146 citations · 365 across the 15 of their papers we have counts for
15 papers
Rethinking Lightweight Salient Object Detection via Network Depth-Width Tradeoff
Jia Li, Shengye Qiao, Zhirui Zhao +3
Existing salient object detection methods often adopt deeper and wider networks for better performance, resulting in heavy computational burden and slow inference speed. This inspi…
Part-guided Relational Transformers for Fine-grained Visual Recognition
Yifan Zhao, Jia Li, Xiaowu Chen +1
Fine-grained visual recognition is to classify objects with visually similar appearances into subcategories, which has made great progress with the development of deep CNNs. Howeve…
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection
Chenxi Xie, Changqun Xia, Mingcan Ma +3
Recent salient object detection (SOD) methods based on deep neural network have achieved remarkable performance. However, most of existing SOD models designed for low-resolution in…
Receptive Field Broadening and Boosting for Salient Object Detection
Mingcan Ma, Changqun Xia, Chenxi Xie +2
Salient object detection requires a comprehensive and scalable receptive field to locate the visually significant objects in the image. Recently, the emergence of visual transforme…
RGB-D Salient Object Detection with Ubiquitous Target Awareness
Yifan Zhao, Jiawei Zhao, Jia Li +1
Conventional RGB-D salient object detection methods aim to leverage depth as complementary information to find the salient regions in both modalities. However, the salient object d…
Is Depth Really Necessary for Salient Object Detection?
Jiawei Zhao, Yifan Zhao, Jia Li +1
Salient object detection (SOD) is a crucial and preliminary task for many computer vision applications, which have made progress with deep CNNs. Most of the existing methods mainly…