AdvAD produces physical-world adversarial patches with improved transferability to unseen object detectors by multi-model optimization, adaptive balancing, and physical variation robustness.
Object recognition from local scale-invariant features
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ST-STORM introduces a dual-branch SSL framework that disentangles semantic content from stylistic appearance using gated latent streams, JEPA for content invariance, and adversarial constraints for style capture.
A hierarchical offline-online framework for 3D global relocalization using synthetic LiDAR and descriptor retrieval achieves 3-second average time and 8 cm accuracy with order-of-magnitude efficiency gains over prior methods.
citing papers explorer
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Transferable Physical-World Adversarial Patches Against Object Detection in Autonomous Driving
AdvAD produces physical-world adversarial patches with improved transferability to unseen object detectors by multi-model optimization, adaptive balancing, and physical variation robustness.
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Stylistic-STORM (ST-STORM) : Perceiving the Semantic Nature of Appearance
ST-STORM introduces a dual-branch SSL framework that disentangles semantic content from stylistic appearance using gated latent streams, JEPA for content invariance, and adversarial constraints for style capture.
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Offline-Online Hierarchical 3D Global Relocalization With Synthetic LiDAR Sensing and Descriptor-Space Retrieval
A hierarchical offline-online framework for 3D global relocalization using synthetic LiDAR and descriptor retrieval achieves 3-second average time and 8 cm accuracy with order-of-magnitude efficiency gains over prior methods.