Uncertainty-Aware Robust Video Activity Anticipation Framework

Core Concepts

The proposed uncertainty-boosted video activity anticipation framework generates uncertainty values to indicate the credibility of the anticipation results, and uses the uncertainty values to modulate the predicted target activity distribution, leading to improved robustness and interpretability.

Abstract

The paper proposes an uncertainty-boosted video activity anticipation framework that addresses the data uncertainty issue in video content and activity evolution. The key contributions are:

The framework generates uncertainty values in parallel with the anticipation outputs to indicate the credibility of the results. The uncertainty values are used to adjust the smoothness of the predicted target activity distribution, enhancing the robustness and interpretability of the model.

The target activity label representation is constructed by incorporating the activity evolution from temporal class correlation and semantic relationship, providing a more comprehensive way to model the uncertainty compared to one-hot labels or multi-label learning.

The relative uncertainty is learned from two complementary perspectives - sample-wise and temporal. The sample-wise relative uncertainty learning forces the model to focus more on hard examples, while the temporal relative uncertainty learning assumes the uncertainty values will gradually decrease as the observed time length increases and the anticipation time length decreases.

Experiments on multiple backbones and benchmarks demonstrate the effectiveness of the proposed framework in terms of improved accuracy, robustness and interpretability, especially when dealing with highly uncertain samples and long-tailed activity categories.

Stats

The EPIC-KITCHENS-55 dataset contains 125 verb classes and 352 noun classes, resulting in 2513 unique activity classes.
The EPIC-KITCHENS-100 dataset has 97 verb classes, 300 noun classes, and 4053 activity classes.
The EGTEA Gaze+ dataset has 106 activity classes.
The MECCANO dataset has 12 verbs, 20 nouns, and 61 unique actions.
The 50 Salads dataset has 7 unique verb classes and 14 noun classes.

Quotes

"Large uncertainty values indicate more diverse but less reliable model outputs, while small uncertainty values indicate more determined and trustable model outputs."
"The data uncertainty seriously impedes the reliability of the anticipation model. Concretely, it results in poor generalization on samples following a flat distribution or activity categories with a large number of possible subsequent activity categories."

Key Insights Distilled From

Uncertainty-boosted Robust Video Activity Anticipation

by Zhaobo Qi,Sh... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18648.pdf

Deeper Inquiries

How can the proposed uncertainty modeling strategy be extended to other video understanding tasks beyond activity anticipation, such as video forecasting and trajectory prediction

The proposed uncertainty modeling strategy can be extended to other video understanding tasks beyond activity anticipation, such as video forecasting and trajectory prediction, by leveraging the concept of relative uncertainty learning. In video forecasting, for example, the uncertainty values can be used to indicate the credibility of the predicted future frames or sequences. By adjusting the smoothness of the predicted frames based on the uncertainty values, the model can provide more reliable and accurate predictions. Similarly, in trajectory prediction tasks, the uncertainty values can help in determining the confidence level of the predicted trajectories, allowing for more robust and interpretable results. By incorporating uncertainty modeling into these tasks, the models can better handle uncertain and complex video data, leading to improved performance and reliability in forecasting and prediction tasks.

How can the constructed target activity label representation be further improved to better capture the complex relationships between visual concepts and activity labels

The constructed target activity label representation can be further improved to better capture the complex relationships between visual concepts and activity labels by incorporating more advanced semantic relationships and context information. One way to enhance the representation is to integrate hierarchical relationships between activity classes, capturing not only direct co-occurrences but also indirect dependencies. By considering the hierarchical structure of activity classes, the model can better understand the relationships between different concepts and improve the accuracy of the anticipation results. Additionally, incorporating contextual information from the video content, such as object interactions and spatial-temporal relationships, can provide a more comprehensive understanding of the activity evolution and further refine the target activity label representation. By enriching the representation with more detailed and contextually relevant information, the model can better capture the nuances and complexities of the relationships between visual concepts and activity labels.

What are the potential applications of the temporal relative uncertainty learning strategy as a proxy task for self-supervised video representation learning

The temporal relative uncertainty learning strategy can be utilized as a proxy task for self-supervised video representation learning in various ways. One potential application is to use the relative uncertainty values as a guiding signal for learning robust and informative video representations. By training the model to predict the relative order of uncertainty values between samples with different anticipation time lengths, the model can learn to capture the temporal dynamics and uncertainty patterns in the video data. This can help in learning representations that are more sensitive to temporal changes and uncertainties in the data, leading to improved performance in downstream tasks. Additionally, the temporal relative uncertainty learning strategy can be used to enhance the model's ability to generalize to new and unseen data by learning to adapt to varying levels of uncertainty in different contexts. By incorporating this strategy into self-supervised learning frameworks, the model can learn more robust and context-aware video representations, benefiting a wide range of video understanding tasks.