RESUMO
Human attention is an interactive activity between our visual system and our brain, using both low-level visual stimulus and high-level semantic information. Previous image salient object detection (SOD) studies conduct their saliency predictions via a multitask methodology in which pixelwise saliency regression and segmentation-like saliency refinement are conducted simultaneously. However, this multitask methodology has one critical limitation: the semantic information embedded in feature backbones might be degenerated during the training process. Our visual attention is determined mainly by semantic information, which is evidenced by our tendency to pay more attention to semantically salient regions even if these regions are not the most perceptually salient at first glance. This fact clearly contradicts the widely used multitask methodology mentioned above. To address this issue, this paper divides the SOD problem into two sequential steps. First, we devise a lightweight, weakly supervised deep network to coarsely locate the semantically salient regions. Next, as a postprocessing refinement, we selectively fuse multiple off-the-shelf deep models on the semantically salient regions identified by the previous step to formulate a pixelwise saliency map. Compared with the state-of-the-art (SOTA) models that focus on learning the pixelwise saliency in single images using only perceptual clues, our method aims at investigating the object-level semantic ranks between multiple images, of which the methodology is more consistent with the human attention mechanism. Our method is simple yet effective, and it is the first attempt to consider salient object detection as mainly an object-level semantic reranking problem.
RESUMO
The 2D image based salient object detection (SOD) has been extensively explored, while the 360° omnidirectional image based SOD has received less research attention and there exist three major bottlenecks that are limiting its performance. Firstly, the currently available training data is insufficient for the training of 360° SOD deep model. Secondly, the visual distortions in 360° omnidirectional images usually result in large feature gap between 360° images and 2D images; consequently, the widely used stage-wise training-a widely-used solution to alleviate the training data shortage problem, becomes infeasible when conducing SOD in 360° omnidirectional images. Thirdly, the existing 360° SOD approach has followed a multi-task methodology that performs salient object localization and segmentation-like saliency refinement at the same time, being faced with extremely large problem domain, making the training data shortage dilemma even worse. To tackle all these issues, this paper divides the 360° SOD into a multi-staqe task, the key rationale of which is to decompose the original complex problem domain into sequential easy sub problems that only demand for small-scale training data. Meanwhile, we learn how to rank the "object-level semantical saliency", aiming to locate salient viewpoints and objects accurately. Specifically, to alleviate the training data shortage problem, we have released a novel dataset named 360-SSOD, containing 1,105 360° omnidirectional images with manually annotated object-level saliency ground truth, whose semantical distribution is more balanced than that of the existing dataset. Also, we have compared the proposed method with 13 SOTA methods, and all quantitative results have demonstrated the performance superiority.
