57 citations · 236 across the 22 of their papers we have counts for
7 papers · 1 filter
Light Field Saliency Detection with Dual Local Graph Learning andReciprocative Guidance
Nian Liu, Wangbo Zhao, Dingwen Zhang +2
The application of light field data in salient object de-tection is becoming increasingly popular recently. The diffi-culty lies in how to effectively fuse the features within the…
Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detection
Ni Zhang, Junwei Han, Nian Liu +1
Humans perform co-saliency detection by first summarizing the consensus knowledge in the whole group and then searching corresponding objects in each image. Previous methods usuall…
Instance-Level Relative Saliency Ranking with Graph Reasoning
Nian Liu, Long Li, Wangbo Zhao +2
Conventional salient object detection models cannot differentiate the importance of different salient objects. Recently, two works have been proposed to detect saliency ranking by…
Context-aware Cross-level Fusion Network for Camouflaged Object Detection
Yujia Sun, Geng Chen, Tao Zhou +2
Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings. In addition, the appearance of camouflaged object…
Weakly Supervised Video Salient Object Detection
Wangbo Zhao, Jing Zhang, Long Li +3
Significant performance improvement has been achieved for fully-supervised video salient object detection with the pixel-wise labeled training datasets, which are time-consuming an…
Visual Saliency Transformer
Nian Liu, Ni Zhang, Kaiyuan Wan +2
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-sequence persp…
Salient Object Detection via Integrity Learning
Mingchen Zhuge, Deng-Ping Fan, Nian Liu +3
Although current salient object detection (SOD) works have achieved significant progress, they are limited when it comes to the integrity of the predicted salient regions. We defin…