Open Access

In this paper, we describe a first publicly available fine-grained product recognition dataset based on leaflet images. Using advertisement leaflets, collected over several years from different European retailers, we provide a total of 41.6k manually annotated product images in 832 classes. Further, we investigate three different approaches for this fine-grained product classification task, Classification by Image, Classification by Text, as well as Classification by Image and Text. The last both approaches use the text extracted directly from the leaflet product images. We show, that the combination of image and text as input improves the classification of visual difficult to distinguish products. The final model leads to an accuracy of 96.4% with a Top-3 score of 99.2%. https://github.com/ladwigd/Leaflet-Product-Classification

This study applies semi-supervised learning to automate the differen-tiation of mold colonies, thereby reducing the time and cost associated with airquality assessments. EfficientNet V2 and Normalization-Free Net (NfNet) weretrained on a dataset of mold colony images, created in a semi-supervised way.NfNet demonstrated superior performance, particularly on non-padded images,with explainable AI techniques enhancing interpretability. The models exhibitedgeneralization capabilities to environmental samples, indicating the potential forautomating mold identification and streamlining air quality monitoring, therebyreducing manual effort and costs. Future work will focus on refining species han-dling and integrating the system into laboratory workflows.

We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests.

Recent work in neural networks for image classification has seen a strong tendency towards increasing the spatial context during encoding. Whether achieved through large convolution kernels or self-attention, models scale poorly with the increased spatial context, such that the improved model accuracy often comes at significant costs. In this paper, we propose a module for studying the effective filter size of convolutional neural networks (CNNs). To facilitate such a study, several challenges need to be addressed: (i) we need an effective means to train models with large filters (potentially as large as the input data) without increasing the number of learnable parameters, (ii) the employed convolution operation should be a plug-and-play module that can replace conventional convolutions in a CNN and allow for an efficient implementation in current frameworks, (iii) the study of filter sizes has to be decoupled from other aspects such as the network width or the number of learnable parameters, and (iv) the cost of the convolution operation itself has to remain manageable i.e. we can not naïvely increase the size of the convolution kernel. To address these challenges, we propose to learn the frequency representations of filter weights as neural implicit functions, such that the better scalability of the convolution in the frequency domain can be leveraged. Additionally, due to the implementation of the proposed neural implicit function, even large and expressive spatial filters can be parameterized by only a few learnable weights. Interestingly, our analysis shows that, although the proposed networks could learn very large convolution kernels, the learned filters are well localized and relatively small in practice when transformed from the frequency to the spatial domain. We anticipate that our analysis of individually optimized filter sizes will allow for more efficient, yet effective, models in the future.