140 citations · 919 across the 60 of their papers we have counts for
5 papers · 1 filter
Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation
Xueyi Li, Tianfei Zhou, Jianwu Li +2
Acquiring sufficient ground-truth supervision to train deep visual models has been a bottleneck over the years due to the data-hungry nature of deep learning. This is exacerbated i…
Manual-Label Free 3D Detection via An Open-Source Simulator
Zhen Yang, Chi Zhang, Huiming Guo +1
LiDAR based 3D object detectors typically need a large amount of detailed-labeled point cloud data for training, but these detailed labels are commonly expensive to acquire. In thi…
Unsupervised Object Detection with LiDAR Clues
Hao Tian, Yuntao Chen, Jifeng Dai +2
Despite the importance of unsupervised object detection, to the best of our knowledge, there is no previous work addressing this problem. One main issue, widely known to the commun…
Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint Estimates
Ke Sun, Zigang Geng, Depu Meng +4
The typical bottom-up human pose estimation framework includes two stages, keypoint detection and grouping. Most existing works focus on developing grouping algorithms, e.g., assoc…
Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels
Junran Peng, Xingyuan Bu, Ming Sun +3
Training with more data has always been the most stable and effective way of improving performance in deep learning era. As the largest object detection dataset so far, Open Images…