The Neuro-Symbolic Concept Learner:Interpreting Scenes, Words, and Sentencesfrom Natural Supervision
Figure 7: Left: An example image-question pair from the VQS dataset and the corresponding execution trace of NS-CL. Right: Results on the VQS test set. Our model achieves a comparable results with the baselines.
['Model', 'Accuracy']
All questions in the VQS dataset can be visually grounded: each question is associated with multiple image regions, annotated by humans as essential for answering the question. We use a syntactic dependency parser to extract programs and concepts from language The object proposals and features are extracted from models pre-trained on the MS-COCO dataset and the ImageNet dataset, respectively. Illustrated in Beyond answering questions, NS-CL effectively learns visual concepts from data.