Junyu Dong

Few-shot Learning for Domain-specific Fine-grained Image Classification

2019 · arXiv (Cornell University)

Learning to recognize novel visual categories from a few examples is a challenging task for machines in real-world industrial applications. In contrast, humans have the ability to discriminate even similar objects with little supervision. This …

Dynamic Representation Learning for Large-Scale Attributed Networks

2020

Network embedding, which aims at learning low-dimensional representations of nodes in a network, has drawn much attention for various network mining tasks, ranging from link prediction to node classification. In addition to network topological information, …

LaFea: Learning Latent Representation Beyond Feature for Universal Domain Adaptation

2023 · IEEE Transactions on Circuits and Systems for Video Technology

Universal Domain Adaptation (UniDA) is a recent advent problem that aims to transfer the knowledge from the source domain to the target domain without any prior knowledge on label sets. The main challenge is to …

HG-SFDA: HyperGraph Learning Meets Source-Free Unsupervised Domain Adaptation

2025 · IEEE Transactions on Image Processing

Source-Free unsupervised Domain Adaptation (SFDA) aims to classify target samples by only accessing a pre-trained source model and unlabelled target samples. Since no source data is available, transferring the knowledge from the source domain to …