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Combined Static and Motion Features for Deep-Networks-Based Activity Recognition in Videos.

, , , , , and . IEEE Trans. Circuits Syst. Video Technol., 29 (9): 2693-2707 (2019)

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Combined Static and Motion Features for Deep-Networks Based Activity Recognition in Videos., , , , , and . CoRR, (2018)Incremental Object Detection via Meta-Learning., , , , , and . CoRR, (2020)On the Benefits of 3D Pose and Tracking for Human Action Recognition., , , , and . CVPR, page 640-649. IEEE, (2023)DeepCaps: Going Deeper With Capsule Networks., , , , , and . CVPR, page 10725-10733. Computer Vision Foundation / IEEE, (2019)Combined Static and Motion Features for Deep-Networks-Based Activity Recognition in Videos., , , , , and . IEEE Trans. Circuits Syst. Video Technol., 29 (9): 2693-2707 (2019)Humans in 4D: Reconstructing and Tracking Humans with Transformers., , , , and . ICCV, page 14737-14748. IEEE, (2023)Self-supervised Knowledge Distillation for Few-shot Learning., , , , and . BMVC, page 179. BMVA Press, (2021)Meta-learning the Learning Trends Shared Across Tasks., , , , and . BMVC, page 182. BMVA Press, (2021)A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps., , , , and . WWW, page 3165-3171. ACM, (2019)EgoPet: Egomotion and Interaction Data from an Animal's Perspective., , , , , , , and . CoRR, (2024)