In this paper we study the problem of estimating the migration direction of
cells based on a single image. To the best of our knowledge, there is only one
related work that uses a classification CNN for four classes (quadrants). This
approach does not allow detailed directional resolution. We solve the single
image estimation problem using deep circular regression with special attention
to cycle-sensitive methods. On two databases we achieve an average accuracy of
$\sim$17 degrees, which is a significant improvement over the previous work.

研究了基于单幅图像估计细胞迁移方向的问题，通过使用深度循环回归方法，解决了单幅图像估计问题，达到了平均精度约 17 度的结果。

深度循环回归单图像细胞迁移方向估计

Single Image Estimation of Cell Migration Direction by Deep Circular  Regression

Chain-of-Thought (CoT) prompting methods have enabled large language models
(LLMs) to generate reasoning paths and solve math word problems (MWPs).
However, they are sensitive to mistakes in the paths, as any mistake can result
in an incorrect answer. We propose a novel method named Progressive
Rectification Prompting (PRP) to improve average accuracy on eight MWP datasets
from 77.3 to 90.5. Given an initial answer from CoT, PRP iterates a
verify-then-rectify process to progressively identify incorrect answers and
rectify the reasoning paths. With the most likely correct answer, the LLM
predicts a masked numerical value in the question; if the prediction does not
match the masked value, the answer is likely incorrect. Then the LLM is
prompted to re-generate the reasoning path hinted with a set of incorrect
answers to prevent itself from repeating previous mistakes. PRP achieves the
best performance compared against the CoT methods. Our implementation is made
publicly available at this https URL

通过 Progressive Rectification Prompting 方法，对 Chain-of-Thought 提示方法进行改进以提高数学问题的平均准确率。

数学满分：渐进修正提示

Get an A in Math: Progressive Rectification Prompting

Data augmentation (DA) encodes invariance and provides implicit
regularization critical to a model's performance in image classification tasks.
However, while DA improves average accuracy, recent studies have shown that its
impact can be highly class dependent: achieving optimal average accuracy comes
at the cost of significantly hurting individual class accuracy by as much as
20% on ImageNet. There has been little progress in resolving class-level
accuracy drops due to a limited understanding of these effects. In this work,
we present a framework for understanding how DA interacts with class-level
learning dynamics. Using higher-quality multi-label annotations on ImageNet, we
systematically categorize the affected classes and find that the majority are
inherently ambiguous, co-occur, or involve fine-grained distinctions, while DA
controls the model's bias towards one of the closely related classes. While
many of the previously reported performance drops are explained by multi-label
annotations, our analysis of class confusions reveals other sources of accuracy
degradation. We show that simple class-conditional augmentation strategies
informed by our framework improve performance on the negatively affected
classes.

数据增强（DA）对模型在图像分类任务中的性能具有关键影响，然而，最近的研究表明，DA 的影响可以高度依赖于类别：达到最佳的平均准确率会显著损害个别类别准确率，如在 ImageNet 上可降低 20%。本研究通过使用更高质量的多标签注释系统地对受影响的类别进行分类，发现大部分类别具有固有的模糊性、共现性或细粒度差异，而 DA 会控制模型对紧密相关类别的偏好。通过基于该框架的简单类别条件的增强策略，我们改善了负面影响类别的性能。