Module 4 contains the higher-level symbolic processes that are important for human visual understanding, including inference on images, language, and videos. We already have begun exploring these more abstract aspects of symbolic cognition.
Symbolic Compositional Models
Recent Publications
, “How Does the Brain Represents Language and Answers Questions? Using an AI System to Understand the Underlying Neurobiological Mechanisms”, Frontiers in Computational Neuroscience, vol. 13, 2019.
CBMM Funded
, “Partially Occluded Hands: A challenging new dataset for single-image hand pose estimation”, in The 14th Asian Conference on Computer Vision (ACCV 2018), 2018.
partially-occluded-hands-6.pdf (8.29 MB)
partially-occluded-hands-6.pdf (8.29 MB) CBMM Funded
, “Deep sequential models for sampling-based planning”, in The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), Madrid, Spain, 2018. kuo2018planning.pdf (637.67 KB)
kuo2018planning.pdf (637.67 KB) CBMM Funded
, “Grounding language acquisition by training semantic parsersusing captioned videos”, in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), , Brussels, Belgium, 2018. Ross-et-al_ACL2018_Grounding language acquisition by training semantic parsing using caption videos.pdf (3.5 MB)
Ross-et-al_ACL2018_Grounding language acquisition by training semantic parsing using caption videos.pdf (3.5 MB) CBMM Funded
, “Image interpretation above and below the object level”, Interface Focus, vol. 8, no. 4, p. 20180020, 2018.
CBMM Funded