Scaling Up End-to-End Temporal Action Detection with Efficient Adapter Tuning

Core Concepts

By introducing a novel temporal-informative adapter and an alternative adapter placement, our method AdaTAD achieves state-of-the-art performance on multiple temporal action detection datasets, becoming the first end-to-end approach to outperform the best feature-based methods.

Abstract

The paper introduces an efficient end-to-end framework for temporal action detection (TAD) that scales up the model size to 1 billion parameters and the input data to 1,536 frames. The key innovations are: Temporal-Informative Adapter (TIA): A novel lightweight module that reduces training memory by only updating the adapter parameters, while also aggregating temporal context from adjacent frames. Alternative Adapter Placement: An external adapter placement that further minimizes memory usage and enables scaling up the model and data size to unprecedented levels. The authors establish a new state-of-the-art across multiple TAD datasets, including achieving 75.4% mAP on THUMOS14, outperforming the previous feature-based best result by a large margin. This work highlights the potential of scaling up end-to-end TAD training, marking a possible paradigm shift in the field.

Stats

The proposed method scales up the TAD model to 1 billion parameters and the input video to 1,536 frames. Using the VideoMAEv2-giant backbone and 1,536 frames, AdaTAD achieves 75.4% mAP on THUMOS14, outperforming the previous feature-based best result of 71.5%. On ActivityNet-1.3, AdaTAD with the largest model and data achieves 41.9% mAP. On EPIC-Kitchens 100, AdaTAD with the VideoMAE-L backbone achieves 29.3% mAP, surpassing the previous feature-based methods.

Quotes

"Owing to our efficient design, we are able to train end-to-end on VideoMAEv2-giant and achieve 75.4% mAP on THUMOS14, being the first end-to-end model to outperform the best feature-based methods." "Remarkably, this represents the first end-to-end approach that outperforms the previous feature-based methods by a large margin."

Key Insights Distilled From

End-to-End Temporal Action Detection with 1B Parameters Across 1000 Frames

by Shuming Liu,... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2311.17241.pdf

Deeper Inquiries

How can the proposed temporal-informative adapter be extended to other video understanding tasks beyond temporal action detection

The proposed temporal-informative adapter can be extended to other video understanding tasks beyond temporal action detection by adapting it to suit the specific requirements of those tasks. For instance, in video classification tasks, the adapter can be modified to focus on capturing long-range dependencies between frames to improve the model's understanding of temporal sequences. In video segmentation tasks, the adapter can be designed to aggregate spatial and temporal information effectively to enhance the segmentation accuracy. By customizing the adapter's architecture and functionality based on the needs of different video understanding tasks, it can serve as a versatile component in various applications.

What are the potential limitations of the adapter-based fine-tuning approach, and how can they be addressed in future work

One potential limitation of the adapter-based fine-tuning approach is the risk of overfitting to the adapter module itself, especially when the adapter is too specialized for the task at hand. This can lead to a decrease in generalization performance on unseen data. To address this limitation, regularization techniques such as dropout or weight decay can be applied to prevent overfitting. Additionally, incorporating diverse and representative training data can help the model learn more robust features through the adapter. Another limitation could be the complexity of designing and tuning the adapter architecture, which may require extensive experimentation and hyperparameter tuning. Future work could focus on automating this process through neural architecture search or reinforcement learning to optimize the adapter design efficiently.

Given the success of scaling up end-to-end training, what other video analysis tasks could benefit from this paradigm shift, and what are the key challenges that need to be overcome

Other video analysis tasks that could benefit from the paradigm shift of scaling up end-to-end training include video summarization, video captioning, and video anomaly detection. These tasks involve understanding and interpreting complex temporal relationships in videos, which can be enhanced by leveraging larger models and more extensive training data. However, key challenges that need to be overcome include the computational resources required for training and inference with larger models, the need for diverse and annotated datasets to prevent overfitting, and the interpretability of the models as they scale up. Addressing these challenges will be crucial in realizing the full potential of scaling up end-to-end training in various video analysis tasks.