You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We perform classification of ancient Roman Republican coins via recognizing their reverse motifs where various objects, faces, scenes, animals, and buildings are minted along with legends. Most of these coins are eroded due to their age and varying degrees of preservation, thereby affecting their informative attributes for visual recognition. Changes in the positions of principal symbols on the reverse motifs also cause huge variations among the coin types. Lastly, in-plane orientations, uneven illumination, and a moderate background clutter further make the task of classification non-trivial and challenging. To this end, we present a novel network model, CoinNet, that employs compact bilinear pooling, residual groups, and feature attention layers. Furthermore, we gathered the largest and most diverse image dataset of the Roman Republican coins that contains more than 18,000 images belonging to 228 different reverse motifs. On this dataset, our model achieves a classification accuracy of more than and outperforms the conventional bag-of-visual-words based approaches and more recent state-of-the-art deep learning methods. We also provide a detailed ablation study of our network and its generalization capability.

Cited Abstract(s):
[1]: Classification of ancient coins is a substantial part of numismatic research which needs a large amount of expert knowledge due to the high number of classes to be considered. In this paper we propose an automatic image-based classification method for ancient coins to support this time-consuming and difficult process. We demonstrate that previously proposed learning-based methods suffer from the practical conditions of this problem: a high number of classes, limited number of training samples per class and complex intra-class variations. As a solution we propose a similarity metric based on feature correspondence which is designed to be robust against the possible intra-class coin variations like degraded parts, non-rigid deformations and illumination-induced appearance changes. The similarity metric is used in an exemplar-based ancient coin classification scheme which shows to outperform previously proposed methods for ancient coin recognition. Experiments are conducted on a dataset of 60 Roman Republican coin classes where the presented method achieves classification rates ranging from 72.7 for the case of one training sample per class up to 97.2 when nine training samples per class are used.
The success of supervised learning mainly comes from the availability of abundant data for the offline training phase. However, in the case of ancient coins, the prevalent problem is the absence of training data due to their rareness and diversity, thereby leading to low recognition rates [1] . In comparison, the feature matching based techniques neither involve an offline training process nor do they require a large number of exemplary images. Even with three or four samples per class, the feature matching substantially outperforms the supervised learning methods [1] . Nevertheless, the online feature matching, as well as the search process it involves, make the feature matching methods computationally intensive. Besides, the complexity increases proportionally with the number of classes in the dataset [1] .