UntrimmedNet for Action Recognition and Detection
We provide the code and models for our CVPR paper (Arxiv Preprint):
UntrimmedNets for Weakly Supervised Action Recognition and Detection Limin Wang, Yuanjun Xiong, Dahua Lin, and Luc Van Gool in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
- October 16th, 2018
- Release the learned models trained only on the train set of ActivityNet1.2 datasets. Note that our previously released ActivityNet models are trained on the train+val set.
- September 19th, 2017
- Release the learned models on the THUMOS14 and ActivityNet1.2 datasets.
- August 20th, 2017
- Release the model protos.
The training of UntrimmedNet is composed of three steps:
- Step 1: extract action proposals (or shot boundaries) for each untrimmed video. We provide a sample of detected shot boudary on the ActivityNet (v1.2) under the folders of
- Step 2: construct file lists for training and validation. There are two filelists: one containing file path, number of frames, and label; the other one containing the shot file path and number of frames (Examples are in the folder
- Step 3: train UntrimmedNets using our modified caffe: https://github.com/yjxiong/caffe/tree/untrimmednet
The testing of UntrimmedNet for action recognition is based on temporal sliding window and top-k pooling
The testing of UntrimmedNet for action detection is based on a simple baseline (see code in
You could download our trained models on the THUMOS14 and ActivityNet datasets by using the scripts of