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We study the features extracted from the second last Alireza Zareian, Svebor Karaman, Shih-Fu Chang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 3736-3745 Abstract Scene Graph Generation (SGG) aims to extract entities, predicates and their semantic structure from images, enabling deep understanding of visual content, with many applications such as visual reasoning and image retrieval. Email addresses: svebor.karaman@unifi.it (Svebor Karaman), giuseppe.lisanti@unifi.it (Giuseppe Lisanti), bagdanov@cvc.uab.es (Andrew D. Bagdanov), alberto.delbimbo@unifi.it (Alberto Del Bimbo) 1Media Integration and Communication Center (MICC), University of Florence, Viale Morgagni 65, Firenze 50134, Italy. 2 Svebor Karaman, Giuseppe Lisanti, Andrew D. Bagdanov, Alberto Del Bimbo sential. In realistic, wide-area surveillance scenarios such as airports, metro and train stations, re-identiﬁcation systems should be capable of robustly associating a unique identity with hundreds, if not thousands, of individual observations collected from a 2021-04-23 · MCK-CCA: Multi Channel-Kernel Canonical Correlation Analysis for Cross-View Person Re-Identification.

Youngwook Kee, Weizhong Zhang. Dmitry Kit, Guang-Tong Zhou. Authors: Karaman, Svebor1 svebor.karaman@unifi.it. Bagdanov, Andrew2 bagdanov@cvc.uab.es. Landucci, Lea1 lea.landucci@unifi.it.

Svebor Karaman; Affiliations. University of Florence (20) University of Bordeaux (7) Universite Paul Sabatier Toulouse III (4) Laboratoire Bordelais de Recherche en

Authors; Authors and affiliations. Svebor Karaman; Andrew D. Bagdanov.

Joseph G. Ellis, Svebor Karaman, Hongzhi Li, Hong Bin Shim and Shih-Fu Chang Columbia University {jge2105, svebor.karaman, hongzhi.li, h.shim, sc250}@columbia.edu ABSTRACT With the growth of social media platforms in recent years, social media is now a major source of information and news for many peo-ple around the world.

Columbia University svebor. karaman@columbia.edu. Abstract. Researchers in computer science have spent.

Home Svebor Karaman. Svebor Karaman. Author’s Email; Skip slideshow Svebor Karaman; Affiliations.
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Svebor Karaman, Matteo Zanotto. Youngwook Kee, Weizhong Zhang. Dmitry Kit, Guang-Tong Zhou. Detecting and Simulating Artifacts in GAN Fake Images (Extended Version) Xu Zhang, Svebor Karaman, and Shih-Fu Chang Identity inference: generalizing person. re-identiﬁcation scenarios.

2020. The Politi- cal Visual Literacy App: Giuseppe Lisanti, Svebor Karaman and Iacopo Masi.
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DOI: 10.1007/978-3-030-58592-1_36 Corpus ID: 210064217. Bridging Knowledge Graphs to Generate Scene Graphs @inproceedings{Zareian2020BridgingKG, title={Bridging Knowledge Graphs to Generate Scene Graphs}, author={Alireza Zareian and Svebor Karaman and Shih-Fu Chang}, booktitle={ECCV}, year={2020} }

However, the specific model used by the attacker is often unavailable. To address this, we propose a GAN simulator, AutoGAN, which can simulate the artifacts produced by the common pipeline shared by several popular GAN models Svebor KARAMAN Andrew Bagdanov Identity Inference: Generalizing Person Re-identification Scenarios Svebor Karaman and Andrew D. Bagdanov Media Integration and Communication Center University of Florence, Viale Morgagni 65, Florence, Italy svebor.karaman@unifi.it, bagdanov@dsi.unifi.it Abstract. Hassan Akbari, Svebor Karaman, Surabhi Bhargava, Brian Chen, Carl Vondrick, Shih-Fu Chang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 12476-12486 2020-01-07 · Authors: Alireza Zareian, Svebor Karaman, Shih-Fu Chang Download PDF Abstract: Scene graphs are powerful representations that parse images into their abstract semantic elements, i.e., objects and their interactions, which facilitates visual comprehension and explainable reasoning.