activity
20222024
most citedBi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search

2 citations · 5 across the 11 of their papers we have counts for

collaborators

11 papers

cs.LG2024

Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classification

Xiaoyang Hao, Zhixi Feng, Tongqing Peng +1

Automatic modulation classification (AMC) is an effective way to deal with physical layer threats of the internet of things (IoT). However, there is often label mislabeling in prac…

cs.CV2024

3rd Place Solution for MOSE Track in CVPR 2024 PVUW workshop: Complex Video Object Segmentation

Xinyu Liu, Jing Zhang, Kexin Zhang +3

Video Object Segmentation (VOS) is a vital task in computer vision, focusing on distinguishing foreground objects from the background across video frames. Our work draws inspiratio…

cs.CV2024

Exploring Beyond Logits: Hierarchical Dynamic Labeling Based on Embeddings for Semi-Supervised Classification

Yanbiao Ma, Licheng Jiao, Fang Liu +3

In semi-supervised learning, methods that rely on confidence learning to generate pseudo-labels have been widely proposed. However, increasing research finds that when faced with n…

cs.RO2024

Vision-Based Force Estimation for Minimally Invasive Telesurgery Through Contact Detection and Local Stiffness Models

Shuyuan Yang, My H. Le, Kyle R. Golobish +2

In minimally invasive telesurgery, obtaining accurate force information is difficult due to the complexities of in-vivo end effector force sensing. This constrains development and…

cs.CV2024

Heterogeneous Network Based Contrastive Learning Method for PolSAR Land Cover Classification

Jianfeng Cai, Yue Ma, Zhixi Feng +1

Polarimetric synthetic aperture radar (PolSAR) image interpretation is widely used in various fields. Recently, deep learning has made significant progress in PolSAR image classifi…

cs.CV2023

Data-Centric Long-Tailed Image Recognition

Yanbiao Ma, Licheng Jiao, Fang Liu +3

In the context of the long-tail scenario, models exhibit a strong demand for high-quality data. Data-centric approaches aim to enhance both the quantity and quality of data to impr…