  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.
