Dynamic Reflections: Probing Video Representations with Text Alignment
Tyler Zhu, Tengda Han, Leonidas Guibas, Viorica Pătrăucean, Maks Ovsjanikov
2025-11-18
Summary
This paper investigates how well video and text representations align with each other, meaning how similarly computers 'understand' the same information whether it's presented as a video or as words. It's about checking if the way computers process videos and text is consistent and how that impacts their ability to understand what's happening in the video.
What's the problem?
While researchers have made progress in aligning images with text, understanding how videos and text relate to each other is a relatively unexplored area. Videos add the complexity of time – things change and happen in sequence – which makes it harder to compare them to static text descriptions. The core issue is figuring out if current video and language processing models actually 'understand' videos in a way that matches how they understand text, and what factors influence this understanding.
What's the solution?
The researchers performed a detailed study comparing how well video and text encoders (the parts of the computer that convert videos and text into numerical representations) align. They tested different types of video and text data – like single images versus multiple frames of video, and single captions versus more detailed descriptions – to see how these variations affected alignment. They also developed a way to predict how alignment changes based on the amount of visual and textual information available, and explored if better alignment with text also meant better overall video understanding, including the ability to reason about events over time.
Why it matters?
This work is important because it provides a new method for evaluating how well video processing models actually 'understand' what they're seeing. By measuring alignment with text, researchers can get a better sense of a model's capabilities without needing to specifically train it for a particular task. This is especially useful for zero-shot learning, where a model is tested on tasks it hasn't been explicitly trained for, and it helps to improve the development of more powerful and versatile video understanding systems.
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/