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Fully Convolutional Crowd Counting on Highly Congested Scenes., , , and . VISIGRAPP (5: VISAPP), page 27-33. SciTePress, (2017)Performance of video processing at the edge for crowd-monitoring applications., , , , and . WF-IoT, page 482-487. IEEE, (2018)Dublin City University and Partners' Participation in the INS and VTT Tracks at TRECVid 2016., , , , , , , , , and 7 other author(s). TRECVID, National Institute of Standards and Technology (NIST), (2016)Investigating Class-Level Difficulty Factors In Multi-Label Classification Problems., , , , , , , , and . ICME, page 1-6. IEEE, (2020)Smart Stadium for Smarter Living: Enriching the Fan Experience., , , , , , , and . ISM, page 152-157. IEEE Computer Society, (2016)People, Penguins and Petri Dishes: Adapting Object Counting Models to New Visual Domains and Object Types Without Forgetting., , , , and . CVPR, page 8070-8079. Computer Vision Foundation / IEEE Computer Society, (2018)Holistic features for real-time crowd behaviour anomaly detection., , , and . ICIP, page 918-922. IEEE, (2016)Insight DCU at TRECVid 2015., , , , , , , , , and 2 other author(s). TRECVID, National Institute of Standards and Technology (NIST), (2015)ResnetCrowd: A residual deep learning architecture for crowd counting, violent behaviour detection and crowd density level classification., , , and . AVSS, page 1-7. IEEE Computer Society, (2017)NICE: CVPR 2023 Challenge on Zero-shot Image Captioning., , , , , , , , , and 32 other author(s). CoRR, (2023)