arXiv Computer Vision
ReSTI: A Source-Grounded Audit and Repair of STI-Bench
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arXiv Computer Vision
Sep 28
STORM-Bench: Evaluating Online Video QA under Evolving and Incomplete Evidence
STORM-Bench is a new benchmark for online video question answering that evaluates models’ ability to track state transitions and selectively abstain when visual evidence is insufficient. It contains 5,736 questions across 630 short, change‑dense episodes in five egocentric domains and two simulation subsets, with questions stratified by change intensity and answerability. The benchmark introduces STORM‑BR, a harmonic metric that reveals abstention failures and overconfidence on uncertain queries, showing that traditional accuracy masks gaps in epistemic reliability and state tracking.
arXiv Computer Vision
Oct 15
Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation
arXiv:2610.20571v1 Announce Type: new Abstract: Long-form agentic video generation must turn a screenplay into shots that remain spatially and temporally consistent across nonconsecutive requests. Pa...
arXiv Computer Vision
Sep 24
SGDet3D++: Geometry-Grounded Semantics for 4D Radar and Camera 3D Object Detection
SGDet3D++ introduces a geometry‑grounded approach to 4D radar‑camera 3D object detection by explicitly conditioning evidence on evolving object hypotheses. It employs Anchor‑Grounded Semantic Retrieval, Geometry‑Consistent Anchor Refinement, and Doppler‑Verified Correspondence to filter and align semantic, geometric, and temporal cues before updating queries. The method achieves significant performance gains on OmniHD‑Scenes, ManTruckScenes, and TJ4DRadSet, with detailed ablations showing improvements in occlusion handling, target‑return purity, and motion consistency.
arXiv Computer Vision
Sep 10
VANTAGE-Bench: Evaluating the Infrastructure AI Gap in Vision-Language Models
arXiv:2609.09396v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) advance toward physical deployment, the focus has remained on action-oriented Embodied AI evaluated on subject-centric...
arXiv Computer Vision
Sep 22
Do LiDAR Language Models Really Understand Spatio-temporal Relationships?
The paper introduces LiDAR-Hallu, a benchmark with 10,000 questions designed to test 4D LiDAR language models on spatio-temporal reasoning. It shows that models often achieve high multiple-choice accuracy by exploiting trivial patterns, such as always selecting the same option or relying on candidate duration, rather than truly understanding object relationships. Detailed analysis reveals systematic failures, especially in lateral-motion cases and opposite-answer scenarios, indicating that aggregate accuracy masks underlying reasoning gaps.
arXiv AI
Jun 11
Reason, Then Re-reason: Cross-view Revisiting Improves Spatial Reasoning
arXiv:2606. 11683v1 Announce Type: cross Abstract: Spatial reasoning from egocentric videos is inherently challenging because the observable evidence is constrained by the camera trajectory.