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Author |
Victor Vaquero; German Ros; Francesc Moreno-Noguer; Antonio Lopez; Alberto Sanfeliu |
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Title |
Joint coarse-and-fine reasoning for deep optical flow |
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Conference Article |
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2017 |
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24th International Conference on Image Processing |
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2558-2562 |
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We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets. |
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Beijing; China; September 2017 |
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ADAS; 600.118 |
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Admin @ si @ VRM2017 |
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2898 |
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Author |
Andrei Polzounov; Artsiom Ablavatski; Sergio Escalera; Shijian Lu; Jianfei Cai |
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Title |
WordFences: Text Localization and Recognition |
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Conference Article |
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2017 |
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24th International Conference on Image Processing |
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Beijing; China; September 2017 |
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HUPBA; no menciona |
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no |
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Admin @ si @ PAE2017 |
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3007 |
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Author |
Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
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Title |
All the people around me: face clustering in egocentric photo streams |
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Conference Article |
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2017 |
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24th International Conference on Image Processing |
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face discovery; face clustering; deepmatching; bag-of-tracklets; egocentric photo-streams |
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Abstract |
arxiv1703.01790
Given an unconstrained stream of images captured by a wearable photo-camera (2fpm), we propose an unsupervised bottom-up approach for automatic clustering appearing faces into the individual identities present in these data. The problem is challenging since images are acquired under real world conditions; hence the visible appearance of the people in the images undergoes intensive variations. Our proposed pipeline consists of first arranging the photo-stream into events, later, localizing the appearance of multiple people in them, and
finally, grouping various appearances of the same person across different events. Experimental results performed on a dataset acquired by wearing a photo-camera during one month, demonstrate the effectiveness of the proposed approach for the considered purpose. |
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Beijing; China; September 2017 |
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MILAB; no menciona |
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Admin @ si @ EDR2017 |
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3025 |
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