Contour based scene text detection methods have rapidly developed recently,
but still suffer from inaccurate frontend contour initialization, multi-stage
error accumulation, or deficient local information aggregation. To tackle these
limitations, we propose a novel arbitrary-shaped scene text detection framework
named CT-Net by progressive contour regression with contour transformers.
Specifically, we first employ a contour initialization module that generates
coarse text contours without any post-processing. Then, we adopt contour
refinement modules to adaptively refine text contours in an iterative manner,
which are beneficial for context information capturing and progressive global
contour deformation. Besides, we propose an adaptive training strategy to
enable the contour transformers to learn more potential deformation paths, and
introduce a re-score mechanism that can effectively suppress false positives.
Extensive experiments are conducted on four challenging datasets, which
demonstrate the accuracy and efficiency of our CT-Net over state-of-the-art
methods. Particularly, CT-Net achieves F-measure of 86.1 at 11.2 frames per
second (FPS) and F-measure of 87.8 at 10.1 FPS for CTW1500 and Total-Text
datasets, respectively.

通过渐进式轮廓回归及轮廓变换器，我们提出了一种名为 CT-Net 的新型任意形状场景文本检测框架，以解决前端轮廓初始化不准确、多阶段误差累积或局部信息聚合不足的限制，并通过大量实验验证了其在准确性和效率方面超越了现有方法。

CT-Net: 通过轮廓变换器进行任意形状文本检测

CT-Net: Arbitrary-Shaped Text Detection via Contour Transformer

The ubiquity of implicit feedback makes them the default choice to build
online recommender systems. While the large volume of implicit feedback
alleviates the data sparsity issue, the downside is that they are not as clean
in reflecting the actual satisfaction of users. For example, in E-commerce, a
large portion of clicks do not translate to purchases, and many purchases end
up with negative reviews. As such, it is of critical importance to account for
the inevitable noises in implicit feedback for recommender training. However,
little work on recommendation has taken the noisy nature of implicit feedback
into consideration.
In this work, we explore the central theme of denoising implicit feedback for
recommender training. We find serious negative impacts of noisy implicit
feedback,i.e., fitting the noisy data prevents the recommender from learning
the actual user preference. Our target is to identify and prune noisy
interactions, so as to improve the quality of recommender training. By
observing the process of normal recommender training, we find that noisy
feedback typically has large loss values in the early stages. Inspired by this
observation, we propose a new training strategy namedAdaptive Denoising
Training(ADT), which adaptively prunes noisy interactions during training.
Specifically, we devise two paradigms for adaptive loss formulation: Truncated
Loss that discards the large-loss samples with a dynamic threshold in each
iteration; and reweighted Loss that adaptively lowers the weight of large-loss
samples. We instantiate the two paradigms on the widely used binary
cross-entropy loss and test the proposed ADT strategies on three representative
recommenders. Extensive experiments on three benchmarks demonstrate that ADT
significantly improves the quality of recommendation over normal training.

研究探讨了去噪隐式反馈机制对于推荐系统训练的影响和重要性，提出了一种自适应去噪训练策略（ADT），通过在训练过程中适当地剪枝噪声交互以提高推荐系统的训练质量。实验结果表明，ADT 显著改善了推荐质量。