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Conference Paper
Neuro-Symbolic Homogeneous Concept Reasoning for Scene Interpretation
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- Authors
- Sangwon Kim, Byoung Chul Ko, In-Su Jang, Kwang-Ju Kim
- Issue Date
- 2024-08
- Citation
- International Conference on Platform Technology and Service (PlatCon) 2024, pp.227-231
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Interpreting visual scenes is a complex challenge in artificial intelligence (AI), requiring the integration of pattern recognition and logical reasoning. This paper introduces neurosymbolic based scene interpretation (NeuroScene), a novel approach combining neural networks and symbolic AI for enhanced scene understanding. NeuroScene employs a pre-trained ResNet for feature extraction and Mask R-CNN for identifying regions of interest. These features are converted into homogeneous concept representations using vector quantization. The semantic parsing module translates natural language questions into executable programs using operations from the CLEVR dataset, which are then used for neuro-symbolic reasoning over the constructed scene graph. Experiments on the CLEVR dataset demonstrate NeuroScene’s effectiveness in interpreting complex visual scenes and providing interpretable answers. By integrating neural and symbolic components, NeuroScene ensures that high-level reasoning is informed by precise visual features, enhancing the clarity and usefulness of the model’s outputs. This approach shows promise for applications requiring detailed scene understanding and logical reasoning.
- KSP Keywords
- Concept representations, Feature extractioN, Logical reasoning, Natural Language Questions, Novel approach, Pattern recognition, R-CNN, Scene Understanding, Scene graph, Scene interpretation, Semantic parsing