Helmholtz Center for Information Security
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Distinguishing between Topical and Non-Topical Information Diffusion Mechanisms in Social Media
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Water Treatment, Distribution, and Electric Power Testbeds for Research in the Design of Secure Interdependent Critical Infrastructures
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DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
No full textThe goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations; and (3) an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speed-up factors. We evaluate our approach on two single-person and two multi-person pose estimation benchmarks. The proposed approach significantly outperforms best known multi-person pose estimation results while demonstrating competitive performance on the task of single person pose estimation. Models and code available at http://pose.mpi-inf.mpg.d
