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VISIONE for newbies: an easier-to-use video retrieval system.

, , , , , , , и . CBMI, стр. 158-162. ACM, (2023)

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Daniele Messina

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René Messina

Giuseppe Messina

Luiz Ary Messina

 

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Text-to-Motion Retrieval: Towards Joint Understanding of Human Motion Data and Natural Language., , , и . SIGIR, стр. 2420-2425. ACM, (2023)Development of a Realistic Crowd Simulation Environment for Fine-Grained Validation of People Tracking Methods., , , , , , , , , и . VISIGRAPP (1: GRAPP), стр. 222-229. SCITEPRESS, (2023)Learning Pedestrian Detection from Virtual Worlds., , , , и . ICIAP (1), том 11751 из Lecture Notes in Computer Science, стр. 302-312. Springer, (2019)VISIONE for newbies: an easier-to-use video retrieval system., , , , , , , и . CBMI, стр. 158-162. ACM, (2023)Is CLIP the main roadblock for fine-grained open-world perception?, , , и . CoRR, (2024)AIMH at SemEval-2021 Task 6: Multimodal Classification Using an Ensemble of Transformer Models., , , и . SemEval@ACL/IJCNLP, стр. 1020-1026. Association for Computational Linguistics, (2021)Combining EfficientNet and Vision Transformers for Video Deepfake Detection., , , и . ICIAP (3), том 13233 из Lecture Notes in Computer Science, стр. 219-229. Springer, (2022)Interactive video retrieval in the age of effective joint embedding deep models: lessons from the 11th VBS., , , , , , , , , и 7 other автор(ы). Multim. Syst., 29 (6): 3481-3504 (декабря 2023)Relational Learning in Computer Vision.. University of Pisa, Italy, (2022)Fine-Grained Visual Textual Alignment for Cross-Modal Retrieval Using Transformer Encoders., , , , , и . ACM Trans. Multim. Comput. Commun. Appl., 17 (4): 128:1-128:23 (2021)