Zobrazeno 1 - 10
of 13 058
pro vyhledávání: '"A. Cremers"'
In this paper we propose MA-DV2F: Multi-Agent Dynamic Velocity Vector Field. It is a framework for simultaneously controlling a group of vehicles in challenging environments. DV2F is generated for each vehicle independently and provides a map of refe
Externí odkaz:
http://arxiv.org/abs/2411.06404
Autor:
Wysocki, Olaf, Tan, Yue, Froech, Thomas, Xia, Yan, Wysocki, Magdalena, Hoegner, Ludwig, Cremers, Daniel, Holst, Christoph
Facade semantic segmentation is a long-standing challenge in photogrammetry and computer vision. Although the last decades have witnessed the influx of facade segmentation methods, there is a lack of comprehensive facade classes and data covering the
Externí odkaz:
http://arxiv.org/abs/2411.04865
Autor:
Cong, Bai, Daheim, Nico, Shen, Yuesong, Cremers, Daniel, Yokota, Rio, Khan, Mohammad Emtiyaz, Möllenhoff, Thomas
We show that variational learning can significantly improve the accuracy and calibration of Low-Rank Adaptation (LoRA) without a substantial increase in the cost. We replace AdamW by the Improved Variational Online Newton (IVON) algorithm to finetune
Externí odkaz:
http://arxiv.org/abs/2411.04421
Post-training quantization is widely employed to reduce the computational demands of neural networks. Typically, individual substructures, such as layers or blocks of layers, are quantized with the objective of minimizing quantization errors in their
Externí odkaz:
http://arxiv.org/abs/2411.03934
Autor:
Ehm, Viktoria, Amrani, Nafie El, Xie, Yizheng, Bastian, Lennart, Gao, Maolin, Wang, Weikang, Sang, Lu, Cao, Dongliang, Lähner, Zorah, Cremers, Daniel, Bernard, Florian
Finding correspondences between 3D shapes is an important and long-standing problem in computer vision, graphics and beyond. While approaches based on machine learning dominate modern 3D shape matching, almost all existing (learning-based) methods re
Externí odkaz:
http://arxiv.org/abs/2411.03511
Autor:
Weber, Mark, Yu, Lijun, Yu, Qihang, Deng, Xueqing, Shen, Xiaohui, Cremers, Daniel, Chen, Liang-Chieh
Masked transformer models for class-conditional image generation have become a compelling alternative to diffusion models. Typically comprising two stages - an initial VQGAN model for transitioning between latent space and image space, and a subseque
Externí odkaz:
http://arxiv.org/abs/2409.16211
Autor:
Cheng, Lei, Hu, Junpeng, Yan, Haodong, Gladkova, Mariia, Huang, Tianyu, Liu, Yun-Hui, Cremers, Daniel, Li, Haoang
Photometric bundle adjustment (PBA) is widely used in estimating the camera pose and 3D geometry by assuming a Lambertian world. However, the assumption of photometric consistency is often violated since the non-diffuse reflection is common in real-w
Externí odkaz:
http://arxiv.org/abs/2409.11854
Autor:
Cremers, Jolien, Kohler, Benjamin, Maier, Benjamin Frank, Eriksen, Stine Nymann, Einsiedler, Johanna, Christensen, Frederik Kølby, Lehmann, Sune, Lassen, David Dreyer, Mortensen, Laust Hvas, Bjerre-Nielsen, Andreas
Social networks shape individuals' lives, influencing everything from career paths to health. This paper presents a registry-based, multi-layer and temporal network of the entire Danish population in the years 2008-2021 (roughly 7.2 mill. individuals
Externí odkaz:
http://arxiv.org/abs/2409.11099
Contemporary research in autonomous driving has demonstrated tremendous potential in emulating the traits of human driving. However, they primarily cater to areas with well built road infrastructure and appropriate traffic management systems. Therefo
Externí odkaz:
http://arxiv.org/abs/2409.05119
Autor:
Yang, Linyan, Hoyer, Lukas, Weber, Mark, Fischer, Tobias, Dai, Dengxin, Leal-Taixé, Laura, Pollefeys, Marc, Cremers, Daniel, Van Gool, Luc
Unsupervised Domain Adaptation (UDA) is the task of bridging the domain gap between a labeled source domain, e.g., synthetic data, and an unlabeled target domain. We observe that current UDA methods show inferior results on fine structures and tend t
Externí odkaz:
http://arxiv.org/abs/2408.16478