Time-R1 applies RL with verifiable rewards to post-train LVLMs for temporal video grounding, reaching state-of-the-art results on multiple datasets using only 2.5K samples while also improving general video capabilities.
Now, let's evaluate the options: (A) C folds the dress, places it on the ironing board, and then hangs it up
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Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding
Time-R1 applies RL with verifiable rewards to post-train LVLMs for temporal video grounding, reaching state-of-the-art results on multiple datasets using only 2.5K samples while also improving general video capabilities.