Scaling End-to-End Temporal Action Localization with Long-Short-range Adapters

Основні поняття

LoSA, a memory-and-parameter-efficient backbone adapter, enables end-to-end training of large video foundation models for improved temporal action localization in untrimmed videos.

Анотація

The paper introduces LoSA, a novel method for temporal action localization (TAL) that aims to overcome the limitations of existing approaches in scaling end-to-end training of large video foundation models.

Key highlights:

• TAL involves localizing and classifying action snippets in untrimmed videos. Existing methods are limited to head-only transfer learning due to the prohibitively large GPU memory required for end-to-end backbone adaptation.
• LoSA comprises a series of Long-range and Short-range Adapters that are attached to the intermediate layers of the video backbone. These adapters run parallel to the backbone, enabling untrimmed temporal learning at each layer without the need for gradient backpropagation through the backbone.
• LoSA also introduces a Long-Short-range Fusion module to strategically combine the outputs of the adapters with the last layer features, generating TAL-enhanced features for the TAL head.
• LoSA's unique design makes it both memory-and-parameter efficient, allowing it to scale end-to-end backbone adaptation to billion-parameter video models like VideoMAEv2 (ViT-g).
• Experiments on THUMOS-14 and ActivityNet-v1.3 show that LoSA significantly outperforms all existing TAL methods, including those using both RGB and optical flow features, as well as previous parameter-efficient transfer learning approaches.

Статистика

VideoMAEv2 (ViT-g) has over 1 billion parameters.
LoSA requires 40.6 GB of peak GPU memory for training on VideoMAEv2 (ViT-g) with a batch size of 1, while full backbone adaptation and PETL methods cause GPU out of memory error.
LoSA achieves 71.0% average mAP on THUMOS-14, outperforming head-only transfer learning by 1.4%.
LoSA achieves 38.6% average mAP on ActivityNet-v1.3, outperforming head-only transfer learning by 1.5%.

Цитати

"LoSA significantly outperforms all existing TAL methods, including those that use both RGB and optical flow features and those that attempt backbone adaptation, thereby establishing a new SOTA on both THUMOS-14 and ActivityNet-v1.3."
"LoSA's unique adapter design enables temporal video understanding over the full untrimmed video at each intermediate layer of the video backbone during end-to-end training, which is unlike any existing end-to-end TAL method."

Ключові висновки, отримані з

LoSA

by Akshita Gupt... о arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.01282.pdf

Глибші Запити

How can LoSA's adapter design be extended to other video understanding tasks beyond temporal action localization

LoSA's adapter design can be extended to other video understanding tasks beyond temporal action localization by adapting the Long-Short-range Adapters to cater to the specific requirements of those tasks. For instance, in video object detection, the adapters can be modified to focus on capturing spatial features at different scales and resolutions. In video segmentation, the adapters can be designed to incorporate temporal context for accurate segmentation of objects over time. By customizing the Long-Short-range Adapters to suit the needs of different video understanding tasks, LoSA can be applied effectively in a wide range of applications.

What are the potential limitations of LoSA's approach, and how could it be further improved to handle more complex video scenarios

One potential limitation of LoSA's approach could be the complexity of handling extremely long videos or videos with multiple complex actions occurring simultaneously. To address this, LoSA could be further improved by incorporating hierarchical adapters that can capture both high-level and low-level temporal features. Additionally, integrating attention mechanisms that dynamically adjust the focus on different parts of the video based on the context could enhance the adaptability of LoSA to handle more complex video scenarios. Furthermore, exploring the use of reinforcement learning to optimize the adapter design based on specific video characteristics could also improve the performance of LoSA in challenging video understanding tasks.

Given LoSA's ability to effectively leverage large video foundation models, how could it be applied to enable other video-centric applications, such as video retrieval or video summarization

Given LoSA's ability to effectively leverage large video foundation models, it could be applied to enable other video-centric applications such as video retrieval or video summarization by adapting the adapter design to focus on extracting key features for these tasks. For video retrieval, the Long-Short-range Adapters can be tailored to capture semantic information and context across different frames to improve the accuracy of video retrieval systems. In video summarization, the adapters can be optimized to identify important segments of the video and generate concise summaries based on the extracted features. By customizing LoSA for these applications, it can enhance the efficiency and effectiveness of video retrieval and summarization processes.