Test-Time Zero-Shot Temporal Action Localization

Benedetta Liberatori, Alessandro Conti, Paolo Rota, Yiming Wang, Elisa Ricci

Abstract: Zero-Shot Temporal Action Localization (ZS-TAL) seeks to identify and locate actions in untrimmed videos unseen during training. Existing ZS-TAL methods involve fine- tuning a model on a large amount of annotated training data. While effective, training-based ZS-TAL approaches as- sume the availability of labeled data for supervised learning, which can be impractical in some applications. Furthermore, the training process naturally induces a domain bias into the learned model, which may adversely affect the model’s gen- eralization ability to arbitrary videos. These considerations prompt us to approach the ZS-TAL problem from a radically novel perspective, relaxing the requirement for training data. To this aim, we introduce a novel method that performs Test- Time adaptation for Temporal Action Localization (T³AL). In a nutshell, T³AL adapts a pre-trained Vision and Lan- guage Model (VLM) at inference time on a sample basis. T³AL operates in three steps. First, a video-level pseudo- label of the action category is computed by aggregating information from the entire video. Then, action localization is performed adopting a novel procedure inspired by self- supervised learning. Finally, frame-level textual descriptions extracted with a state-of-the-art captioning model are em- ployed for refining the action region proposals. We validate the effectiveness of T³AL by conducting experiments on the THUMOS14 and the ActivityNet-v1.3 datasets. Our results demonstrate that T 3 AL significantly outperforms zero-shot baselines based on state-of-the-art VLMs, confirming the benefit of a test-time adaptation approach.

Setup

We recommend the use of a Linux machine with CUDA compatible GPUs. We provide a Conda environment to configure the required libraries.

Clone the repo with:

git clone ...
cd T3AL

Conda

The environment can be installed and activated with:

conda create --name t3al python=3.8
conda activate t3al
pip install -r requirements.txt

Preparing Datasets

We recommend to use pre-extracted CoCa features to accelerate inference. Please download the extracted features for THUMOS14 and ActivityNet datasets from links below:

Pre-extracted Features

Dataset	Link
THUMOS14	Google Drive
ActivityNet-v1.3	Google Drive

Then add the paths in the config files config/<dataset_name>.yaml, for example as:

training:
  feature_path: '/path/to/Thumos14/features/'
  video_path: '/path/to/Thumos14/videos/'

Evaluation

The method can be evaluated on the dataset of interest and selecting the split and setting, by running the following bash script:

python src/train.py experiment=tt_<dataset_name> data=<dataset_name> model.video_path=</path/to/data/> model.split=<split> model.setting=<setting> data.nsplit=0 exp_name=<exp_name>

We provide config files for the main method tt_<dataset_name>, the training free baseline tf_<dataset_name> and the baselines baseline.

Citation

Please consider citing our paper in your publications if the project helps your research.