Image classification has been one of the most popular tasks in Deep Learning,
seeing an abundance of impressive implementations each year. However, there is
a lot of criticism tied to promoting complex architectures that continuously
push performance metrics higher and higher. Robustness tests can uncover
several vulnerabilities and biases which go unnoticed during the typical model
evaluation stage. So far, model robustness under distribution shifts has mainly
been examined within carefully curated datasets. Nevertheless, such approaches
do not test the real response of classifiers in the wild, e.g. when uncurated
web-crawled image data of corresponding classes are provided. In our work, we
perform fine-grained classification on closely related categories, which are
identified with the help of hierarchical knowledge. Extensive experimentation
on a variety of convolutional and transformer-based architectures reveals model
robustness in this novel setting. Finally, hierarchical knowledge is again
employed to evaluate and explain misclassifications, providing an
information-rich evaluation scheme adaptable to any classifier.

本文从层次知识入手，进行细粒度分类实验，验证模型在此类型下的稳健性，并通过其他方法解释和评估模型的错误分类行为。

野外细粒度图像分类

Fine-Grained ImageNet Classification in the Wild

Evaluation metrics for image captioning face two challenges. Firstly,
commonly used metrics such as CIDEr, METEOR, ROUGE and BLEU often do not
correlate well with human judgments. Secondly, each metric has well known blind
spots to pathological caption constructions, and rule-based metrics lack
provisions to repair such blind spots once identified. For example, the newly
proposed SPICE correlates well with human judgments, but fails to capture the
syntactic structure of a sentence. To address these two challenges, we propose
a novel learning based discriminative evaluation metric that is directly
trained to distinguish between human and machine-generated captions. In
addition, we further propose a data augmentation scheme to explicitly
incorporate pathological transformations as negative examples during training.
The proposed metric is evaluated with three kinds of robustness tests and its
correlation with human judgments. Extensive experiments show that the proposed
data augmentation scheme not only makes our metric more robust toward several
pathological transformations, but also improves its correlation with human
judgments. Our metric outperforms other metrics on both caption level human
correlation in Flickr 8k and system level human correlation in COCO. The
proposed approach could be served as a learning based evaluation metric that is
complementary to existing rule-based metrics.

提出了一种基于学习的区分性评价指标，通过数据扩增方案显著提高了评价指标对病态构造的鲁棒性，并在 FLickr 8k 和 COCO 数据集上对其他指标表现良好。