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20232026
most citedContinual Adversarial Defense

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

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cs.CV2026

AlphaWiSE: Adaptive Weight Interpolation for Continual Multimodal Representation Learning

Sarthak Jain, Qiran Hu, Zhen Zhu +1

Multimodal models such as CLIP learn a shared embedding space for cross-modal retrieval, but continual adaptation to sequentially arriving data can disrupt the cross-modal alignmen…

cs.CV2026

AC3S: Adaptive Conditioning for 3D-Aware Synthetic Data Generation

Eric Ji, Qiran Hu, Wufei Ma +4

Synthetic data generation has emerged as a powerful tool for improving data scalability in computer vision. Recent diffusion-based pipelines have demonstrated strong photorealism.…

cs.CV2024

Learning a Category-level Object Pose Estimator without Pose Annotations

Fengrui Tian, Yaoyao Liu, Adam Kortylewski +4

3D object pose estimation is a challenging task. Previous works always require thousands of object images with annotated poses for learning the 3D pose correspondence, which is lab…

cs.CV2023★ 2 cited

Continual Adversarial Defense

Qian Wang, Hefei Ling, Yingwei Li +3

In response to the rapidly evolving nature of adversarial attacks against visual classifiers, numerous defenses have been proposed to generalize against as many known attacks as po…

cs.CV2023

Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning

Ruxiao Duan, Jieneng Chen, Adam Kortylewski +2

Replay-based methods in class-incremental learning (CIL) have attained remarkable success. Despite their effectiveness, the inherent memory restriction results in saving a limited…