CoADNet : Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

40 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationNeurIPS Proceedings
Subtitle of host publicationAdvances in Neural Information Processing Systems 33 (NeurIPS 2020)
EditorsH. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, H. Lin
Number of pages12
Volume33
Publication statusPublished - Dec 2020

Conference

Title34th Conference on Neural Information Processing Systems (NeurIPS 2020)
LocationVirtual
PlaceCanada
CityVancouver
Period6 - 12 December 2020

Abstract

Co-Salient Object Detection (CoSOD) aims at discovering salient objects that repeatedly appear in a given query group containing two or more relevant images. One challenging issue is how to effectively capture co-saliency cues by modeling and exploiting inter-image relationships. In this paper, we present an end-to-end collaborative aggregation-and-distribution network (CoADNet) to capture both salient and repetitive visual patterns from multiple images. First, we integrate saliency priors into the backbone features to suppress the redundant background information through an online intra-saliency guidance structure. After that, we design a two-stage aggregate-and-distribute architecture to explore group-wise semantic interactions and produce the co-saliency features. In the first stage, we propose a group-attentional semantic aggregation module that models inter-image relationships to generate the group-wise semantic representations. In the second stage, we propose a gated group distribution module that adaptively distributes the learned group semantics to different individuals in a dynamic gating mechanism. Finally, we develop a group consistency preserving decoder tailored for the CoSOD task, which maintains group constraints during feature decoding to predict more consistent full-resolution co-saliency maps. The proposed CoADNet is evaluated on four prevailing CoSOD benchmark datasets, which demonstrates the remarkable performance improvement over ten state-of-the-art competitors.

Citation Format(s)

CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection. / Zhang, Qijian; Cong, Runmin; Hou, Junhui et al.
NeurIPS Proceedings: Advances in Neural Information Processing Systems 33 (NeurIPS 2020). ed. / H. Larochelle; M. Ranzato; R. Hadsell; M.F. Balcan; H. Lin. Vol. 33 2020.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review