SMART: Shot-Aware Multimodal Video Moment Retrieval with Audio-Enhanced MLLM

Researchers introduce SMART, a new multimodal AI framework for video moment retrieval that combines audio and visual features with shot-aware token compression to locate specific temporal segments in untrimmed videos. The method demonstrates significant performance improvements on benchmark datasets, achieving 1.61% and 2.59% gains in key metrics over previous state-of-the-art approaches.

SMART represents an incremental but meaningful advancement in video understanding technology by addressing fundamental limitations in how AI systems locate temporal moments within videos. The framework moves beyond single-modality approaches by integrating audio cues alongside visual features, recognizing that meaningful video moments often depend on synchronized information across multiple sensory channels. This multimodal integration mirrors broader trends in machine learning where richer input representations yield more robust models.

The innovation of shot-aware token compression specifically targets computational efficiency—a critical concern for deploying video understanding systems at scale. By selectively retaining high-information tokens within individual shots rather than processing all visual data uniformly, SMART reduces redundancy while maintaining fine-grained temporal precision. This represents thoughtful engineering that balances model capacity constraints with performance requirements.

The benchmark improvements on Charades-STA and QVHighlights demonstrate measurable progress, though the percentage gains suggest optimization of existing approaches rather than architectural breakthroughs. These datasets represent important evaluation standards in video understanding research, so consistent improvements across multiple metrics validate the framework's effectiveness. The work builds naturally on existing MLLM infrastructure, making adoption feasible within established research and development pipelines.

Looking forward, this line of research influences practical video retrieval applications spanning content creation, surveillance analysis, and media indexing. The integration of audio signals establishes a methodological pattern that other video understanding tasks may adopt. Whether SMART's efficiency gains translate to production deployments depends on real-world performance with longer videos and noisier audio conditions—scenarios not fully addressed in benchmark evaluations.

• →SMART combines audio and visual features through multimodal MLLMs to improve video moment localization accuracy
• →Shot-aware token compression reduces computational redundancy while preserving temporal precision in video understanding
• →Benchmark improvements of 1.61-2.59% demonstrate measurable advances over previous state-of-the-art methods
• →The framework advances video understanding by integrating synchronized multisensory inputs rather than single-modality approaches
• →Research establishes patterns for audio-visual integration applicable to broader video analysis tasks