2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDIMA-ELI-1.2
Paper Title CO-SALIENCY DETECTION VIA UNIFIED HIERARCHICAL GRAPH NEURAL NETWORK WITH GEOMETRIC ATTENTION
Authors Jiaqing Qiao, Shaowei Sun, Mingzhu Xu, Yongqiang Li, Bing Liu, Harbin Institute of Technology, China
SessionIMA-ELI-1: Imaging and Media Applications + Electronic Imaging
LocationArea F
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic Imaging and Media Applications: Image and video processing over networks
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Co-saliency detection aims to identify the common and salient objects from a group of relevant images. The main challenge for co-saliency detection is how to mine and exploit the saliency cues of both intra-image and inter-image. In this paper, we present a novel unified hierarchical neural network (UHGNN). We first construct the graph model by segmenting the images into super-pixels and extracting the intra-image hierarchical saliency cues. Then, the inter-image hierarchical saliency representation is mined to form the unified two-dimensional hierarchical feature setup. We further propose the geometric attention module to make the most of the intra-image and inter-image cues. Our UHGNN model competes or outperforms the state-of-the-art methods on two co-saliency detection benchmark datasets (MSRC, iCoSeg).