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Fully Convolutional Crowd Counting on Highly Congested Scenes., , , and . VISIGRAPP (5: VISAPP), page 27-33. SciTePress, (2017)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)Performance of video processing at the edge for crowd-monitoring applications., , , , and . WF-IoT, page 482-487. IEEE, (2018)People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting., , , , and . CoRR, (2017)Holistic features for real-time crowd behaviour anomaly detection., , , and . ICIP, page 918-922. IEEE, (2016)Large-Scale Bidirectional Training for Zero-Shot Image Captioning., , , , , , and . CoRR, (2022)Insight DCU at TRECVid 2015., , , , , , , , , and 2 other author(s). TRECVID, National Institute of Standards and Technology (NIST), (2015)Intraoperative Margin Assessment in Oral and Oropharyngeal Cancer Using Label-Free Fluorescence Lifetime Imaging and Machine Learning., , , , , , , , , and . IEEE Trans. Biomed. Eng., 68 (3): 857-868 (2021)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)