1 code implementation • 30 May 2022 • Xin Wen, Bingchen Zhao, Anlin Zheng, Xiangyu Zhang, Xiaojuan Qi
The semantic grouping is performed by assigning pixels to a set of learnable prototypes, which can adapt to each sample by attentive pooling over the feature and form new slots.
Ranked #15 on Unsupervised Semantic Segmentation on COCO-Stuff-27 (Accuracy metric)
2 code implementations • CVPR 2022 • Anlin Zheng, Yuang Zhang, Xiangyu Zhang, Xiaojuan Qi, Jian Sun
Experiments show that our method can significantly boost the performance of query-based detectors in crowded scenes.
Ranked #1 on Object Detection on CrowdHuman
3 code implementations • CVPR 2020 • Xuangeng Chu, Anlin Zheng, Xiangyu Zhang, Jian Sun
We propose a simple yet effective proposal-based object detector, aiming at detecting highly-overlapped instances in crowded scenes.
Ranked #2 on Pedestrian Detection on TJU-Ped-campus
no code implementations • 23 Nov 2018 • Jia Li, Junjie Wu, Anlin Zheng, Yafei Song, Yu Zhang, Xiaowu Chen
Segmenting primary objects in a video is an important yet challenging problem in computer vision, as it exhibits various levels of foreground/background ambiguities.
no code implementations • ICCV 2017 • Xiaowu Chen, Anlin Zheng, Jia Li, Feng Lu
Toward this end, this paper proposes two-stream fixation-semantic CNNs, whose architecture is inspired by the fact that salient objects in complex images can be unambiguously annotated by selecting the pre-segmented semantic objects that receive the highest fixation density in eye-tracking experiments.
no code implementations • ICCV 2017 • Jia Li, Anlin Zheng, Xiaowu Chen, Bin Zhou
By applying CCNN on each video frame, the spatial foregroundness and backgroundness maps can be initialized, which are then propagated between various frames so as to segment primary video objects and suppress distractors.
no code implementations • CVPR 2017 • Changqun Xia, Jia Li, Xiaowu Chen, Anlin Zheng, Yu Zhang
Finding what is and what is not a salient object can be helpful in developing better features and models in salient object detection (SOD).