TALLFormer: Temporal Action Localization with Long-memory Transformer

• URL: http://arxiv.org/abs/2204.01680v1
• Date: Mon, 4 Apr 2022 17:51:20 GMT
• Title: TALLFormer: Temporal Action Localization with Long-memory Transformer
• Authors: Feng Cheng, Gedas Bertasius
• Abstract summary: TALLFormer is a memory-efficient and end-to-end trainable temporal action localization transformer. Our long-term memory mechanism eliminates the need for processing hundreds of redundant video frames during each training iteration. With only RGB frames as input, TALLFormer outperforms previous state-of-the-art methods by a large margin.
• Score: 16.208160001820044
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most modern approaches in temporal action localization divide this problem into two parts: (i) short-term feature extraction and (ii) long-range temporal boundary localization. Due to the high GPU memory cost caused by processing long untrimmed videos, many methods sacrifice the representational power of the short-term feature extractor by either freezing the backbone or using a very small spatial video resolution. This issue becomes even worse with the recent video transformer models, many of which have quadratic memory complexity. To address these issues, we propose TALLFormer, a memory-efficient and end-to-end trainable Temporal Action Localization transformer with Long-term memory. Our long-term memory mechanism eliminates the need for processing hundreds of redundant video frames during each training iteration, thus, significantly reducing the GPU memory consumption and training time. These efficiency savings allow us (i) to use a powerful video transformer-based feature extractor without freezing the backbone or reducing the spatial video resolution, while (ii) also maintaining long-range temporal boundary localization capability. With only RGB frames as input and no external action recognition classifier, TALLFormer outperforms previous state-of-the-art methods by a large margin, achieving an average mAP of 59.1% on THUMOS14 and 35.6% on ActivityNet-1.3. The code will be available in https://github.com/klauscc/TALLFormer.

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