Context-Aware Zero-Shot Learning for Object Recognition
Authors: Eloi Zablocki, Patrick Bordes, Laure Soulier, Benjamin Piwowarski, Patrick Gallinari
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, through extensive experiments conducted on Visual Genome, we show that contextual information can substantially improve the standard ZSL approach and is robust to unbalanced classes. |
| Researcher Affiliation | Collaboration | 1Sorbonne Université, CNRS, Laboratoire d Informatique de Paris 6, LIP6, F-75005 Paris, France 2Criteo AI Lab, Paris. |
| Pseudocode | No | The paper describes its methods using mathematical formulations and textual explanations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: 'To facilitate future work on context-aware ZSL, we publicly release data splits and annotations 1.' Footnote 1 provides a URL. However, this explicitly refers to 'data splits and annotations' and not the source code for the methodology described in the paper. |
| Open Datasets | Yes | We rather use Visual Genome (Krishna et al., 2017), a large-scale image dataset (108K images) annotated at a fine-grained level (3.8M object instances), covering various concepts (105K unique object names). |
| Dataset Splits | Yes | In order to shape the data to our task, we randomly split the set of images of Visual Genome into train/validation/test sets (70%/10%/20% of the total size). |
| Hardware Specification | No | The paper mentions using a pre-trained Inception-v3 CNN and the Adam optimizer, but it does not specify any hardware details such as GPU or CPU models, memory, or cloud computing instance types used for experiments. |
| Software Dependencies | No | The paper refers to algorithms and models like Skip-Gram, Inception-v3 CNN, and Adam, along with their respective citations, but it does not provide specific software version numbers for any libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | For each objective LC, LV and LP , at each iteration of the learning algorithm, 5 negative entities are sampled per positive example. Word representations are vectors of R300, learned with the Skip-Gram algorithm (Mikolov et al., 2013) on Wikipedia. Image regions are cropped, rescaled to (299 × 299), and fed to CNN, an Inception-v3 CNN (Szegedy et al., 2016)... Models are trained with Adam (Kingma & Ba, 2014) and regularized with a L2-penalty... |