2,265 research outputs found
Salient Object Detection via Integrity Learning
Albeit current salient object detection (SOD) works have achieved fantastic
progress, they are cast into the shade when it comes to the integrity of the
predicted salient regions. We define the concept of integrity at both the micro
and macro level. Specifically, at the micro level, the model should highlight
all parts that belong to a certain salient object, while at the macro level,
the model needs to discover all salient objects from the given image scene. To
facilitate integrity learning for salient object detection, we design a novel
Integrity Cognition Network (ICON), which explores three important components
to learn strong integrity features. 1) Unlike the existing models that focus
more on feature discriminability, we introduce a diverse feature aggregation
(DFA) component to aggregate features with various receptive fields (i.e.,,
kernel shape and context) and increase the feature diversity. Such diversity is
the foundation for mining the integral salient objects. 2) Based on the DFA
features, we introduce the integrity channel enhancement (ICE) component with
the goal of enhancing feature channels that highlight the integral salient
objects at the macro level, while suppressing the other distracting ones. 3)
After extracting the enhanced features, the part-whole verification (PWV)
method is employed to determine whether the part and whole object features have
strong agreement. Such part-whole agreements can further improve the
micro-level integrity for each salient object. To demonstrate the effectiveness
of ICON, comprehensive experiments are conducted on seven challenging
benchmarks, where promising results are achieved
- …