  This paper identifies and addresses a serious design bias of existing salient
object detection (SOD) datasets, which unrealistically assume that each image
should contain at least one clear and uncluttered salient object. This design
bias has led to a saturation in performance for state-of-the-art SOD models
when evaluated on existing datasets. However, these models are still far from
satisfactory when applied to real-world scenes. Based on our analyses, we
propose a new high-quality dataset and update the previous saliency benchmark.
Specifically, our dataset, called Salient Objects in Clutter~\textbf{(SOC)},
includes images with both salient and non-salient objects from several common
object categories. In addition to object category annotations, each salient
image is accompanied by attributes that reflect common challenges in common
scenes, which can help provide deeper insight into the SOD problem. Further,
with a given saliency encoder, e.g., the backbone network, existing saliency
models are designed to achieve mapping from the training image set to the
training ground-truth set. We, therefore, argue that improving the dataset can
yield higher performance gains than focusing only on the decoder design. With
this in mind, we investigate several dataset-enhancement strategies, including
label smoothing to implicitly emphasize salient boundaries, random image
augmentation to adapt saliency models to various scenarios, and self-supervised
learning as a regularization strategy to learn from small datasets. Our
extensive results demonstrate the effectiveness of these tricks. We also
provide a comprehensive benchmark for SOD, which can be found in our
repository: https://github.com/DengPingFan/SODBenchmark.
