How does the performance of VideoRAG compare to temporal video question answering models on long-form video un

Abstract

Abstract: We present HERO, a novel framework for large-scale video+language omnirepresentation learning. HERO encodes multimodal inputs in a hierarchical structure, where local context of a video frame is captured by a Cross-modal Transformer via multimodal fusion, and global video context is captured by a Temporal Transformer. In addition to standard Masked Language Modeling (MLM) and Masked Frame Modeling (MFM) objectives, we design two new pre-training tasks: (i) Video-Subtitle Matching (VSM), where the model predicts both global and local temporal alignment; and (ii) Frame Order Modeling (FOM), wher

Research Question

How does the performance of VideoRAG compare to temporal video question answering models on long-form video understanding tasks when evaluated with METEOR scores across 10x context scaling from 32K to 128K tokens?

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