Kavli Affiliate: Wei Gao
| First 5 Authors: Guibiao Liao, Wei Gao, , ,
| Summary:
Salient object detection (SOD) has achieved substantial progress in recent
years. In practical scenarios, compressed images (CI) serve as the primary
medium for data transmission and storage. However, scant attention has been
directed towards SOD for compressed images using convolutional neural networks
(CNNs). In this paper, we are dedicated to strictly benchmarking and analyzing
CNN-based salient object detection on compressed images. To comprehensively
study this issue, we meticulously establish various CI SOD datasets from
existing public SOD datasets. Subsequently, we investigate representative
CNN-based SOD methods, assessing their robustness on compressed images
(approximately 2.64 million images). Importantly, our evaluation results reveal
two key findings: 1) current state-of-the-art CNN-based SOD models, while
excelling on clean images, exhibit significant performance bottlenecks when
applied to compressed images. 2) The principal factors influencing the
robustness of CI SOD are rooted in the characteristics of compressed images and
the limitations in saliency feature learning. Based on these observations, we
propose a simple yet promising baseline framework that focuses on robust
feature representation learning to achieve robust CNN-based CI SOD. Extensive
experiments demonstrate the effectiveness of our approach, showcasing markedly
improved robustness across various levels of image degradation, while
maintaining competitive accuracy on clean data. We hope that our benchmarking
efforts, analytical insights, and proposed techniques will contribute to a more
comprehensive understanding of the robustness of CNN-based SOD algorithms,
inspiring future research in the community.
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