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First of all, thank you for your work.
The method is promising and your article is very interesting, so I tried to use it in two way:
- determining whether a detected object is a False Positive
- …
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您好，我想请教一下你们，关于Tail & OOD detection 的实验设计思路，我可能基础较差，没有弄懂这个逻辑。我的理解是验证不确定性的泛化能力
okoys updated
2 years ago
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Thank you very much for the code library you provided. Now I have two questions when reproducing the fsood detection.
- There are some problems with the training and testing parameters of your sem …
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**Description:**
I would like to propose the addition of a new loss function and detector to the pytorch-ood library: an Angular Loss function (e.g., ArcFace) and an Angle-Based Detector. These add…
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Dear Zhang,
I am currently reviewing the source code related to your work on Out-of-Distribution (OOD) adversarial training and have encountered some questions regarding your method for evading OOD…
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## タイトル: 2024年 OOD-CV UNICORN チャレンジ オブジェクト検出支援LLM 計数能力向上のためのソリューション
## リンク: https://arxiv.org/abs/2410.16287
## 概要:
本レポートでは、ECCV OOD-CV UNICORN Challenge 2024 において我々が探求し提案した手法の詳細を説明します。このコンテストは、大規…
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In the paper you mention you validate the hyperparameters for the input processing (the FGSM magnitude) and the feature ensemble using adversarial samples (right part of Table 2 in the paper). I think…
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Hello, this work has provided me with a lot of inspiration！
But I have some uncertainties regarding the evaluation criteria for out-of-distribution generalization.
Based on my understanding of out-o…
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Hello, I trained cifar10-lt and cifar100-lt with the default parameters in the code, but the results I obtained are quite different from those in the paper.
Could you please tell me how can I achiev…