Fine-grained classification is challenging due to the difficulty of finding
discriminatory features. This problem is exacerbated when applied to
identifying species within the same taxonomical class. This is because species
are often sharing morphological characteristics that make them difficult to
differentiate. We consider the taxonomical class of Insecta. The identification
of insects is essential in biodiversity monitoring as they are one of the
inhabitants at the base of many ecosystems. Citizen science is doing brilliant
work of collecting images of insects in the wild giving the possibility to
experts to create improved distribution maps in all countries. We have billions
of images that need to be automatically classified and deep neural network
algorithms are one of the main techniques explored for fine-grained tasks. At
the SOTA, the field of deep learning algorithms is extremely fruitful, so how
to identify the algorithm to use? We focus on Odonata and Coleoptera orders,
and we propose an initial comparative study to analyse the two best-known layer
structures for computer vision: transformer and convolutional layers. We
compare the performance of T2TViT, a fully transformer-base, EfficientNet, a
fully convolutional-base, and ViTAE, a hybrid. We analyse the performance of
the three models in identical conditions evaluating the performance per
species, per morph together with sex, the inference time, and the overall
performance with unbalanced datasets of images from smartphones. Although we
observe high performances with all three families of models, our analysis shows
that the hybrid model outperforms the fully convolutional-base and fully
transformer-base models on accuracy performance and the fully transformer-base
model outperforms the others on inference speed and, these prove the
transformer to be robust to the shortage of samples and to be faster at
inference time.

在物种鉴定中，细粒度分类是一项具有挑战性的任务。本研究比较了利用转换器层和卷积层的两种深度学习算法在昆虫纲中的表现，发现混合模型在准确性上优于完全卷积模型和完全转换器模型，而完全转换器模型在推断速度上优于其他模型。

变形金刚和卷积模型在昆虫细粒度分类中的比较

Comparison between transformers and convolutional models for  fine-grained classification of insects

A key aspect of human intelligence is the ability to infer abstract rules
directly from high-dimensional sensory data, and to do so given only a limited
amount of training experience. Deep neural network algorithms have proven to be
a powerful tool for learning directly from high-dimensional data, but currently
lack this capacity for data-efficient induction of abstract rules, leading some
to argue that symbol-processing mechanisms will be necessary to account for
this capacity. In this work, we take a step toward bridging this gap by
introducing the Emergent Symbol Binding Network (ESBN), a recurrent network
augmented with an external memory that enables a form of variable-binding and
indirection. This binding mechanism allows symbol-like representations to
emerge through the learning process without the need to explicitly incorporate
symbol-processing machinery, enabling the ESBN to learn rules in a manner that
is abstracted away from the particular entities to which those rules apply.
Across a series of tasks, we show that this architecture displays nearly
perfect generalization of learned rules to novel entities given only a limited
number of training examples, and outperforms a number of other competitive
neural network architectures.

本研究介绍了 Emergent Symbol Binding Network (ESBN)，它是一种采用外部记忆扩展的循环神经网络，能够通过一种变量绑定和间接储存的机制从学习过程中出现符号式表示，使得其能够在抽象规则的学习中达到几乎完美的泛化，摆脱了对显式加工符号的依赖。