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VideoTG-R1: Boosting Video Temporal Grounding via Curriculum Reinforcement Learning on Reflected Boundary Annotations

arXiv — cs.CV•Tuesday, October 28, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
The recent introduction of VideoTG-R1 marks a significant advancement in video temporal grounding, a crucial area in video understanding. By utilizing curriculum reinforcement learning on reflected boundary annotations, this approach addresses the challenges posed by the quality and difficulty of training samples. This innovation not only enhances the accuracy of locating specific video segments based on language queries but also sets a new standard for future research in the field, making it an exciting development for both researchers and practitioners.
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