Enhancing Video-Language Representations With Structural Spatio-Temporal Alignment.

Area of Science:

• Artificial Intelligence
• Computer Vision
• Natural Language Processing

Background:

• Large-scale video-language models (VLMs) show promise but struggle with coarse alignment, temporal dynamics, and detached modalities.
• Existing methods often lack fine-grained understanding of spatial and temporal relationships within video and text.

Purpose of the Study:

• To enhance video-language models (VLMs) by introducing a fine-grained structural spatio-temporal alignment learning method named Finsta.
• To improve the cross-modal alignment, temporal modeling, and overall performance of existing VLMs.

Main Methods:

• Representing text and videos using fine-grained scene graph (SG) structures, unified into a holistic SG (HSG).
• Employing a graph Transformer for textual SG (TSG) encoding and a recurrent graph Transformer for video dynamic SG (DSG) and HSG modeling.
• Utilizing a spatial-temporal Gaussian differential graph Transformer to capture object changes across dimensions and performing object-centered spatial and predicate-centered temporal alignments.

Main Results:

• Finsta consistently improves 13 existing strong-performing VLMs across 6 video-language tasks and 12 datasets.
• Significant state-of-the-art performance enhancements were achieved in both fine-tuning and zero-shot settings.
• The plug-and-play system integrates seamlessly without requiring training from scratch or downstream scene graph annotations.

Conclusions:

• Finsta offers an effective plug-and-play solution for augmenting VLMs with enhanced spatio-temporal understanding.
• The method significantly boosts performance on diverse video-language tasks, establishing new state-of-the-art benchmarks.
• Finsta's ability to improve existing models without extensive retraining makes it a versatile tool for VLM research and application.