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Guidelines for Being a CVPR Area Chair Based on ICCV 2019 & CVPR 2019

Serving as an Area Chair for CVPR involves making transparent decisions for the community, ensuring consistency, and handling critical paper evaluations. The decision process includes assigning papers to reviewers, managing reviews, discussions, rebuttals, and final decisions. Area Chairs play a cru

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Guidance on Writing Effective Reviews for CVPR - Insights from ICCV 2019 & CVPR 2019 Program Chairs

Providing quality reviews is crucial for the success of CVPR. Helpful reviews assist area chairs in making informed decisions, aid authors in receiving constructive feedback, and benefit the community by ensuring valuable papers are accepted. Conversely, poorly written reviews can have negative cons

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Mastering the Art of Writing Good Reviews for CVPR

Embrace the significance of your role as a reviewer, understand the paper decision process, learn how to structure effective reviews with examples, and gain valuable tips to enhance your reviewing skills. Your reviews impact career advancement, authors' experiences, and community knowledge dissemina

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Graphical Models and Belief Propagation in Computer Vision

Identical local evidence can lead to different interpretations in computer vision, highlighting the importance of propagating information effectively. Probabilistic graphical models serve as a powerful tool for this purpose, enabling the propagation of local information within an image. This lecture

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Orientational Pyramid Matching for Indoor Scene Recognition at CVPR 2014

This presentation at CVPR 2014 introduces Orientational Pyramid Matching for recognizing indoor scenes. The speaker, Lingxi Xie, along with other authors, presents the Bag-of-Feature Model and its experimental results. The focus is on scene recognition and the importance of image understanding in va

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Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images

This research project presented at CVPR 2019 by Wuyang Chen, Ziyu Jiang, Zhangyang Wang, Kexin Cui, and Xiaoning Qian focuses on memory-efficient segmentation of ultra-high resolution images using Collaborative Global-Local Networks. The study explores the benefits of employing two branches for deep

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