  Motivated by recent findings from cognitive neural science, we advocate the
use of a dual-level model for concept representations: the embodied level
consists of concept-oriented feature representations, and the symbolic level
consists of concept graphs. Embodied concept representations are modality
specific and exist in the form of feature vectors in a feature space. Symbolic
concept representations, on the other hand, are amodal and language specific,
and exist in the form of word / knowledge-graph embeddings in a concept /
knowledge space. The human conceptual system comprises both embodied
representations and symbolic representations, which typically interact to drive
conceptual processing. As such, we further advocate the use of dual
embodied-symbolic concept representations for deep learning. To demonstrate
their usage and value, we discuss two important use cases: embodied-symbolic
knowledge distillation for few-shot class incremental learning, and
embodied-symbolic fused representation for image-text matching. Dual
embodied-symbolic concept representations are the foundation for deep learning
and symbolic AI integration. We discuss two important examples of such
integration: scene graph generation with knowledge graph bridging, and
multimodal knowledge graphs.
