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README.md

Temporal Cycle-Consistency Learning (https://sites.google.com/view/temporal-cycle-consistency/home)

The codebase is useful for self-supervised representation learning on videos. It was used in the CVPR 2019 paper Temporal Cycle-Consistency Learning (https://arxiv.org/abs/1904.07846). Many functions will be useful for other sequential data too.

Self-supervised Learning Methods

Currently supported self-supervised algorithms include:

  • Temporal Cycle-Consistency (algos/alignment.py)
  • Shuffle and Learn (algos/sal.py)
  • Time-Contrastive Networks (algos/tcn.py)
  • Various combinations of the above three algorithms. (algos/alignment_sal_tcn.py)

We also have a supervised learning baseline that does per-frame classification (algos/classification.py).

Evaluation Tasks

A model that has been pre-trained with any of the above self-supervised/supervised losses can be used for a number of downstream fine-grained sequential/temporal understanding tasks.

We evaluated methods on 4 temporally fine-grained tasks. They are as follows:

  • Phase classification (evaluation/classfication.py)
  • Few-shot phase classification (evaluation/few_shot_classification.py)
  • Phase progression (evaluation/event_completion.py)
  • Kendall's Tau (evaluation/kendalls_tau.py)

Please refer to paper/code for definitions of these tasks.

To validate the representations, we do not fine-tune the trained models on any of these tasks in our paper. We extract embeddings and train SVMs on top of these embeddings. However, for practical purposes you might want to fine-tune the pre-trained model on your task. In that case, you might find evaluation/algo_loss.py useful as a skeleton that provides a loss on a given dataset. You just need to add an optimizer to minimize this downstream loss. Don't forget to switch set_learning_phase to 1 if you are fine-tuning.

Preparing Data

Depending on the source of data you can use different utilities to prepare TFRecords. The training scripts assume the data to be present in the format described in the decode function in datasets.py.

Videos

If you have unlabeled videos which you want to use for self-supervised representation learning, use dataset_preparation/videos_to_tfrecords.py to produce TFRecords.

Per-frame Labels for Video

If you have per-frame labels, you can run supervised learning (per-frame classification) or use the labels for evaluation tasks. To do so, you can use dataset_preparation/videos_to_tfrecords.py to produce TFRecords with labels for each frame.

Sets of images

If you have already extracted the frames of a video into images in a folder or want to run the algorithms between sequences of images you can run use dataset_preparation/images_to_tfrecords.py to produce TFRecords.

Per-frame Labels for Sets of Images

Use the fps parameter to assign a timestamp to each image in the set. Based on this timestamp the labels will be associated with each image in the set. In case these images are not from video, you can use an fps of 1 and timestamps at each second to label each image.

Training, Evaluation and Visualization

  • Please download the relevant data by running this script. dataset_preparation/download_pouring_data.sh

  • Dowload ImageNet pre-trained ResNetV2-50 to /tmp/. If you want to download the checkpoint to some other location change CONFIG.MODEL.RESNET_PRETRAINED_WEIGHTS in config.py. `wget -P /tmp/ https://github.com/keras-team/keras-applications/releases/download/resnet/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5

  • Set directory of the library. `root_dir=

  • Start training. python $root_dir/train --alsologtostderr

  • Start evaluation. python $root_dir/evaluate --alsologtostderr

  • Tensorboard. $tensorboard --logdir=/tmp/alignment_logs

  • Extract per-frame embeddings. python $root_dir/extract_embeddings --alsologtostderr --dataset <DATASET> \ --split <SPLIT> --logdir <LOGDIR>

  • Visualize nearest neighbor alignments. python $root_dir/visualize_alignment --alsologtostderr \ --video_path /tmp/aligned.mp4 --embs_path /tmp/embeddings.npy

Using alignment loss on your own embeddings

If you have your own dataset of sequences, embedder (neural network), and training code setup and only want to plugin our alignment loss on sequential embeddings consider using functions in the library in tcc/ folder.

To perform alignment of samples in a batch you can use the function compute_alignment_loss in tcc/alignment.py.

To align pairs of sequences together you can use the function align_pair_of_sequences in tcc/deterministic_alignment.py. This returns logits and labels. To calculate the loss function itself see how the logits and labels are used in function compute_deterministic_alignment_loss in tcc/deterministic_alignment.py.

Now you can go ahead and minimize the alignment loss by optimizing over the varibales of your embedder.

A cautionary note: please check if your embedder can encode the position of the frame (like LSTM/ positional embeddings in Transformer). If so, then there is a trivial solution to the TCC loss that just encodes the position of the frame in the embedding, not learning anything semantic. The loss will go down because the model learns to count but these embeddings might not be great for semantic tasks like phase classification. Consider adding ways by which your embedder finds it difficult to count.

Citation

If you found our paper/code useful in your research, please consider citing our paper:

author = {Dwibedi, Debidatta and Aytar, Yusuf and Tompson, Jonathan and Sermanet, Pierre and Zisserman, Andrew},
title = {Temporal Cycle-Consistency Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019},
}
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