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Showing 1 to 9 of 9 for “"Fine-grained Classification"”.
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Fine-grained painting classification
… of progress has been made in the domain of image classification in the deep learning era, however, not so much for paintings. Even though paintings are images they are very different from photographs and classification of paintings requires in-depth domain knowledge compared to classifying an …
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Visual Representation Learning from Synthetic Data
… models trained on real images in tasks including fine-grained classification and semantic segmentation. These works establish a robust foundation for advancing generative models in representation learning and solving key computer vision tasks, and mark an advance in utilizing synthetic data for …
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Image Classification With Unstructured Collections
… about a scene. Previous work in multi-view image classification typically focuses on classifying structured collection data. In this paradigm, the key object, feature, or perspective of each image is predetermined and uniform across all collections. Consequently, classification methods for …
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Advanced Computer Vision for Smart Retail: From Anomaly Detection to Fine-grained Product Classification
… particularly in automated anomaly detection, fine-grained product classification, and interactive product localization. Existing systems often struggle with the complexity and variability inherent in retail scenarios, including dynamic customer behaviors, subtle product differences, …
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Exploring Loss Functions in Machine Learning
… stands as a prevalent choice in neural network classification tasks. It treats all misclassifications uniformly. However, multi-class classification problems often have many semantically similar classes. We should expect that these semantically similar classes will have similar parameter …
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Fine-grained artworks classification
… apply deep convolutional neural networks to ne-grained artwork classification on the large-scale painting collection, WikiArt. We propose a new architecture that aggregates features from different convolutional layers to exploit earlier layer features. The new architecture is evaluated on the …
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Multimedia Traffic Management over Software-Defined Networking
… through the integration of software defined networking allows for the effective control of traffic and remote management of network devices. Nevertheless, the growing popularity and diversity of video-based multimedia applications, coupled with the expanding user base, have created …
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Deep Zero- and Few-shot Learning in Computer Vision
… learning can be decoupled into two disjoint classification tasks. Few-shot learning is a variant of knowledge transfer which is formulated as a meta-learning process. Firstly we investigate so-called second-order pooling and Power Normalizations in fine-grained classification tasks to study a …
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Deep Zero- and Few-shot Learning in Computer Vision
… learning can be decoupled into two disjoint classification tasks. Few-shot learning is a variant of knowledge transfer which is formulated as a meta-learning process. Firstly we investigate so-called second-order pooling and Power Normalizations in fine-grained classification tasks to study a …