Papers › Few-Shot Temporal Action Localization with Query Adaptive Transformer

Few-Shot Temporal Action Localization with Query Adaptive Transformer

20 Oct 2021arXiv:2110.10552archive 2025-07-28

Sauradip Nag, Xiatian Zhu, Tao Xiang

Existing temporal action localization (TAL) works rely on a large number of training videos with exhaustive segment-level annotation, preventing them from scaling to new classes. As a solution to this problem, few-shot TAL (FS-TAL) aims to adapt a model to a new class represented by as few as a single video. Exiting FS-TAL methods assume trimmed training videos for new classes. However, this setting is not only unnatural actions are typically captured in untrimmed videos, but also ignores background video segments containing vital contextual cues for foreground action segmentation. In this work, we first propose a new FS-TAL setting by proposing to use untrimmed training videos. Further, a novel FS-TAL model is proposed which maximizes the knowledge transfer from training classes whilst enabling the model to be dynamically adapted to both the new class and each video of that class simultaneously. This is achieved by introducing a query adaptive Transformer in the model. Extensive experiments on two action localization benchmarks demonstrate that our method can outperform all the state of the art alternatives significantly in both single-domain and cross-domain scenarios. The source code can be found in https://github.com/sauradip/fewshotQAT

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Code

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Tasks

Action Localization Action Segmentation Few Shot Temporal Action Localization Fine-Grained Action Detection Temporal Action Localization Temporal Localization Transfer Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Few Shot Temporal Action Localization	ActivityNet	FS-QAT	mIoU	38.5	#1 of 1	Archive leaderboard	report
Few Shot Temporal Action Localization	THUMOS14	FS-QAT	mIoU	30.2	#1 of 1	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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