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Training Adversarial Discriminators for Cross-Channel Abnormal Event Detection in Crowds., , , and . WACV, page 1896-1904. IEEE, (2019)Self Paced Adversarial Training for Multimodal Few-shot Learning., , , , and . CoRR, (2018)Fast but Not Deep: Efficient Crowd Abnormality Detection with Local Binary Tracklets., , , , and . AVSS, page 1-6. IEEE, (2018)Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds, and . (2019)cite arxiv:1911.02365.A Unified Objective for Novel Class Discovery., , , , , and . ICCV, page 9264-9272. IEEE, (2021)Attention Is (not) All You Need for Commonsense Reasoning., and . ACL (1), page 4831-4836. Association for Computational Linguistics, (2019)Novel dataset for fine-grained abnormal behavior understanding in crowd., , , , , and . AVSS, page 95-101. IEEE Computer Society, (2016)Learning with dataset bias in latent subcategory models., , , , , and . CVPR, page 3650-3658. IEEE Computer Society, (2015)Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models., and . EMNLP (1), page 8737-8743. Association for Computational Linguistics, (2021)Multimodal Self-supervised Learning for Medical Image Analysis., , , and . IPMI, volume 12729 of Lecture Notes in Computer Science, page 661-673. Springer, (2021)