6 papers
Conditional Representation Learning for Customized Tasks
Honglin Liu, Chao Sun, Peng Hu +2
Conventional representation learning methods learn a universal representation that primarily captures dominant semantics, which may not always align with customized downstream task…
DUDE: Diffusion-Based Unsupervised Cross-Domain Image Retrieval
Ruohong Yang, Peng Hu, Yunfan Li +1
Unsupervised cross-domain image retrieval (UCIR) aims to retrieve images of the same category across diverse domains without relying on annotations. Existing UCIR methods, which al…
DiFiC: Your Diffusion Model Holds the Secret to Fine-Grained Clustering
Ruohong Yang, Peng Hu, Xi Peng +2
Fine-grained clustering is a practical yet challenging task, whose essence lies in capturing the subtle differences between instances of different classes. Such subtle differences…
Image Clustering with External Guidance
Yunfan Li, Peng Hu, Dezhong Peng +3
The core of clustering is incorporating prior knowledge to construct supervision signals. From classic k-means based on data compactness to recent contrastive clustering guided by…
A Survey on Deep Clustering: From the Prior Perspective
Yiding Lu, Haobin Li, Yunfan Li +2
Facilitated by the powerful feature extraction ability of neural networks, deep clustering has achieved great success in analyzing high-dimensional and complex real-world data. The…
An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model
Yuxin Tian, Mouxing Yang, Yunfan Li +4
Recent studies applied Parameter Efficient Fine-Tuning techniques (PEFTs) to efficiently narrow the performance gap between pre-training and downstream. There are two important fac…