3 citations · 3 across the 4 of their papers we have counts for
5 papers
Beyond Visual Cues: Leveraging General Semantics as Support for Few-Shot Segmentation
Jin Wang, Bingfeng Zhang, Jian Pang +3
Few-shot segmentation (FSS) aims to segment novel classes under the guidance of limited support samples by a meta-learning paradigm. Existing methods mainly mine references from su…
Unbiased Semantic Decoding with Vision Foundation Models for Few-shot Segmentation
Jin Wang, Bingfeng Zhang, Jian Pang +3
Few-shot segmentation has garnered significant attention. Many recent approaches attempt to introduce the Segment Anything Model (SAM) to handle this task. With the strong generali…
Rethinking Prior Information Generation with CLIP for Few-Shot Segmentation
Jin Wang, Bingfeng Zhang, Jian Pang +2
Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes. Most previous approaches rely on extracting high-level feature maps…
Balancing Privacy Protection and Interpretability in Federated Learning
Zhe Li, Honglong Chen, Zhichen Ni +1
Federated learning (FL) aims to collaboratively train the global model in a distributed manner by sharing the model parameters from local clients to a central server, thereby poten…
Heterogeneous Graph Learning for Explainable Recommendation over Academic Networks
Xiangtai Chen, Tao Tang, Jing Ren +3
With the explosive growth of new graduates with research degrees every year, unprecedented challenges arise for early-career researchers to find a job at a suitable institution. Th…