8 citations · 19 across the 8 of their papers we have counts for
8 papers
Towards Open-World Co-Salient Object Detection with Generative Uncertainty-aware Group Selective Exchange-Masking
Yang Wu, Shenglong Hu, Huihui Song +3
The traditional definition of co-salient object detection (CoSOD) task is to segment the common salient objects in a group of relevant images. This definition is based on an assump…
Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking
Fei Xie, Wankou Yang, Bo Liu +3
Existing visual object tracking usually learns a bounding-box based template to match the targets across frames, which cannot accurately learn a pixel-wise representation, thereby…
Meta-Learning with Network Pruning
Hongduan Tian, Bo Liu, Xiao-Tong Yuan +1
Meta-learning is a powerful paradigm for few-shot learning. Although with remarkable success witnessed in many applications, the existing optimization based meta-learning models wi…
Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
Kaihua Zhang, Tengpeng Li, Shiwen Shen +3
Co-saliency detection aims to discover the common and salient foregrounds from a group of relevant images. For this task, we present a novel adaptive graph convolutional network wi…
Dual Temporal Memory Network for Efficient Video Object Segmentation
Kaihua Zhang, Long Wang, Dong Liu +3
Video Object Segmentation (VOS) is typically formulated in a semi-supervised setting. Given the ground-truth segmentation mask on the first frame, the task of VOS is to track and s…
Video Saliency Prediction Using Enhanced Spatiotemporal Alignment Network
Jin Chen, Huihui Song, Kaihua Zhang +2
Due to a variety of motions across different frames, it is highly challenging to learn an effective spatiotemporal representation for accurate video saliency prediction (VSP). To a…
Deep Object Co-segmentation via Spatial-Semantic Network Modulation
Kaihua Zhang, Jin Chen, Bo Liu +1
Object co-segmentation is to segment the shared objects in multiple relevant images, which has numerous applications in computer vision. This paper presents a spatial and semantic…
Sharpen Focus: Learning with Attention Separability and Consistency
Lezi Wang, Ziyan Wu, Srikrishna Karanam +4
Recent developments in gradient-based attention modeling have seen attention maps emerge as a powerful tool for interpreting convolutional neural networks. Despite good localizatio…