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 …

Salient object detection models often demand a considerable amount of computation cost to
make precise prediction for each pixel, making them hardly applicable on low-power …

Retinex theory is developed mainly to decompose an image into the illumination and
reflectance components by analyzing local image derivatives. In this theory, larger …

We present a novel group collaborative learning framework (GCNet) capable of detecting co-
salient objects in real time (16ms), by simultaneously mining consensus representations at …

CNN-based salient object detection (SOD) methods achieve impressive performance.
However, the way semantic information is encoded in them and whether they are category …

Co-salient object detection (CoSOD) is a newly emerging and rapidly growing branch of
salient object detection (SOD), which aims to detect the co-occurring salient objects in …

Co-salient object detection (Co-SOD) aims at discovering the common objects in a group of
relevant images. Mining a co-representation is essential for locating co-salient objects …

No-reference image quality assessment (NR-IQA) has always been a difficult research
problem because digital images may suffer very diverse types of distortions and their …

With the emerging use of technology and screen-oriented applications in our daily life,
screen content images have gained the same importance as natural scene images. This …

In this paper, we present a novel end-to-end group collaborative learning network, termed
GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural …