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논문에서 제시하고 있는 method를 명확하게 두 스텝으로 설명해주셔서 이해가 수월했습니다. STL10 dataset의 경우 supervised로 학습한 것보다 본 논문에서 제시한 unsupervised 방식으로 학습한 성능의 더 좋은 결과를 보인 것이 흥미로웠습니다.
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굉장히 중요한 논문인 Nerf에서 Input과 Output이 어떤것인지 명시해 주셔서 감사합니다. 전체적으로 Method나 Experiment 부분에 내용이 보충된다면 더 좋은 리뷰가 될 것 같습니다 감사합니다.
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Thank you for publishing this work.
You mention in the paper that more details are provided in the Supplementary. Is it available?
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References to related work are missing
Some spelling mistakes ie. technique (check related work and section 5)
The dataset section may be included in Method
A typo/syntactic error in the first line…
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We would like to introduce new paper regarding OOD.
Tailoring Self-Supervision for Supervised Learning (ECCV 2022)
Arxiv version is available
[https://arxiv.org/abs/2207.10023](https://jebcco…
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## Development Plan 2023
We are excited to announce the release of MMPose 1.0.0 as a part of the OpenMMLab 2.0 project!
MMPose v1.0.0 is a major update, including many API and config file changes,…
Tau-J updated
2 weeks ago
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I want to know whether this paper is accepted to ECCV 2022
thank you.
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Hi Yuejiang,
Thanks for maintaining this awesome list!
I would like to request to add our ECCV 2022 paper: "Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recog…
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2022 ECCV: DFNet: Enhance Absolute Pose Regression with Direct Feature Matching
https://github.com/ActiveVisionLab/DFNet
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I had the pleasure of reading your RDIoU article in ECCV 2022 and have a question for you, are your results on the kitti val set in table1 trained on the train+val dataset together?