961 citations · 1.3k across the 14 of their papers we have counts for
14 papers
Two-shot Video Object Segmentation
Kun Yan, Xiao Li, Fangyun Wei +4
Previous works on video object segmentation (VOS) are trained on densely annotated videos. Nevertheless, acquiring annotations in pixel level is expensive and time-consuming. In th…
Localization Distillation for Object Detection
Zhaohui Zheng, Rongguang Ye, Qibin Hou +4
Previous knowledge distillation (KD) methods for object detection mostly focus on feature imitation instead of mimicking the prediction logits due to its inefficiency in distilling…
Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation
Jizong Peng, Ping Wang, Marco Pedersoli +1
Unsupervised pre-training has been proven as an effective approach to boost various downstream tasks given limited labeled data. Among various methods, contrastive learning learns…
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention
Kun Yan, Chenbin Zhang, Jun Hou +4
Multi-label few-shot image classification (ML-FSIC) is the task of assigning descriptive labels to previously unseen images, based on a small number of training examples. A key fea…
Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels
Jizong Peng, Ping Wang, Chrisitian Desrosiers +1
Pre-training a recognition model with contrastive learning on a large dataset of unlabeled data has shown great potential to boost the performance of a downstream task, e.g., image…
Context-aware virtual adversarial training for anatomically-plausible segmentation
Ping Wang, Jizong Peng, Marco Pedersoli +3
Despite their outstanding accuracy, semi-supervised segmentation methods based on deep neural networks can still yield predictions that are considered anatomically impossible by cl…