# Multi-label Object Attribute Classification using a Convolutional Neural Network Source: [Multi-label Object Attribute Classification using a Convolutional Neural Network]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:18:52.954Z Authors: Soubarna Banik, Mikko Lauri, Simone Frintrop Published: 2018-11-10T20:27:59.000Z ## Abstract Objects of different classes can be described using a limited number of attributes such as color, shape, pattern, and texture. Learning to detect object attributes instead of only detecting objects can be helpful in dealing with a priori unknown objects. With this inspiration, a deep convolutional neural network for low-level object attribute classification, called the Deep Attribute Network (DAN), is proposed. Since object features are implicitly learned by object recognition networks, one such existing network is modified and fine-tuned for developing DAN. The performance of DAN is evaluated on the ImageNet Attribute and a-Pascal datasets. Experiments show that in comparison with state-of-the-art methods, the proposed model achieves better results. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()