arXiv:2607. 25736v1 Announce Type: cross Abstract: Perception is one of the primary applications where neural networks outperform conventional algorithms.
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The paper introduces a Kolmogorov-Arnold network as an interpretable post‑hoc surrogate to assess the trustworthiness of YOLOv10 object detections, using seven geometric and semantic features. Its additive spline structure allows direct visualization of each feature’s influence, revealing when confidence scores are reliable or unreliable. A bootstrapped BLIP language‑image model generates scene captions, providing a lightweight multimodal interface that does not compromise interpretability. Experiments on COCO and University of Bath campus images show the framework accurately flags low‑trust predictions under blur, occlusion, or low texture, offering actionable insights for risk mitigation.
With the development of applications like autonomous driving, object detection has gained significant attention, while also highlighting critical vulnerabilities like backdoor attacks that severely co...
arXiv:2609.28239v1 Announce Type: new Abstract: With the development of applications like autonomous driving, object detection has gained significant attention, while also highlighting critical vulne...
arXiv:2607. 16938v1 Announce Type: cross Abstract: End-to-end autonomous driving models are now able to navigate complex road scenarios, mapping raw sensor observations directly to observed paths for open-loop evaluation and often effective driving in closed-loop evaluation.
End-to-end autonomous driving models are now able to navigate complex road scenarios, mapping raw sensor observations directly to observed paths for open-loop evaluation and often effective driving in closed-loop evaluation. Yet the internal logic of these safety-critical systems remains largely opaque, due to the complexity of traffic scenes.
arXiv:2607. 18195v1 Announce Type: cross Abstract: Vision models have been found to be susceptible to perturbations such as motion blur induced at runtime by a shaking camera.