VideoTemp-o3: Harmonizing Temporal Grounding and Video Understanding in Agentic Thinking-with-Videos
arXiv:2602.07801v4 Announce Type: replace-cross Abstract: In long-video understanding, conventional uniform frame sampling often fails to capture key visual evidence, leading to degraded performance and increased hallucinations. To address this, recent agentic thinking-with-videos paradigms have emerged, adopting a localize-clip-answer pipeline in which the model actively identifies relevant video segments, performs dense sampling within those clips, and then produces answers. However, existing methods remain inefficient, suffer from weak localization, and adhere to rigid workflows. To solve t
The proliferation of long-form video content and the increasing sophistication of AI models are driving the need for more efficient and accurate video understanding solutions.
Improved video understanding, especially in long-form content, unlocks new applications for AI agents and enhances their ability to process and act upon complex visual information.
Traditional uniform frame sampling for video understanding is being supplanted by more intelligent, agentic approaches that localize key segments for deeper analysis.
- · AI agent developers
- · Video analytics platforms
- · Content creators using long-form video
- · Computer vision researchers
- · Inefficient video processing methodologies
- · Models reliant on uniform frame sampling
More accurate and nuanced interpretation of multimedia content by AI systems.
Accelerated development of AI agents capable of advanced reasoning over video data.
New forms of automated content creation, surveillance, and educational tools based on deep video understanding.
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Read at arXiv cs.AI