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Computer vision finds itself at an exciting stage of its development. Many of its areas are approaching sufficiently high performance levels to become useful for real-world applications, and numerous interesting connections are opening up to related fields such as machine learning, graphics, and mobile robotics. Moreover, instead of only focusing on a single area and advancing it through step-by-step progress, it now becomes possible for the first time to combine many different vision capabilities and explore the benefits that can be reaped in through their close integration.
Our research aims exactly at this interface. The central theme of our work is the connection of different areas of computer vision and graphics into so-called "cognitive loops", collaborative feedback cycles in which multiple vision modalities mutually support each other in order to solve a bigger task than any could do on its own. Object recognition takes a key role in this integration, since it can deliver a semantic interpretation of the image content, which considerably simplifies other tasks such as segmentation, 3D reconstruction, and tracking. In some cases, such connections are even required in order to render complex applications possible in the first place. In return, those other vision capabilities deliver additional information which again constrains and improves the recognition results.
Our research aims exactly at this interface. The central theme of our work is the connection of different areas of computer vision and graphics into so-called "cognitive loops", collaborative feedback cycles in which multiple vision modalities mutually support each other in order to solve a bigger task than any could do on its own. Object recognition takes a key role in this integration, since it can deliver a semantic interpretation of the image content, which considerably simplifies other tasks such as segmentation, 3D reconstruction, and tracking. In some cases, such connections are even required in order to render complex applications possible in the first place. In return, those other vision capabilities deliver additional information which again constrains and improves the recognition results.
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论文共 290 篇作者统计合作学者相似作者
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Patrick Wienholt,Alexander Hermans,Firas Khader, Behrus Puladi,Bastian Leibe,Christiane Kuhl,Sven Nebelung,Daniel Truhn
CoRR (2024)
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Zicong Fan,Takehiko Ohkawa,Linlin Yang, Nie Lin, Zhishan Zhou, Shihao Zhou, Jiajun Liang, Zhong Gao, Xuanyang Zhang, Xue Zhang, Fei Li, Liu Zheng,
arxiv(2024)
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Johannes Raufeisen, Kunpeng Xie, Fabian Hörst,Till Braunschweig,Jianning Li,Jens Kleesiek, Rainer Röhrig,Jan Egger,Bastian Leibe,Frank Hölzle,Alexander Hermans,Behrus Puladi
CoRR (2024)
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Jonas Schult, Sam Tsai, Lukas Hoellein,Bichen Wu, Jialiang Wang, Chih-Yao Ma,Kunpeng Li,Xiaofang Wang, Felix Wimbauer,Zijian He,Peizhao Zhang,Bastian Leibe,
CVPR 2024 (2024)
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CVPR 2023 (2023): 18738-18748
Lecture Notes in Computer Science Pattern Recognition (2023): 131-146
CoRR (2023)
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CoRR (2023)
Jonas Schult, Sam Tsai, Lukas Höllein,Bichen Wu,Jialiang Wang, Chih-Yao Ma,Kunpeng Li,Xiaofang Wang,Felix Wimbauer,Zijian He,Peizhao Zhang,Bastian Leibe,
CoRR (2023)
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