DeepAI AI Chat
Log In Sign Up

Sharp Eyes: A Salient Object Detector Working The Same Way as Human Visual Characteristics

by   Ge Zhu, et al.
Heilongjiang University

Current methods aggregate multi-level features or introduce edge and skeleton to get more refined saliency maps. However, little attention is paid to how to obtain the complete salient object in cluttered background, where the targets are usually similar in color and texture to the background. To handle this complex scene, we propose a sharp eyes network (SENet) that first seperates the object from scene, and then finely segments it, which is in line with human visual characteristics, i.e., to look first and then focus. Different from previous methods which directly integrate edge or skeleton to supplement the defects of objects, the proposed method aims to utilize the expanded objects to guide the network obtain complete prediction. Specifically, SENet mainly consists of target separation (TS) brach and object segmentation (OS) branch trained by minimizing a new hierarchical difference aware (HDA) loss. In the TS branch, we construct a fractal structure to produce saliency features with expanded boundary via the supervision of expanded ground truth, which can enlarge the detail difference between foreground and background. In the OS branch, we first aggregate multi-level features to adaptively select complementary components, and then feed the saliency features with expanded boundary into aggregated features to guide the network obtain complete prediction. Moreover, we propose the HDA loss to further improve the structural integrity and local details of the salient objects, which assigns weight to each pixel according to its distance from the boundary hierarchically. Hard pixels with similar appearance in border region will be given more attention hierarchically to emphasize their importance in completeness prediction. Comprehensive experimental results on five datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.


page 1

page 4

page 7

page 11

page 12


F3Net: Fusion, Feedback and Focus for Salient Object Detection

Most of existing salient object detection models have achieved great pro...

Exploring Reciprocal Attention for Salient Object Detection by Cooperative Learning

Typically, objects with the same semantics are not always prominent in i...

Chaotic Phase Synchronization and Desynchronization in an Oscillator Network for Object Selection

Object selection refers to the mechanism of extracting objects of intere...

Polarimetric Hierarchical Semantic Model and Scattering Mechanism Based PolSAR Image Classification

For polarimetric SAR (PolSAR) image classification, it is a challenge to...

Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss

By the aid of attention mechanisms to weight the image features adaptive...

Region Refinement Network for Salient Object Detection

Albeit intensively studied, false prediction and unclear boundaries are ...