Pattern recognition (psychology)

Operating system

Image (mathematics)

Pixel

Enhanced Data Rates for GSM Evolution

Computer science

Management

Edge detection

Feature extraction

Machine learning

Philosophy

Economics

Object detection

Computer vision

Linguistics

Feature (linguistics)

Salient

Artificial intelligence

Task (project management)

Source code

Segmentation

Image processing

Image segmentation

IEEE Transactions on Image Processing

In this paper, we solve three low-level pixel-wise vision problems, including salient object segmentation, edge detection, and skeleton extraction, within a unified framework. We first show some similarities shared by these tasks and then demonstrate how they can be leveraged for developing a unified framework that can be trained end-to-end. In particular, we introduce a selective integration module that allows each task to dynamically choose features at different levels from the shared backbone based on its own characteristics. Furthermore, we design a task-adaptive attention module, aiming at intelligently allocating information for different tasks according to the image content priors. To evaluate the performance of our proposed network on these tasks, we conduct exhaustive experiments on multiple representative datasets. We will show that though these tasks are naturally quite different, our network can work well on all of them and even perform better than current single-purpose state-of-the-art methods. ...

Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge, and Skeleton