The paper shows that mAP-selected semantic features, multinomial scheduled sampling, and a length-modulated training loss improve video captioning numbers on YouTube2Text and roughly match prior state of the art on MSR-VTT.
Video captioning with attention-based lstm and semantic con- sistency,
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A Semantics-Assisted Video Captioning Model Trained with Scheduled Sampling
The paper shows that mAP-selected semantic features, multinomial scheduled sampling, and a length-modulated training loss improve video captioning numbers on YouTube2Text and roughly match prior state of the art on MSR-VTT.