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From the 1 of 13 linked papers with an AI index.

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13 papers

cs.CV2026

CertVLA: Certified Defense against Physical Visual Attacks for Vision-Language-Action Models

Hui Lu, Zhijie Peng, Yuqi Lin +8

Vision-Language-Action (VLA) policies are vulnerable to localized physical perturbations, yet existing certified patch defenses target discrete labels and cannot directly certify c…

cs.CV2026

ENCORE: Event-Assisted Complementary Motion Refinement for Learned Video Compression

Shuhan Ye, Hongbin Yu, Chenqi Kong +4

The paper introduces ENCORE, a framework that uses asynchronous event‑camera data to refine motion estimation in learned video compression, improving quality especially under chall…

cs.CV2026

ChronoLock: Protecting Videos from Unauthorized Text-to-Video Personalization

Jiaming He, Jiashu Zhang, Guanyu Hou +4

Text-to-video (T2V) diffusion models have made it increasingly easy to synthesize realistic and temporally coherent videos, while recent personalization techniques allow such model…

cs.NE2026

STARS: Spike Tail-Aware Relational Synthesis for ANN-to-SNN Data-Free Knowledge Distillation

Shuhan Ye, Yi Yu, Qixin Zhang +5

SNNs promise energy-efficient and low-latency inference, but their performance still trails that of ANNs. ANN-to-SNN knowledge distillation helps narrow this gap, yet the original…

cs.NE2026

Benchmarking Fairness in Spiking Neural Networks: Data Bias, Spurious Features, and Hardware Effects

Hudi He, Fukun Wang, Zhe Wang +7

Evaluating fairness in Spiking Neural Networks (SNNs) demands rigorous benchmarks that reflect real-world complexities, yet existing assessments remain limited by superficial datas…

cs.CR2026

Time Is All It Takes: Spike-Retiming Attacks on Event-Driven Spiking Neural Networks

Yi Yu, Qixin Zhang, Shuhan Ye +6

Spiking neural networks (SNNs) compute with discrete spikes and exploit temporal structure, yet most adversarial attacks change intensities or event counts instead of timing. We st…