Mathematical analysis

Generalization

Context (archaeology)

Convolutional neural network

Segmentation

Pixel

Computer science

Noise (video)

Image (mathematics)

Pattern recognition (psychology)

Context model

Mathematics

Biology

Convolution (computer science)

Pooling

Paleontology

Object (grammar)

Computer vision

Artificial neural network

Artificial intelligence

Object detection

Embedding

IEEE Transactions on Image Processing

Existing saliency models typically incorporate contexts holistically. However, for each pixel, usually only part of its context region contributes to saliency prediction, while other parts are likely either noise or distractions. In this paper, we propose a novel pixel-wise contextual attention network (PiCANet) to selectively attend to informative context locations at each pixel. The proposed PiCANet generates an attention map over the contextual region of each pixel and construct attentive contextual features via selectively incorporating the features of useful context locations. We present three formulations of the PiCANet via embedding the pixel-wise contextual attention mechanism into the pooling and convolution operations with attending to global or local contexts. All the three models are fully differentiable and can be integrated with convolutional neural networks with joint training. In this work, we introduce the proposed PiCANets into a U-Net model for salient object detection. The generated global...

PiCANet: Pixel-Wise Contextual Attention Learning for Accurate Saliency Detection