arXiv:2609.22582v1 Announce Type: new Abstract: End-to-end and vision-language-action (VLA) driving policies are compared by leaderboard rank, but a rank reports an outcome, not the behaviour behind...
The paper introduces a deployment‑focused evaluation protocol for pedestrian tracking that isolates tracker performance by using shared detections, assessing initialization, gap continuation, identity recovery, close‑neighbor association, and runtime under load, while still reporting HOTA as an aggregate metric. Applied to the JackRabbot Dataset, the protocol reveals that trackers vary widely in capability, with none maintaining spatially correct, same‑identity output after 1 s of missing detector support, and that runtime on an NVIDIA Jetson Orin often falls below the 10 Hz target in crowded scenes. The authors provide open‑source code and scripts to enable reproducible assessment of tracker suitability for real‑world pedestrian‑centric robotics. whyItMatters":"The protocol offers a practical, tracker‑only framework that directly informs the design and deployment of mobile robots operating among pedestrians by highlighting specific failure modes and computational constraints that aggregate scores overlook."
arXiv:2609.01584v1 Announce Type: new Abstract: Vehicle attribute analysis is a key component of Intelligent Transportation Systems (ITS), supporting applications such as vehicle identification, traf...
The paper demonstrates that occlusion can be inferred from the disagreement between two monocular distance cues—apparent height and metric depth—without requiring any occlusion labels or training. By comparing the ordering of these cues, the authors achieve high occlusion detection performance on KITTI and synthetic CARLA scenes, outperforming distance alone. The method relies only on a camera‑only pipeline and a depth model whose errors are close to a pure gain, a condition that can be verified without labels.
The paper introduces Counterfactual Neighborhood Risk Fields (CNRF), an evidence‑grounded framework that improves safety for vision‑language models (VLMs). CNRF builds context‑matched benign‑harmful counterfactual pairs, uses their representation differences to define risk directions, and separates neighborhood support from risk estimation. This approach allows selective intervention rather than blanket rejection, achieving high macro defense success rates across multiple VLMs while preserving benign‑task utility.
arXiv:2606. 05149v1 Announce Type: cross Abstract: Vehicle body type is a significant determinant of cyclist injury severity in overtaking crashes, yet automated tools for classifying vehicles into injury-risk-relevant categories from naturalistic roadway video do not exist in the open literature.
arXiv:2608. 19807v1 Announce Type: new Abstract: Vision-language models (VLMs) can estimate physical quantities such as duration, speed, and acceleration from visual observations, but existing benchmarks primarily assess overall model performance against annotated ground truth.
arXiv:2608. 09101v1 Announce Type: cross Abstract: Semantic segmentation models are trained and evaluated against human-drawn masks, yet remote-sensing annotations are often coarse, incomplete, or misaligned; high overlap scores may then reflect agreement with imperfect labels rather than faithfulness to the image, creating an evaluation paradox.
arXiv:2606. 08123v2 Announce Type: replace-cross Abstract: Model selection for safety-relevant visual recognition is often based on clean aggregate performance, although robustness, transfer, embedded latency, and explanation faithfulness may produce different preferences.
arXiv:2609.13308v1 Announce Type: cross Abstract: A companion evaluation found that naming the target part in a manipulation prompt increased action accuracy by 0.32-0.63 across eight vision-language...
arXiv:2605.31192v2 Announce Type: replace Abstract: Generalizable deepfake detection requires complementary forensic and semantic visual evidence. Specialist encoders capture subtle manipulation trac...
The paper evaluates five vision‑language models on autonomous driving tasks under various visual input conditions, finding that visual corruption affects accuracy and confidence differently across models and datasets. It then tests Visual Evidence Augmentation (VEA) as an inference‑time technique to enhance reliability, observing mixed improvements depending on the model and setting.