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..
Attribute Prediction as Multiple Instance Learning
Authors: Diego Marcos, Aike Potze, Wenjia Xu, Devis Tuia, Zeynep Akata
TMLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on CUB-200-2011, SUN Attributes and Aw A2 show improvements on attribute detection, attribute-based zero-shot classification and weakly supervised part localization. We evaluate AMIL using the image-level attribute annotations where available. Then, we evaluate the learned attributes on attribute-based downstream tasks: zero-shot classification and part localization. |
| Researcher Affiliation | Academia | Inria, France Wageningen University, The Netherlands Chinese Academy of Sciences, China EPFL, Switzerland University of Tübingen, Germany |
| Pseudocode | No | The paper describes methods using mathematical formulations and textual descriptions but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We use three datasets with attribute annotations: CUB-200-2011 (CUB) (Wah et al., 2011), SUN Attribute Dataset (SUN) (Patterson & Hays, 2012) and Animals With Attributes (Aw A2) (Xian et al., 2018a). |
| Dataset Splits | Yes | In all experiments we use the train-test splits proposed for ZSL in (Xian et al., 2018a) such that the evaluation is always performed on unseen classes. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., GPU models, CPU types, or memory). |
| Software Dependencies | No | The paper mentions using ResNet50 as an image encoder and the Adam optimizer but does not provide specific version numbers for these or other software libraries/frameworks. |
| Experiment Setup | Yes | All models are trained for three epochs with a multi-label binary cross-entropy loss or a noise robust loss... We use the Adam optimizer with a learning rate of 0.0001 for the attribute prediction base model and 0.001 for learning the last linear layer of the attribute prediction model, with a learning rate decay of 0.25 after each epoch. |