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

Alberto Maté; Mariella Dimiccoli

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arXiv · 2412.19424

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

Alberto Maté·Mariella Dimiccoli

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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.

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BibTeX RIS

Alberto Maté, Mariella Dimiccoli. 2024-12-27. Temporal Context Consistency Above All: Enhancing Long-Term Anticipation by Learning and Enforcing Temporal Constraints. https://arxiv.org/abs/2412.19424

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