Neural Networks for Fashion Image Classification and Visual Search
원문 초록
We discuss two potentially challenging problems faced by the ecommerce industry. One relates to the problem faced by sellers while uploading pictures of products on the platform for sale and the consequent manual tagging involved. It gives rise to misclassifications leading to its absence from search results. The other problem concerns with the potential bottleneck in placing orders when a customer may not know the right keywords but has a visual impression of an image. An image based search algorithm can unleash the true potential of ecommerce by enabling customers to click a picture of an object and search for similar products without the need for typing. In this paper, we explore machine learning algorithms which can help us solve both these problems.
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Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion
Deep Prototypical Networks with Hybrid Residual Attention for Hyperspectral Image Classification
Fast Gradient Descent Algorithm for Image Classification with Neural Networks
토론
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