CVPR是IEEE Conference on Computer Vision and Pattern Recognition的缩写,即IEEE国际计算机视觉与模式识别会议。该会议是由IEEE举办的计算机视觉和模式识别领域的顶级会议。

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CVPR 2019 论文打包下载(1294篇)

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【导读】CVPR 是计算机视觉领域三大顶会之一,每年投稿都上万,竞争异常激烈。今年Deadline是11月16号,相信很多老师同学正在为此赶稿。最近CVPR2021官网发布了针对Reviewer的审稿指南,有些关键点需要好好把握,值得关注!

https://cvpr2022.thecvf.com/reviewer-guidelines

审稿要点

被接受的论文应该在技术上是可靠的,并且对这个领域做出贡献。寻找论文上好的内容。特别是,看看这篇论文有什么新的知识贡献。我们建议您接受新颖、大胆的概念,即使它们没有在许多数据集上进行过测试。例如,所提出的方法在现有基准数据集上没有超过最先进的性能这一事实本身不能作为拒绝的理由。相反,重要的是要权衡工作的新颖性和潜在影响与报告的表现。可以很容易纠正的小缺陷不应该成为拒绝论文的理由。

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This work aims to reproduce results from the CVPR 2020 paper by Gidaris et al. Self-supervised learning (SSL) is used to learn feature representations of an image using an unlabeled dataset. This work proposes to use bag-of-words (BoW) deep feature descriptors as a self-supervised learning target to learn robust, deep representations. BowNet is trained to reconstruct the histogram of visual words (ie. the deep BoW descriptor) of a reference image when presented a perturbed version of the image as input. Thus, this method aims to learn perturbation-invariant and context-aware image features that can be useful for few-shot tasks or supervised downstream tasks. In the paper, the author describes BowNet as a network consisting of a convolutional feature extractor $\Phi(\cdot)$ and a Dense-softmax layer $\Omega(\cdot)$ trained to predict BoW features from images. After BoW training, the features of $\Phi$ are used in downstream tasks. For this challenge we were trying to build and train a network that could reproduce the CIFAR-100 accuracy improvements reported in the original paper. However, we were unsuccessful in reproducing an accuracy improvement comparable to what the authors mentioned.

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