English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Contexts play an important role in the saliency detection task. However,given a context region, not all contextual information is helpful for the finaltask. In this paper, we propose a novel pixel-wise contextual attentionnetwork, i.e., the PiCANet, to learn to selectively attend to informativecontext locations for each pixel. Specifically, for each pixel, it can generatean attention map in which each attention weight corresponds to the contextualrelevance at each context location. An attended contextual feature can then beconstructed by selectively aggregating the contextual information. We formulatethe proposed PiCANet in both global and local forms to attend to global andlocal contexts, respectively. Both models are fully differentiable and can beembedded into CNNs for joint training. We also incorporate the proposed modelswith the U-Net architecture to detect salient objects. Extensive experimentsshow that the proposed PiCANets can consistently improve saliency detectionperformance. The global and local PiCANets facilitate learning global contrastand homogeneousness, respectively. As a result, our saliency model can detectsalient objects more accurately and uniformly, thus performing favorablyagainst the state-of-the-art methods.

PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection