Search Results for author:
Found 10 papers, 7 papers with code
NUDT4MSTAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild
This paper introduces NUDT4MSTAR, a large-scale SAR dataset for remote sensing target recognition in the wild, including 40 vehicle target types and various imaging conditions across 5 realistic scenes.
DeMo: Decoupled Momentum Optimization
Training large neural networks typically requires sharing gradients between accelerators through specialized high-speed interconnects.
MaDiNet: Mamba Diffusion Network for SAR Target Detection
The fundamental challenge in SAR target detection lies in developing discriminative, efficient, and robust representations of target characteristics within intricate non-cooperative environments.
S$^4$ST: A Strong, Self-transferable, faSt, and Simple Scale Transformation for Transferable Targeted Attack
Transferable targeted adversarial attacks (TTAs) against deep neural networks have been proven significantly more challenging than untargeted ones, yet they remain relatively underexplored.
Enhancing Transferability of Targeted Adversarial Examples: A Self-Universal Perspective
We also contribute a surprising empirical insight that one of the most fundamental transformations, simple image scaling, is highly effective, scalable, sufficient, and necessary in enhancing targeted transferability.
Towards Assessing the Synthetic-to-Measured Adversarial Vulnerability of SAR ATR
Recently, there has been increasing concern about the vulnerability of deep neural network (DNN)-based synthetic aperture radar (SAR) automatic target recognition (ATR) to adversarial attacks, where a DNN could be easily deceived by clean input with imperceptible but aggressive perturbations.
ARBiBench: Benchmarking Adversarial Robustness of Binarized Neural Networks
2) BNNs consistently exhibit better adversarial robustness under black-box attacks.
YaRN: Efficient Context Window Extension of Large Language Models
Rotary Position Embeddings (RoPE) have been shown to effectively encode positional information in transformer-based language models.
Learning Invariant Representation via Contrastive Feature Alignment for Clutter Robust SAR Target Recognition
The proposed method contributes a mixed clutter variants generation strategy and a new inference branch equipped with channel-weighted mean square error (CWMSE) loss for invariant representation learning.
Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense
Toward building more robust DNN-based SAR ATR models, this article explores the domain knowledge of SAR imaging process and proposes a novel Scattering Model Guided Adversarial Attack (SMGAA) algorithm which can generate adversarial perturbations in the form of electromagnetic scattering response (called adversarial scatterers).
