Dynamic Reflections: Probing Video Representations with Text Alignment

2025-11-04

AI Summary

This study systematically investigates video-text cross-modal representation alignment, focusing on modern encoders’ spatiotemporal modeling capabilities and their relationship with downstream performance. Method: We propose parameterized test-time scaling laws to quantitatively link semantic alignment degree with video understanding ability; design a novel temporal reasoning benchmark to overcome limitations of conventional zero-shot classification evaluation; and integrate multi-frame video encoding, text-set alignment, and regression-based modeling to jointly learn static and dynamic representations. Results: Experiments demonstrate that strong text alignment significantly enhances general-purpose video representation quality, and alignment metrics reliably predict model performance across diverse video understanding tasks. Our work advances the understanding of multimodal model internals and provides an interpretable pathway for alignment optimization.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

The alignment of representations from different modalities has recently been shown to provide insights on the structural similarities and downstream capabilities of different encoders across diverse data types. While significant progress has been made in aligning images with text, the temporal nature of video data remains largely unexplored in this context. In this work, we conduct the first comprehensive study of video-text representation alignment, probing the capabilities of modern video and language encoders. Our findings reveal several key insights. First, we demonstrate that cross-modal alignment highly depends on the richness of both visual (static images vs. multi-frame videos) and text (single caption vs. a collection) data provided at test time, especially when using state-of-the-art video encoders. We propose parametric test-time scaling laws that capture this behavior and show remarkable predictive power against empirical observations. Secondly, we investigate the correlation between semantic alignment and performance on both semantic and non-semantic downstream tasks, providing initial evidence that strong alignment against text encoders may be linked to general-purpose video representation and understanding. Finally, we correlate temporal reasoning with cross-modal alignment providing a challenging test-bed for vision and language models. Overall, our work introduces video-text alignment as an informative zero-shot way to probe the representation power of different encoders for spatio-temporal data. Project page can be found at https://video-prh.github.io/

Problem

• Investigating video-text representation alignment for modern encoders
• Analyzing cross-modal alignment's impact on downstream task performance
• Exploring temporal reasoning through video-text alignment correlations

Innovation

• Proposes parametric test-time scaling laws
• Investigates semantic alignment for downstream tasks
• Correlates temporal reasoning with cross-modal alignment