Temporally Consistent Unsupervised Action Segmentation via Unbalanced Optimal Transport

Core Concepts

We propose a novel optimal transport-based method, ASOT, that can efficiently decode temporally consistent action segmentations from noisy frame-action affinity matrices, without requiring prior knowledge of action ordering.

Abstract

The paper proposes a novel method called Action Segmentation Optimal Transport (ASOT) for unsupervised action segmentation in long, untrimmed videos.
Key highlights:

ASOT formulates the action segmentation task as an optimal transport problem, fusing visual information with a structure-aware Gromov-Wasserstein component to encourage temporal consistency.
The unbalanced optimal transport formulation allows ASOT to handle long-tailed action class distributions, which are common in action segmentation datasets.
Unlike prior approaches that rely on hidden Markov models and require knowing the action order, ASOT can handle order variations and repeated actions without this prior knowledge.
ASOT is shown to be effective as a post-processing step for both unsupervised and supervised action segmentation pipelines, yielding state-of-the-art results on several benchmark datasets.
The authors also present a simple self-training pipeline for unsupervised action segmentation, where ASOT is used to generate high-quality pseudo-labels.

Stats

Videos in the Breakfast dataset range from a few seconds to several minutes, with 10 activity categories and 48 actions across all activities.
The YouTube Instructions dataset contains 150 instructional videos belonging to 5 activity categories, with an average video length of 2 minutes.
The 50 Salads dataset contains 50 videos of cooking activities, totaling 4.5 hours in length, with 19 and 12 action classes at the Mid and Eval granularity levels, respectively.
The Desktop Assembly dataset includes 76 videos of assembly activities, each around 1.5 minutes long, with 22 actions performed in a fixed order.

Quotes

"Our method addresses all three limitations of TOT, handling order variations and repeated actions, by expanding the OT formulation, with no significant increase in learnable parameters or network architecture complexity from UFSA [40]."
"Unbalanced OT allows for only a subset of actions to be represented within a video. We argue this is unreasonable for action segmentation since datasets used [2,21,35] exhibit long-tailed class distributions [11]."

Key Insights Distilled From

Temporally Consistent Unbalanced Optimal Transport for Unsupervised Action Segmentation

by Ming Xu,Step... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01518.pdf

Deeper Inquiries

How can the ASOT formulation be extended to handle more complex temporal dependencies, such as hierarchical or concurrent actions, in the action segmentation task

To handle more complex temporal dependencies in the action segmentation task, the ASOT formulation can be extended in several ways. One approach is to incorporate hierarchical action structures by introducing a multi-level optimal transport problem. This would involve defining cost matrices and coupling constraints at different levels of granularity, allowing for the segmentation of actions at various hierarchical levels. Additionally, the formulation could be adapted to handle concurrent actions by modifying the cost functions to penalize conflicting assignments of frames to multiple actions simultaneously. By incorporating constraints that capture the temporal relationships between concurrent actions, the ASOT approach can effectively model and segment complex temporal dependencies in videos.

What other types of structural priors, beyond the Gromov-Wasserstein component, could be incorporated into the optimal transport framework to further improve the quality of the generated segmentations

In addition to the Gromov-Wasserstein component, other types of structural priors can be integrated into the optimal transport framework to enhance the quality of generated segmentations. One possible extension is to incorporate spatial constraints that enforce consistency in the spatial layout of actions within frames. By penalizing assignments that violate spatial coherence, the segmentation results can better reflect the spatial organization of actions in videos. Furthermore, incorporating semantic constraints based on action semantics or contextual information can improve the interpretability and accuracy of the segmentations. By leveraging additional structural priors, the ASOT approach can capture a more comprehensive understanding of the underlying video content and produce more meaningful segmentations.

Can the ASOT approach be adapted to other video understanding tasks, such as video summarization or video question answering, where temporal consistency is also an important consideration

The ASOT approach can be adapted to other video understanding tasks where temporal consistency is crucial, such as video summarization or video question answering. For video summarization, ASOT can be utilized to generate temporally consistent summaries by segmenting key actions or events in the video sequence. By incorporating the ASOT formulation into the summarization pipeline, the generated summaries can maintain coherence and relevance to the original video content. Similarly, in video question answering tasks, ASOT can ensure that the temporal alignment between video frames and action classes is preserved, enabling more accurate and contextually relevant responses to queries. By leveraging the temporal consistency provided by ASOT, these tasks can benefit from improved understanding and interpretation of video content.