74 citations · 170 across the 12 of their papers we have counts for
8 papers · 1 filter
Light-ResKAN: A Parameter-Sharing Lightweight KAN with Gram Polynomials for Efficient SAR Image Recognition
Pan Yi, Weijie Li, Xiaodong Chen +3
Synthetic Aperture Radar (SAR) image recognition is vital for disaster monitoring, military reconnaissance, and ocean observation. However, large SAR image sizes hinder deep learni…
A Causal Adjustment Module for Debiasing Scene Graph Generation
Li Liu, Shuzhou Sun, Shuaifeng Zhi +4
While recent debiasing methods for Scene Graph Generation (SGG) have shown impressive performance, these efforts often attribute model bias solely to the long-tail distribution of…
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning
Huali Xu, Li Liu, Shuaifeng Zhi +4
Existing Cross-Domain Few-Shot Learning (CDFSL) methods require access to source domain data to train a model in the pre-training phase. However, due to increasing concerns about d…
LSKNet: A Foundation Lightweight Backbone for Remote Sensing
Yuxuan Li, Xiang Li, Yimian Dai +5
Remote sensing images pose distinct challenges for downstream tasks due to their inherent complexity. While a considerable amount of research has been dedicated to remote sensing c…
Towards Assessing the Synthetic-to-Measured Adversarial Vulnerability of SAR ATR
Bowen Peng, Bo Peng, Jingyuan Xia +3
Recently, there has been increasing concern about the vulnerability of deep neural network (DNN)-based synthetic aperture radar (SAR) automatic target recognition (ATR) to adversar…
Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture
Weijie Li, Yang Wei, Tianpeng Liu +5
The growing Synthetic Aperture Radar (SAR) data has the potential to build a foundation model through Self-Supervised Learning (SSL) methods, which can achieve various SAR Automati…
Enhancing Representations through Heterogeneous Self-Supervised Learning
Zhong-Yu Li, Bo-Wen Yin, Yongxiang Liu +2
Incorporating heterogeneous representations from different architectures has facilitated various vision tasks, e.g., some hybrid networks combine transformers and convolutions. How…
Fast Sparse PCA via Positive Semidefinite Projection for Unsupervised Feature Selection
Junjing Zheng, Xinyu Zhang, Yongxiang Liu +3
In the field of unsupervised feature selection, sparse principal component analysis (SPCA) methods have attracted more and more attention recently. Compared to spectral-based metho…