2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVSMR-2.3
Paper Title SYNTACTICALLY GUIDED GENERATIVE EMBEDDINGS FOR ZERO-SHOT SKELETON ACTION RECOGNITION
Authors Pranay Gupta, Divyanshu Sharma, Ravi Kiran Sarvadevabhatla, International Institute of Information Technology, Hyderabad, India
SessionMLR-APPL-IVSMR-2: Machine learning for image and video sensing, modeling and representation 2
LocationArea D
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video sensing, modeling, and representation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract We introduce SynSE (Syntactically guided Skeleton Embeddings), a novel syntactically guided generative approach for Zero-Shot Learning (ZSL). Our end-to-end approach learns progressively refined generative embedding spaces constrained within and across the involved modalities (visual, language). The inter-modal constraints are defined between action sequence embedding and embeddings of Parts of Speech (PoS) tagged words in the corresponding action description. We deploy SynSE for the task of skeleton-based action sequence recognition. Our design choices enable SynSE to generalize compositionally, i.e., recognize sequences whose action descriptions contain words not encountered during training. We also extend our approach to the more challenging Generalized Zero-Shot Learning (GZSL) problem via a confidence-based gating mechanism. We are the first to present zero-shot skeleton action recognition results on the large-scale NTU-60 and NTU-120 skeleton action datasets with multiple splits. Our results demonstrate SynSE's state-of-the-art performance in both ZSL and GZSL settings compared to strong baselines on the NTU-60 and NTU-120 datasets.