5 papers · 1 filter
Advancing Complex Wide-Area Scene Understanding with Hierarchical Coresets Selection
Jingyao Wang, Yiming Chen, Lingyu Si +1
Scene understanding is one of the core tasks in computer vision, aiming to extract semantic information from images to identify objects, scene categories, and their interrelationsh…
A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation
Dazhao Du, Lingyu Si, Fanjiang Xu +2
Due to the selective absorption and scattering of light by diverse aquatic media, underwater images usually suffer from various visual degradations. Existing underwater image enhan…
On the Transferability and Discriminability of Repersentation Learning in Unsupervised Domain Adaptation
Wenwen Qiang, Ziyin Gu, Lingyu Si +4
In this paper, we addressed the limitation of relying solely on distribution alignment and source-domain empirical risk minimization in Unsupervised Domain Adaptation (UDA). Our in…
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification
Meijuan Su, Feihong He, Fanzhang Li
Few-shot image classification has received considerable attention for overcoming the challenge of limited classification performance with limited samples in novel classes. Most exi…
Less yet robust: crucial region selection for scene recognition
Jianqi Zhang, Mengxuan Wang, Jingyao Wang +3
Scene recognition, particularly for aerial and underwater images, often suffers from various types of degradation, such as blurring or overexposure. Previous works that focus on co…