The Explainable Detection of Online Sexism task presents the problem of
explainable sexism detection through fine-grained categorisation of sexist
cases with three subtasks. Our team experimented with different ways to combat
class imbalance throughout the tasks using data augmentation and loss
alteration techniques. We tackled the challenge by utilising ensembles of
Transformer models trained on different datasets, which are tested to find the
balance between performance and interpretability. This solution ranked us in
the top 40\% of teams for each of the tracks.

该研究通过细粒度分类，使用数据增强和迭代技术来解决在线性别歧视检测的挑战，采用不同数据集训练的 Transformer 模型集成，解决了性能和可解释性之间的平衡问题。

在 SemEval-2023 任务 10 中解决性别歧视检测中的类别不平衡问题：基于集成学习的 AdamR 方法

AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in  Sexism Detection with Ensemble Learning

A video dataset that is designed to study fine-grained categorisation of
pedestrians is introduced. Pedestrians were recorded "in-the-wild" from a
moving vehicle. Annotations include bounding boxes, tracks, 14 keypoints with
occlusion information and the fine-grained categories of age (5 classes), sex
(2 classes), weight (3 classes) and clothing style (4 classes). There are a
total of 27,454 bounding box and pose labels across 4222 tracks. This dataset
is designed to train and test algorithms for fine-grained categorisation of
people, it is also useful for benchmarking tracking, detection and pose
estimation of pedestrians. State-of-the-art algorithms for fine-grained
classification and pose estimation were tested using the dataset and the
results are reported as a useful performance baseline.

该文介绍了一个视频数据集，包含了人群的详细分类信息和关节点标注，被用于训练和测试人群的精细分类、追踪、检测和姿态估计算法。

视频中行人的细粒度分类：基准及最新技术

Fine-Grained Classification of Pedestrians in Video: Benchmark and State  of the Art

Fine-grained categorisation has been a challenging problem due to small
inter-class variation, large intra-class variation and low number of training
images. We propose a learning system which first clusters visually similar
classes and then learns deep convolutional neural network features specific to
each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset
show that the proposed method outperforms recent fine-grained categorisation
methods under the most difficult setting: no bounding boxes are presented at
test time. It achieves a mean accuracy of 77.5%, compared to the previous best
performance of 73.2%. We also show that progressive transfer learning allows us
to first learn domain-generic features (for bird classification) which can then
be adapted to specific set of bird classes, yielding improvements in accuracy.

本文提出了一种基于深度卷积神经网络的学习系统，该系统对视觉相似的类别进行聚类，并学习针对每个子集的特定深度卷积神经网络特征，在测试时不需要边界框。在流行的 Caltech-UCSD 鸟类数据集上，实验证明该方法优于最近的细粒度分类方法，无需边界框，在最困难的情况下达到了 77.5% 的平均准确率，而先前的最佳表现为 73.2%。本文还表明，渐进式迁移学习使我们能够先学习通用领域的特征，然后将其调整到特定的鸟类集合中，从而提高准确性。