{"id":2,"date":"2024-04-29T19:13:31","date_gmt":"2024-04-29T19:13:31","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2024team1\/?page_id=2"},"modified":"2024-12-05T01:05:44","modified_gmt":"2024-12-05T01:05:44","slug":"resource","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2024team1\/resource\/","title":{"rendered":"Resource"},"content":{"rendered":"\n

Github Page<\/a><\/p>\n\n\n\n

Poster<\/a><\/p>\n\n\n\n

Fall\u201924<\/h2>\n\n\n\n

Presentation Slide<\/a><\/p>\n\n\n\n

Spring\u201924<\/h2>\n\n\n\n

Presentation Slide<\/a><\/p>\n\n\n\n

<\/p>\n\n\n\n

Reference<\/h2>\n\n\n\n

[1] Zhou, Xingyi, et al. “Detecting twenty-thousand classes using image-level supervision.” European Conference on Computer Vision<\/em>. Cham: Springer Nature Switzerland, 2022.<\/p>\n\n\n\n

[2] Kirillov, Alexander, et al. “Segment anything.” Proceedings of the IEEE\/CVF International Conference on Computer Vision<\/em>. 2023.<\/p>\n\n\n\n

[3] Shou, Zheng, Dongang Wang, and Shih-Fu Chang. “Temporal action localization in untrimmed videos via multi-stage cnns.” Proceedings of the IEEE conference on computer vision and pattern recognition<\/em>. 2016.<\/p>\n\n\n\n

[4] Chao, Yu-Wei, et al. “Rethinking the faster r-cnn architecture for temporal action localization.” Proceedings of the IEEE conference on computer vision and pattern recognition<\/em>. 2018.<\/p>\n\n\n\n

[5] Kluger, Florian, et al. “Deep learning for vanishing point detection using an inverse gnomonic projection.” Pattern Recognition: 39th German Conference, GCPR 2017, Basel, Switzerland, September 12\u201315, 2017, Proceedings 39<\/em>. Springer International Publishing, 2017.<\/p>\n\n\n\n

[6] Lin, Yancong, et al. “Deep vanishing point detection: Geometric priors make dataset variations vanish.” Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition<\/em>. 2022.<\/p>\n","protected":false},"excerpt":{"rendered":"

Github Page Poster Fall\u201924 Presentation Slide Spring\u201924 Presentation Slide Reference [1] Zhou, Xingyi, et al. “Detecting twenty-thousand classes using image-level supervision.” European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022. [2] Kirillov, Alexander, et al. “Segment anything.” Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2023. [3] Shou, Zheng, Dongang Wang, and Shih-Fu Chang. “Temporal … <\/p>\n

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