Attribute Prediction as Multiple Instance Learning
Authors: Diego Marcos, Aike Potze, Wenjia Xu, Devis Tuia, Zeynep Akata
TMLR 2022 | Venue PDF | Archive PDF | Plain Text | 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. |