Fine-grained image categorization is challenging due to the subtle
inter-class differences.We posit that exploiting the rich relationships between
channels can help capture such differences since different channels correspond
to different semantics. In this paper, we propose a channel interaction network
(CIN), which models the channel-wise interplay both within an image and across
images. For a single image, a self-channel interaction (SCI) module is proposed
to explore channel-wise correlation within the image. This allows the model to
learn the complementary features from the correlated channels, yielding
stronger fine-grained features. Furthermore, given an image pair, we introduce
a contrastive channel interaction (CCI) module to model the cross-sample
channel interaction with a metric learning framework, allowing the CIN to
distinguish the subtle visual differences between images. Our model can be
trained efficiently in an end-to-end fashion without the need of multi-stage
training and testing. Finally, comprehensive experiments are conducted on three
publicly available benchmarks, where the proposed method consistently
outperforms the state-of-theart approaches, such as DFL-CNN (Wang, Morariu, and
Davis 2018) and NTS (Yang et al. 2018).

本研究提出一种名为 CIN 的模型， 结合自通道交互 (SCI) 和对比通道交互 (CCI) 模块， 进行细粒度图像分类。实验结果表明，该模型在公开基准测试中比 DFL-CNN 和 NTS 方法表现更好。

通道交互网络用于细粒度图像分类

Channel Interaction Networks for Fine-Grained Image Categorization

Key for solving fine-grained image categorization is finding discriminate and
local regions that correspond to subtle visual traits. Great strides have been
made, with complex networks designed specifically to learn part-level
discriminate feature representations. In this paper, we show it is possible to
cultivate subtle details without the need for overly complicated network
designs or training mechanisms -- a single loss is all it takes. The main trick
lies with how we delve into individual feature channels early on, as opposed to
the convention of starting from a consolidated feature map. The proposed loss
function, termed as mutual-channel loss (MC-Loss), consists of two
channel-specific components: a discriminality component and a diversity
component. The discriminality component forces all feature channels belonging
to the same class to be discriminative, through a novel channel-wise attention
mechanism. The diversity component additionally constraints channels so that
they become mutually exclusive on spatial-wise. The end result is therefore a
set of feature channels that each reflects different locally discriminative
regions for a specific class. The MC-Loss can be trained end-to-end, without
the need for any bounding-box/part annotations, and yields highly
discriminative regions during inference. Experimental results show our MC-Loss
when implemented on top of common base networks can achieve state-of-the-art
performance on all four fine-grained categorization datasets (CUB-Birds,
FGVC-Aircraft, Flowers-102, and Stanford-Cars). Ablative studies further
demonstrate the superiority of MC-Loss when compared with other recently
proposed general-purpose losses for visual classification, on two different
base networks. Code available at
this https URL

本研究提出的互相通道（MC）损失函数具有两个组件：一个区别度组件和一个多样性组件，它们共同对特征通道进行调整，以形成针对特定类别的本地可区分区域。在四个特定数据集上的试验表明，MC 损失函数的有效性与其他近期提出的视觉分类损失相比具有明显优势。

基于互通通道损失的细粒度图像分类

The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image  Classification

In this paper, we categorize fine-grained images without using any object /
part annotation neither in the training nor in the testing stage, a step
towards making it suitable for deployments. Fine-grained image categorization
aims to classify objects with subtle distinctions. Most existing works heavily
rely on object / part detectors to build the correspondence between object
parts by using object or object part annotations inside training images. The
need for expensive object annotations prevents the wide usage of these methods.
Instead, we propose to select useful parts from multi-scale part proposals in
objects, and use them to compute a global image representation for
categorization. This is specially designed for the annotation-free fine-grained
categorization task, because useful parts have shown to play an important role
in existing annotation-dependent works but accurate part detectors can be
hardly acquired. With the proposed image representation, we can further detect
and visualize the key (most discriminative) parts in objects of different
classes. In the experiment, the proposed annotation-free method achieves better
accuracy than that of state-of-the-art annotation-free and most existing
annotation-dependent methods on two challenging datasets, which shows that it
is not always necessary to use accurate object / part annotations in
fine-grained image categorization.

该论文致力于通过使用多尺度部分提议来选择有用的部分，并使用它们计算全局图像表示以进行无注释的细粒度图像分类，同时探索了其关键部分的检测和可视化。实验表明该方法在两个具有挑战性的数据集上比同类方法更加准确。