Deep convolutional neural networks (CNNs) have shown a strong ability in
mining discriminative object pose and parts information for image recognition.
For fine-grained recognition, context-aware rich feature representation of
object/scene plays a key role since it exhibits a significant variance in the
same subcategory and subtle variance among different subcategories. Finding the
subtle variance that fully characterizes the object/scene is not
straightforward. To address this, we propose a novel context-aware attentional
pooling (CAP) that effectively captures subtle changes via sub-pixel gradients,
and learns to attend informative integral regions and their importance in
discriminating different subcategories without requiring the bounding-box
and/or distinguishable part annotations. We also introduce a novel feature
encoding by considering the intrinsic consistency between the informativeness
of the integral regions and their spatial structures to capture the semantic
correlation among them. Our approach is simple yet extremely effective and can
be easily applied on top of a standard classification backbone network. We
evaluate our approach using six state-of-the-art (SotA) backbone networks and
eight benchmark datasets. Our method significantly outperforms the SotA
approaches on six datasets and is very competitive with the remaining two.

提出了一种基于上下文感知的注意力池化（CAP）方法和特征编码技术，可以有效地捕获子像素梯度，无需边界框和 / 或可区分的部分注释，从而学习关键部位的特征表示。经过在六个最先进的骨干网络和八个基准数据集上的评估，该方法在六个数据集上表现显著优于最先进的方法，并且在其余两个数据集上非常有竞争力。

面向细粒度视觉分类的上下文感知注意池化 (CAP)

Context-aware Attentional Pooling (CAP) for Fine-grained Visual  Classification

The recently advanced unsupervised learning approaches use the siamese-like
framework to compare two "views" from the same image for learning
representations. Making the two views distinctive is a core to guarantee that
unsupervised methods can learn meaningful information. However, such frameworks
are sometimes fragile on overfitting if the augmentations used for generating
two views are not strong enough, causing the over-confident issue on the
training data. This drawback hinders the model from learning subtle variance
and fine-grained information. To address this, in this work we aim to involve
the distance concept on label space in the unsupervised learning and let the
model be aware of the soft degree of similarity between positive or negative
pairs through mixing the input data space, to further work collaboratively for
the input and loss spaces. Despite its conceptual simplicity, we show
empirically that with the solution -- Unsupervised image mixtures (Un-Mix), we
can learn subtler, more robust and generalized representations from the
transformed input and corresponding new label space. Extensive experiments are
conducted on CIFAR-10, CIFAR-100, STL-10, Tiny ImageNet and standard ImageNet
with popular unsupervised methods SimCLR, BYOL, MoCo V1&V2, SwAV, etc. Our
proposed image mixture and label assignment strategy can obtain consistent
improvement by 1~3% following exactly the same hyperparameters and training
procedures of the base methods. Code is publicly available at
this https URL

该论文提出了一种基于标签空间的无监督学习方法 (Un-Mix)，通过混合输入数据进行联合工作，从而学习到更加精细、鲁棒且广义的表示。实验证明，在 CIFAR-10、CIFAR-100、STL-10、Tiny ImageNet 和 ImageNet 等数据集上，该方法均可以在保持基本方法的超参数和训练程序不变的条件下，显著提高模型的泛化性能。