# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 5 Product Detection and Recognition
### Product Recognition Based on Deep Learning  
Object recognition enables classifying products already detected or images with a single product. In [85], an updated version of [86] was presented with the contextual and visual features computed by CNN models, such as ResNet50, Alexnet or VGG. The deep class embedding, strengthened by the product's visual appearance and its relative position on the shelf, is reparametrised by a CRF-based method. The proposed model was designed for the fine-grained classification of numerous classes. A framework to recognise fine-grained product images of different source and target domains was proposed by [87] with the CNN model containing two modules: an adversarial module to handle the cross-domain scenario and a self-attention module that captures the most discriminative image regions to increase accuracy. The results were compared with domain adaption methods, excluding known grocery recognition models. [PERSON] et al. [88] suggested a fine-grained classifier that combined object-level and part-level information of the product images. The novel reconstruction-classification network (RC-Net) extracted a more general product feature, being robust to a range of store lighting levels. The discriminative features were searched in an unsupervised manner and encoded using convolutional Long Short-Term Memory (LSTM). The R-CNN classified the object based on both feature-level.  
In [46], the authors presented preliminary benchmark results for the Grocery Store dataset (GSD). The experiments showed that using the joint DenseNet-169 features of both iconic and natural images as inputs to the variational autoencoder canonical correlation analysis (VAE-CCA) [89] was better than only using the extracted features of natural images. For the classification, the SVM was trained on the latent representations. [PERSON] et al. [90] used the previous model as the baseline method. They suggest supervised and unsupervised frameworks based on the three hierarchy class labels of GSD. In both, a multi-task learning DenseNet-169 network was used to extract features for the corresponding class level. The supervised model was trained over ImageNet and fine-tuned on the GSD dataset with data augmentation. The best result (83%) was obtained using a cubic SVM on the features extracted from the last average pooling layer before the network split. For the unsupervised method, they linked the same features of the supervised model to the Affinity Propagation algorithm [91]. Both frameworks have difficulty adapting to other datasets. To overcome the scarcity of image data, [PERSON] et al. [92] studied the implementation of Siamese neural networks in grocery classification, assessing four one-shot learning architectures. The datasets contain few images per category or even one iconic image per class. The integration of ResNeXt-101 with LOMO descriptor into the Siamese network achieved good results, obtaining an F1-score of 89.1%. Low computational cost can be achieved with lightweight descriptors.  
In [93], [PERSON] et al. proposed a self-attention mechanism to eliminate the noise when performing the destruction and construction of image knowledge to grocery classification in the Retail Product Checkout dataset [5]. Other fine-grained classification methods fall short compared to self-attention-based destruction and construction learning (SADCL). This application achieved an accuracy above 80% with low computational costs. As shown in Table 5, each approach uses a different dataset and metric to analyse the results, thus making a proper comparison difficult.