DaMO: A Data-Efficient Multimodal Orchestrator for Temporal Reasoning with Video LLMs

TL;DR

DaMO is a data-efficient multimodal video language model that excels in fine-grained temporal reasoning and multimodal understanding through a hierarchical architecture and structured training, outperforming prior methods.

Contribution

Introduces DaMO, a novel data-efficient Video LLM with a hierarchical dual-stream architecture and a four-stage training process for improved temporal reasoning.

Findings

Outperforms prior methods on temporal grounding tasks

Achieves superior results on video QA benchmarks

Demonstrates effective multimodal and temporal understanding

Abstract

Large Language Models (LLMs) have recently been extended to the video domain, enabling sophisticated video-language understanding. However, existing Video LLMs often exhibit limitations in fine-grained temporal reasoning, restricting their ability to precisely attribute responses to specific video moments, especially under constrained supervision. We introduce DaMO, a data-efficient Video LLM explicitly designed for accurate temporal reasoning and multimodal understanding. At its core, the proposed Temporal-aware Fuseformer employs a hierarchical dual-stream architecture that progressively captures temporal dynamics within each modality and effectively fuses complementary visual and audio information. To further enhance computational efficiency, DaMO integrates a global residual that reduces spatial redundancy while preserving essential semantic details. We train DaMO via a structured four-stage progressive training paradigm, incrementally equipping the model with multimodal alignment, semantic grounding, and temporal reasoning capabilities. This work also contributes multiple datasets augmented from existing ones with LLM-generated temporally grounded QA pairs for tasks requiring temporal supervision. Comprehensive experiments on temporal grounding and video QA benchmarks demonstrate that DaMO consistently surpasses prior methods, particularly in tasks demanding precise temporal alignment and reasoning. Our work establishes a promising direction for data-efficient video-language modeling.