Researcher Affiliation | Collaboration | Alessandro Conti1 Enrico Fini1 Massimiliano Mancini1 Paolo Rota1 Yiming Wang2 Elisa Ricci1,2 1University of Trento 2Fondazione Bruno Kessler (FBK) |
| Pseudocode | No | No. The paper describes the method steps in narrative form and with equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and demo is available at https://github.com/altndrr/vic |
| Open Datasets | Yes | Datasets. We follow existing works [53, 66] and use ten datasets that feature both coarse-grained and fine-grained classification in different domains: Caltech-101 (C101) [14], DTD [7], Euro SAT (ESAT) [21], FGVC-Aircraft (Airc.) [40], Flowers-102 (Flwr) [43], Food-101 (Food) [4], Oxford Pets (Pets), Stanford Cars (Cars) [29], SUN397 (SUN) [61], and UCF101 (UCF) [55]. Additionally, we used Image Net [10] for hyperparameters tuning. As database, we use a subset of PMD [54], containing five of its largest datasets: Conceptual Captions (CC3M) [52], Conceptual Captions 12M (CC12M) [5], Wikipedia Image Text (WIT) [56], Redcaps [12], and a subset of [57] used for PMD (YFCC100M*). |
| Dataset Splits | No | No. The paper mentions using ImageNet for hyperparameter tuning but does not provide specific training/validation/test split percentages, sample counts, or citations to predefined splits for the main datasets (Caltech-101, DTD, etc.) used for evaluation. |
| Hardware Specification | Yes | Implementation details. Our experiments were conducted using NVIDIA A6000 GPUs with mixedbit precision. |
| Software Dependencies | No | No. The paper mentions the use of CLIP and the NLP library flair (https://github.com/flair NLP/flair) but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We tuned the α hyperparameter of Eq. (6) and the number of retrieved captions K of our method on the Image Net dataset, finding that α = 0.7 and K = 10 led to the best results. We use these values across all experiments. |