论文标题
在时间行动本地化任务中减轻任务差异问题的软地面策略
Soft-Landing Strategy for Alleviating the Task Discrepancy Problem in Temporal Action Localization Tasks
论文作者
论文摘要
时间动作定位（TAL）方法通常在冻结的摘要编码器的特征序列之上进行操作，该摘要通过修剪动作分类（TAC）任务预处理，从而导致任务差异问题。尽管现有的TAL方法可以通过借口任务或端到端的微调来训练编码器来减轻此问题，但它们通常需要过多的高内存和计算。在这项工作中，我们引入了软地面（SOLA）策略，这是一个有效而有效的框架，可以通过结合轻度重量神经网络（即Sola模块），在冷冻装置的顶部弥合预验证的编码器和下游任务之间的可传递性差距。我们还为Sola模块提出了一个无监督的培训计划；它以框架间的相似性匹配学习，将框架间隔用作其监督信号，从而消除了对时间注释的需求。对下游TAL任务的各种基准测试的实验评估表明，我们的方法可以通过出色的计算效率有效地减轻任务差异问题。
Temporal Action Localization (TAL) methods typically operate on top of feature sequences from a frozen snippet encoder that is pretrained with the Trimmed Action Classification (TAC) tasks, resulting in a task discrepancy problem. While existing TAL methods mitigate this issue either by retraining the encoder with a pretext task or by end-to-end fine-tuning, they commonly require an overload of high memory and computation. In this work, we introduce Soft-Landing (SoLa) strategy, an efficient yet effective framework to bridge the transferability gap between the pretrained encoder and the downstream tasks by incorporating a light-weight neural network, i.e., a SoLa module, on top of the frozen encoder. We also propose an unsupervised training scheme for the SoLa module; it learns with inter-frame Similarity Matching that uses the frame interval as its supervisory signal, eliminating the need for temporal annotations. Experimental evaluation on various benchmarks for downstream TAL tasks shows that our method effectively alleviates the task discrepancy problem with remarkable computational efficiency.
