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| CVPR 2015 | |
|---|---|
| Name | Conference on Computer Vision and Pattern Recognition 2015 |
| Abbrev | CVPR 2015 |
| Genre | Academic conference |
| Date | June 2015 |
| Location | Boston, Massachusetts |
| Organizer | IEEE Computer Society, IEEE |
CVPR 2015 was the 2015 edition of the annual international conference focused on computer vision and pattern recognition, attracting researchers from academia and industry. The meeting assembled presenters, attendees, and exhibitors connected to institutions and corporations worldwide, showcasing advances in image understanding, machine learning applications, and visual recognition systems. The program combined plenary sessions, technical papers, tutorials, workshops, and competitions that reflected ongoing developments in deep learning and large-scale visual datasets.
The conference took place in a context shaped by rapid progress from groups affiliated with institutions such as University of Oxford, Massachusetts Institute of Technology, Stanford University, Carnegie Mellon University, University of California, Berkeley, University of Toronto, University of Illinois Urbana–Champaign, Princeton University, California Institute of Technology, University of Washington, ETH Zurich, University of Cambridge, Peking University, National University of Singapore, Tsinghua University, Imperial College London, Columbia University, Yale University, University of Michigan, and University of California, Los Angeles. Industry research labs including Google, Microsoft Research, Facebook, DeepMind, IBM Research, Adobe Systems, NVIDIA, Intel Corporation, Apple Inc., Amazon (company), Yahoo!, IBM Watson, Baidu Research, and Tencent participated. Funding and sponsorship involved organizations such as the National Science Foundation, Defense Advanced Research Projects Agency, and corporate supporters tied to hardware and software products.
The event was organized under the auspices of the IEEE Computer Society and hosted in Boston, Massachusetts with sessions held at major conference facilities and nearby hotels. Local arrangements involved collaborations with universities in the New England region, including outreach to researchers from Harvard University and regional industry partners. Committees included program chairs, area chairs, and reviewer panels drawn from established labs and departments at organizations such as MIT Computer Science and Artificial Intelligence Laboratory, Stanford Artificial Intelligence Laboratory, Berkeley Artificial Intelligence Research, Facebook AI Research, and Google Research.
Plenary and keynote presentations featured leaders with affiliations to institutions and companies such as Microsoft Research, Google, Facebook, Stanford University, MIT, Carnegie Mellon University, University of Oxford, DeepMind, University of Toronto, and University of California, Berkeley. Tutorials were delivered by experts connected to labs and programs at ETH Zurich, Imperial College London, Peking University, Tsinghua University, Adobe Systems Research, NVIDIA Research, Intel Labs, IBM Research, and Amazon Science, covering topics spanning convolutional neural networks, probabilistic models, structured prediction, and optimization techniques.
The accepted program included landmark papers from authors affiliated with University of Oxford, Stanford University, University of Toronto, University of California, Berkeley, Carnegie Mellon University, Microsoft Research, Google Research, Facebook AI Research, DeepMind, Adobe Research, NVIDIA Research, ETH Zurich, Peking University, Tsinghua University, University of Michigan, Columbia University, Princeton University, and Imperial College London. Topics reflected breakthroughs in convolutional architectures, object detection, semantic segmentation, face recognition, and large-scale visual descriptors, building on prior work from groups associated with ImageNet, Alex Krizhevsky, Geoffrey Hinton, Yann LeCun, Fei-Fei Li, Ross Girshick, Kaiming He, Jitendra Malik, Pietro Perona, and Richard Szeliski. Datasets and benchmarks cited in presentations had provenance tied to initiatives like ImageNet Large Scale Visual Recognition Challenge, PASCAL VOC, MS COCO, KITTI (dataset), Caltech 101, and Oxford Visual Geometry Group resources.
Best paper awards and honorable mentions recognized contributions from researchers at institutions such as Stanford University, University of Toronto, University of Oxford, Carnegie Mellon University, Microsoft Research, Google Research, and Facebook AI Research. Program committee selections highlighted work connected to leading contributors like Andrew Ng, Yann LeCun, Geoffrey Hinton, Jitendra Malik, Fei-Fei Li, Pietro Perona, Serge Belongie, Cordelia Schmid, and Antonio Torralba. Student paper awards and oral presentation accolades were given to teams affiliated with graduate programs at MIT, UC Berkeley, CMU, Oxford, Toronto, Princeton, and ETH Zurich.
A wide array of workshops and special sessions featured collaborations among organizations including Microsoft Research, Google Research, Facebook AI Research, NVIDIA Research, IBM Research, Adobe Research, Intel Labs, and academic centers such as MIT CSAIL, Stanford AI Lab, Berkeley AI Research, CMU Robotics Institute, Oxford Robotics Institute, and ETH Zurich. Topics included fine-grained recognition, 3D reconstruction, RGB-D sensing, visual tracking, medical imaging, computational photography, robotic vision, and multimodal learning, drawing participants from Harvard Medical School, Johns Hopkins University, Massachusetts General Hospital, Siemens Healthineers, and Philips labs.
The conference influenced subsequent research trajectories at universities and companies including Stanford University, University of Toronto, MIT, Google, Facebook, Microsoft Research, DeepMind, NVIDIA Research, and Intel Research. Techniques presented informed later work deployed in products and services by Google, Facebook, Apple Inc., Microsoft, Amazon (company), NVIDIA, and Intel Corporation. The proceedings became reference points for graduate curricula at institutions such as MIT, Stanford University, UC Berkeley, Carnegie Mellon University, Princeton University, University of Oxford, and ETH Zurich, and for benchmark updates by consortia maintaining ImageNet Large Scale Visual Recognition Challenge, MS COCO, and PASCAL VOC.
Category:Computer vision conferences