In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …

Medical image segmentation plays a critical role in accurate diagnosis and treatment
planning, enabling precise analysis across a wide range of clinical tasks. This review begins …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Humans are able to recognize structured relations in observation, allowing us to decompose
complex scenes into simpler parts and abstract the visual world in multiple levels. However …

Due to often working in the environment of variable speeds and loads, it is an enormous
challenge to achieve high-accuracy fault diagnosis (FD) of rolling bearings (RB) via existing …

Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying
machine learning models in the real world. Existing solutions are mainly driven by small …

Recent advances in semi-supervised semantic segmentation have been heavily reliant on
pseudo labeling to compensate for limited labeled data, disregarding the valuable relational …

Existing image classification datasets used in computer vision tend to have a uniform
distribution of images across object categories. In contrast, the natural world is heavily …

Although an object may appear in numerous contexts, we often describe it in a limited
number of ways. Language allows us to abstract away visual variation to represent and …

Hyperspectral image classification (HSIC) methods based on convolutional neural network
(CNN) continue to progress in recent years. However, high complexity, information …