It's a Matter of Time: Three Lessons on Long-Term Motion for Perception

2026-02-16

AI Summary

This work investigates the role of long-term motion cues in visual perception tasks and their advantages over static image representations. The authors construct a low-dimensional, efficient motion representation based on point trajectory estimation and systematically evaluate its performance across multiple tasks, including action recognition, object understanding, material classification, and spatial reasoning. The study demonstrates that this motion-based representation captures rich semantic information and exhibits significantly stronger generalization than conventional image features under low-data and zero-shot settings. Furthermore, when fused with standard video representations, it yields additional accuracy gains. Notably, the proposed approach achieves a superior trade-off between computational efficiency (measured in GFLOPs) and performance, highlighting its potential for efficient visual understanding.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Representation Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Temporal information has long been considered to be essential for perception. While there is extensive research on the role of image information for perceptual tasks, the role of the temporal dimension remains less well understood: What can we learn about the world from long-term motion information? What properties does long-term motion information have for visual learning? We leverage recent success in point-track estimation, which offers an excellent opportunity to learn temporal representations and experiment on a variety of perceptual tasks. We draw 3 clear lessons: 1) Long-term motion representations contain information to understand actions, but also objects, materials, and spatial information, often even better than images. 2) Long-term motion representations generalize far better than image representations in low-data settings and in zero-shot tasks. 3) The very low dimensionality of motion information makes motion representations a better trade-off between GFLOPs and accuracy than standard video representations, and used together they achieve higher performance than video representations alone. We hope these insights will pave the way for the design of future models that leverage the power of long-term motion information for perception.

Problem

• long-term motion
• temporal information
• visual perception
• motion representation

Innovation

• long-term motion
• temporal representation
• point-track estimation
• zero-shot generalization
• computational efficiency