961 citations · 1.3k across the 14 of their papers we have counts for
7 papers · 1 filter
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…
CNN Retrieval based Unsupervised Metric Learning for Near-Duplicated Video Retrieval
Hao Cheng, Ping Wang, Chun Qi
As important data carriers, the drastically increasing number of multimedia videos often brings many duplicate and near-duplicate videos in the top results of search. Near-duplicat…
Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning
Kun Yan, Zied Bouraoui, Ping Wang +2
Few-shot learning (FSL) is the task of learning to recognize previously unseen categories of images from a small number of training examples. This is a challenging task, as the ava…
Localization Distillation for Dense Object Detection
Zhaohui Zheng, Rongguang Ye, Ping Wang +4
Knowledge distillation (KD) has witnessed its powerful capability in learning compact models in object detection. Previous KD methods for object detection mostly focus on imitating…