Due to object detection's close relationship with video analysis and image understanding, it
has attracted much research attention in recent years. Traditional object detection methods …

The machine learning community has been overwhelmed by a plethora of deep learning--
based approaches. Many challenging computer vision tasks, such as detection, localization …

While originally designed for natural language processing tasks, the self-attention
mechanism has recently taken various computer vision areas by storm. However, the 2D …

Existing state-of-the-art saliency detection methods heavily rely on CNN-based
architectures. Alternatively, we rethink this task from a convolution-free sequence-to …

In this paper, we design a simple yet powerful deep network architecture, U 2-Net, for salient
object detection (SOD). The architecture of our U 2-Net is a two-level nested U-structure. The …

Deep-learning based salient object detection methods achieve great progress. However, the
variable scale and unknown category of salient objects are great challenges all the time …

We present the first systematic study on concealed object detection (COD), which aims to
identify objects that are visually embedded in their background. The high intrinsic similarities …

Most salient object detection approaches use U-Net or feature pyramid networks (FPN) as
their basic structures. These methods ignore two key problems when the encoder …

Camouflage is a key defence mechanism across species that is critical to survival. Common
camouflage include background matching, imitating the color and pattern of the …

Weakly supervised semantic segmentation produces a pixel-level localization from class
labels; but a classifier trained on such labels is likely to restrict its focus to a small …