Temporal Preservation over Processing: Diagnosing and Designing Spatiotemporal Single-Stage Video Detectors
arXiv:2606.31421v1 Announce Type: cross Abstract: Single-stage video object detectors are increasingly deployed in time-critical applications, yet it remains unclear whether these models genuinely reason over temporal context or merely exploit a single informative frame-a gap hidden by standard metrics, which reward correct predictions regardless of how they are reached. We address this from two complementary directions: first, we propose TemporalLens, a model-agnostic diagnostic framework probing temporal dependence through controlled perturbations, structured occlusions, temporal shuffling,
The proliferation of single-stage video object detectors in time-critical applications necessitates a deeper understanding of their true temporal reasoning capabilities, which this research addresses through novel diagnostic tools.
This research provides critical tools for evaluating and improving the robustness and reliability of AI models in dynamic environments, directly impacting the deployment of autonomous systems and real-time decision-making applications.
We now have a model-agnostic framework (TemporalLens) to rigorously diagnose how spatiotemporal video detectors leverage or neglect temporal context, moving beyond mere accuracy metrics to understand underlying mechanisms.
- · AI researchers
- · Developers of real-time video detection systems
- · Industries relying on AI for time-critical operations
- · Developers using black-box video detectors without temporal validation
- · Legacy video detection methodologies that over-rely on single-frame data
Improved design and deployment of more truly 'temporal' video object detectors across various applications.
Increased trust and reliability in AI systems that operate in dynamic, time-sensitive environments, fostering broader adoption.
Acceleration of research into more genuinely spatiotemporally aware AI architectures, potentially influencing future AI agent development.
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Read at arXiv cs.AI