6 accepted papers
In recent years, Transformers have been widely applied in human pose estimation by converting image features into token forms as inputs. However, redundant information in images burdens the network and can even negatively impact training as noise. Thus, we propose a coarse-to-fine Transformer called…
Explainable recommender system has recently drawn increasing attention due to its capability of providing justification to recommendation. Rather than focusing on certain topics or specific item features, the explanation generated by existing works are too general without the guidance of aspects. Ho…
Optical flow estimation aims to find the 2D motion field by identifying corresponding pixels between two images. Despite the tremendous progress of deep learning-based optical flow methods, it remains a challenge to accurately estimate large displacements with motion blur. This is mainly because the…
Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn image features that incorporate large context while keep high spatial resolutions. To approach this goal, the most widely u…
Deep learning as a service has been widely deployed to utilize deep neural network models to provide prediction services. However, this raises privacy concerns since clients need to send sensitive information to servers. In this paper, we focus on the scenario where clients want to classify private…
Obstacle avoidance is the core problem for mobile robots. Its objective is to allow mobile robots to explore an unknown environment without colliding into other objects. It is the basis for various tasks, e.g. surveillance and rescue, etc. Previous approaches mainly focused on geometric models (such…