This paper presents a new mechanism which is more effective for wearable devices to
classify patient-specific electrocardiogram (ECG) heartbeats. In our method, a Generic …

Pooling layers help reduce redundancy and the number of parameters in deep neural
networks without the need of performing additional learning processes. Although these …

Multi-label image classification problem is one of the most important and fundamental
problems in computer vision. In an image with multiple labels, the objects usually locate at …

Accurate class and attribute recognition is the critical technique to convert the unstructured
product image data into structured knowledge base, which provides strong support for …

Deep convolutional neural networks have achieved great success on image classification. A
series of feature extractors learned from CNN have been used in many computer vision …

In many real-world problems, data samples are simultaneously associated with multiple
labels, instead of a single label. Multi-label classification deals with such problems, and has …

Image retrieval usually faces scale-variance issues as the amount of image data is rapidly
increasing, which calls for more accurate retrieval technology. Besides, existing methods …

Deep convolutional neural networks have achieved great success on many visual tasks (eg,
image classification). Non-linear activation plays a very important role in deep convolutional …

Different from the western paintings, Chinese ink-wash paintings (IWPs) have own
distinctive art styles. Furthermore, Chinese IWPs can be divided into two classes, Gongbi …

Multi-label learning (MLL) trains a classification model from multiple labelled datasets,
where each training instance is annotated with a set of class labels simultaneously …