Temporal Context Consistency Above All: Enhancing Long-Term Anticipation by Learning and Enforcing Temporal Constraints

2024-12-27

AI Summary

This work addresses long-horizon video action forecasting, jointly modeling action classes and their durations to ensure temporal coherence and physical plausibility. We propose an encoder-decoder framework with three key contributions: (1) a novel bidirectional action context regularization module that explicitly captures long-range temporal dependencies; (2) a globally optimized action transition matrix learned from annotated segments to enforce physically realistic state transitions; and (3) an action-segment-specific encoder that enhances representation quality of observed segments. The method supports parallel decoding and probabilistic duration prediction, augmented by explicit temporal consistency constraints. Evaluated on four major benchmarks—EpicKitchen-55, EGTEA+, 50Salads, and Breakfast—our approach achieves state-of-the-art or competitive performance, significantly outperforming both LLM-based and traditional probabilistic methods that operate on trimmed inputs.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This paper proposes a method for long-term action anticipation (LTA), the task of predicting action labels and their duration in a video given the observation of an initial untrimmed video interval. We build on an encoder-decoder architecture with parallel decoding and make two key contributions. First, we introduce a bi-directional action context regularizer module on the top of the decoder that ensures temporal context coherence in temporally adjacent segments. Second, we learn from classified segments a transition matrix that models the probability of transitioning from one action to another and the sequence is optimized globally over the full prediction interval. In addition, we use a specialized encoder for the task of action segmentation to increase the quality of the predictions in the observation interval at inference time, leading to a better understanding of the past. We validate our methods on four benchmark datasets for LTA, the EpicKitchen-55, EGTEA+, 50Salads and Breakfast demonstrating superior or comparable performance to state-of-the-art methods, including probabilistic models and also those based on Large Language Models, that assume trimmed video as input. The code will be released upon acceptance.

Problem

• Action Prediction
• Video Analysis
• Temporal Consistency

Innovation

• Encoder-Decoder Architecture
• Action Coherence
• Transition Learning