Action Anticipation at a Glimpse: To What Extent Can Multimodal Cues Replace Video?

2025-12-02

AI Summary

This study investigates whether reliable action anticipation can be achieved from a single frame without temporal video information. To this end, we propose AAG, a multimodal single-frame action anticipation framework that unifies RGB images, depth maps, vision-language model–generated textual context, and single-frame action recognition priors—enabling joint spatial-semantic reasoning without explicit temporal modeling. AAG employs a Transformer-based architecture for cross-modal feature alignment and fusion. Evaluated on three assembly action datasets—IKEA-ASM, Meccano, and Assembly101—AAG matches or exceeds the performance of state-of-the-art temporal models, demonstrating that rich multimodal cues from a single frame suffice for high-accuracy action anticipation. Our key contribution is challenging the prevailing video-dependency paradigm by establishing the efficacy and practicality of multimodal single-frame fusion for action anticipation.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Anticipating actions before they occur is a core challenge in action understanding research. While conventional methods rely on extracting and aggregating temporal information from videos, as humans we can often predict upcoming actions by observing a single moment from a scene, when given sufficient context. Can a model achieve this competence? The short answer is yes, although its effectiveness depends on the complexity of the task. In this work, we investigate to what extent video aggregation can be replaced with alternative modalities. To this end, based on recent advances in visual feature extraction and language-based reasoning, we introduce AAG, a method for Action Anticipation at a Glimpse. AAG combines RGB features with depth cues from a single frame for enhanced spatial reasoning, and incorporates prior action information to provide long-term context. This context is obtained either through textual summaries from Vision-Language Models, or from predictions generated by a single-frame action recognizer. Our results demonstrate that multimodal single-frame action anticipation using AAG can perform competitively compared to both temporally aggregated video baselines and state-of-the-art methods across three instructional activity datasets: IKEA-ASM, Meccano, and Assembly101.

Problem

• Investigates replacing video with multimodal cues for action anticipation
• Combines RGB, depth, and prior context from single frames
• Evaluates competitive performance against video-based methods on datasets

Innovation

• Combines RGB and depth cues from single frames
• Incorporates textual summaries from Vision-Language Models
• Uses prior action predictions for long-term context