SMART Framework Advances Video Moment Retrieval with Audio-Enhanced Multimodal Processing
Researchers have introduced SMART, a multimodal large language model framework that localizes specific temporal segments in videos using both audio and visual cues alongside shot-level temporal structure. Most existing video moment retrieval methods rely on a single visual modality and coarse temporal understanding, limiting their effectiveness on complex videos. SMART's improvements on standard benchmarks suggest that incorporating audio and structured token compression can meaningfully advance automated video understanding.
SMART (Shot-aware Multimodal Audio-enhanced Retrieval of Temporal Segments) is a new framework for video moment retrieval, a task that involves pinpointing a specific time segment in an untrimmed video based on a natural language query. Unlike most prior approaches that depend solely on visual features, SMART integrates audio cues with visual representations and exploits shot-level temporal structure to improve localization accuracy. A key technical contribution is Shot-aware Token Compression, which selectively retains high-information tokens within each shot, reducing redundancy while preserving fine-grained temporal details. The system also incorporates refined prompt design to better leverage audio-visual signals within a multimodal large language model architecture. Evaluations on two standard benchmarks—Charades-STA and QVHighlights—show SMART outperforms current state-of-the-art methods, achieving a 1.61% gain in R1@0.5 and a 2.59% gain in R1@0.7 on Charades-STA. The work was submitted to arXiv in November 2025 and updated in June 2026, and has not yet undergone formal peer review.
What's missing
The paper has not undergone formal peer review, so independent validation of the reported benchmark gains is pending. The study does not report computational cost or inference latency comparisons with baseline methods, which are relevant for practical deployment. It is also unclear how SMART performs on videos where audio is absent, low-quality, or misaligned with visual content, and the generalizability beyond the two tested benchmarks (Charades-STA and QVHighlights) remains an open question.
What different sources said
- arXiv cs.AICenter
SMART: Shot-Aware Multimodal Video Moment Retrieval with Audio-Enhanced MLLM
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