Camouflaged object detection (COD) aims to identify objects that are perfectly embedded in
their environment, which has various downstream applications in fields such as medicine …

Automatically detecting/segmenting object (s) that blend in with their surroundings is difficult
for current models. A major challenge is that the intrinsic similarities between such …

To improve the generalization of detectors, for domain adaptive object detection (DAOD),
recent advances mainly explore aligning feature-level distributions between the source and …

In recent years, deep Convolutional Neural Networks (CNNs) have broken all records in
salient object detection. However, training such a deep model requires a large amount of …

In this paper, we study the problem of salient object detection (SOD) for RGB-D images
using both color and depth information. A major technical challenge in performing salient …

Spotting camouflaged objects that are visually assimilated into the background is tricky for
both object detection algorithms and humans who are usually confused or cheated by the …

Camouflaged object detection, which aims to detect/segment the object (s) that blend in with
their surrounding, remains challenging for deep models due to the intrinsic similarities …

We present a learning model that makes full use of boundary information for salient object
segmentation. Specifically, we come up with a novel loss function, ie, Contour Loss, which …

Crowd counting is an important yet challenging task due to the large scale and density
variation. Recent investigations have shown that distilling rich relations among multi-scale …

Video saliency prediction has received increasing attention in the field of computer vision
research. How to model the spatio-temporal information in video frames is a key issue for …