Forschungspapier (80)

421.
Forschungspapier
Mehta, D.; Kim, K. I.; Theobalt, C.: Implicit Filter Sparsification In Convolutional Neural Networks. (2019), 4 S.
422.
Forschungspapier
Mehta, D.; Sotnychenko, O.; Mueller, F.; Xu, W.; Elgharib, M.; Fua, P.; Seidel, H.-P.; Rhodin, H.; Pons-Moll, G.; Theobalt, C.: XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera. (2019), 18 S.
423.
Forschungspapier
Miller, A.; Patt-Shamir, B.; Rosenbaum, W.: With Great Speed Come Small Buffers: Space-Bandwidth Tradeoffs for Routing. (2019), 21 S.
424.
Forschungspapier
Nag Chowdhury, S.; Razniewski, S.; Weikum, G.: Story-oriented Image Selection and Placement. (2019), 10 S.
425.
Forschungspapier
Nag Chowdhury, S.; Tandon, N.; Ferhatosmanoglu, H.; Weikum, G.: VISIR: Visual and Semantic Image Label Refinement. (2019), 9 S.
426.
Forschungspapier
Nag Chowdhury, S.; Tandon, N.; Weikum, G.: Know2Look: Commonsense Knowledge for Visual Search. (2019), 10 S.
427.
Forschungspapier
Popat, K.; Mukherjee, S.; Yates, A.; Weikum, G.: STANCY: Stance Classification Based on Consistency Cues. (2019), 6 S.
428.
Forschungspapier
Ray Chaudhury, B.; Kavitha, T.; Mehlhorn, K.; Sgouritsa, A.: A Little Charity Guarantees Almost Envy-Freeness. (2019), 20 S.
429.
Forschungspapier
Romero, J.; Razniewski, S.; Pal, K.; Pan, J. Z.; Sakhadeo, A.; Weikum, G.: Commonsense Properties from Query Logs and Question Answering Forums. (2019), 10 S.
430.
Forschungspapier
Sattar, H.; Krombholz, K.; Pons-Moll, G.; Fritz, M.: Shape Evasion: Preventing Body Shape Inference of Multi-Stage Approaches. (2019), 10 S.
431.
Forschungspapier
Shimada, S.; Golyanik, V.; Theobalt, C.; Stricker, D.: IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction. (2019), 13 S.
432.
Forschungspapier
Shimada, S.; Golyanik, V.; Tretschk, E.; Stricker, D.; Theobalt, C.: DispVoxNets: Non-Rigid Point Set Alignment with Supervised Learning Proxies. (2019)
433.
Forschungspapier
Shukla, A.; Saidi, S. J.; Schmid, S.; Canini, M.; Zinner, T.; Feldmann, A.: Consistent SDNs through Network State Fuzzing. (2019), 16 S.
434.
Forschungspapier
Stutz, D.; Hein, M.; Schiele, B.: Confidence-Calibrated Adversarial Training and Detection: More Robust Models Generalizing Beyond the Attack Used During Training. (2019)
435.
Forschungspapier
Tatti, N.; Miettinen, P.: Boolean Matrix Factorization Meets Consecutive Ones Property. (2019), 13 S.
436.
Forschungspapier
Teucke, A.; Voigt, M.; Weidenbach, C.: On the Expressivity and Applicability of Model Representation Formalisms. (2019), 15 S.
437.
Forschungspapier
Thies, J.; Elgharib, M.; Tewari, A.; Theobalt, C.; Nießner, M.: Neural Voice Puppetry: Audio-driven Facial Reenactment. (2019), 12 S.
438.
Forschungspapier
Tigunova, A.; Yates, A.; Mirza, P.; Weikum, G.: Listening between the Lines: Learning Personal Attributes from Conversations. (2019), 11 S.
439.
Forschungspapier
Tretschk, E.; Tewari, A.; Zollhöfer, M.; Golyanik, V.; Theobalt, C.: DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects. (2019), 13 S.
440.
Forschungspapier
Wang, H.; Grgic-Hlaca, N.; Lahoti, P.; Gummadi, K. P.; Weller, A.: An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision. (2019), 7 S.
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