5 papers
Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot Learning
Suyi Li, Chenyi Jiang, Shidong Wang +3
Compositional Zero-shot Learning (CZSL) aims to identify novel compositions via known attribute-object pairs. The primary challenge in CZSL tasks lies in the significant discrepanc…
Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation
Yazhou Zhu, Shidong Wang, Tong Xin +2
Few-shot Medical Image Segmentation (FSMIS) is a more promising solution for medical image segmentation tasks where high-quality annotations are naturally scarce. However, current…
Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformer
Yazhou Zhu, Shidong Wang, Tong Xin +1
Automated segmentation of large volumes of medical images is often plagued by the limited availability of fully annotated data and the diversity of organ surface properties resulti…
Learning to Hash Naturally Sorts
Jiaguo Yu, Yuming Shen, Menghan Wang +2
Learning to hash pictures a list-wise sorting problem. Its testing metrics, e.g., mean-average precision, count on a sorted candidate list ordered by pair-wise code similarity. How…
Boosting Generative Zero-Shot Learning by Synthesizing Diverse Features with Attribute Augmentation
Xiaojie Zhao, Yuming Shen, Shidong Wang +1
The recent advance in deep generative models outlines a promising perspective in the realm of Zero-Shot Learning (ZSL). Most generative ZSL methods use category semantic attributes…