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Video Understanding Using Temporal Cycle-Consistency Learning

Thursday, August 8, 2019

Posted by Debidatta Dwibedi, Research Associate, Google ResearchpowerfuldeeplearningmodelsframeTemporal Cycle-Consistency Learning codebaseRepresentation Learning Using TCCcomputervisionResNetvideo 1video 2video 1NN₂video 2NN₂video 1NN₁video 1NN₁

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Using TCC, we learn embeddings with temporally fine-grained understanding of an action by aligning related videos.

What Does TCC Learn?videos from the Penn Action Dataset

Right: Input videos of people performing a squat exercise. The video on the top left is the reference. The other videos show nearest neighbor frames (in the TCC embedding space) from other videos of people doing squats. Left: The corresponding frame embeddings move as the action is performed.

Applications of TCC

Few-shot action phase classification
When few labeled videos are available for training, the few-shot scenario, TCC performs very well. In fact, TCC can classify the phases of different actions with as few as a single labeled video. In the next figure we compare to other supervised and self-supervised learning approaches in the few-shot setting. We find that supervised learning requires about 50 videos with each frame labeled to achieve the same accuracy that self-supervised methods achieve with just one fully labeled video.

Comparison of self-supervised and supervised learning for few-shot action phase classification.

Unsupervised video alignment
Aligning or synchronizing videos manually becomes prohibitively difficult as the number of videos increases. Using TCC, many videos can be aligned by selecting the nearest neighbor to each frame in a reference video, without the need for additional labels, as demonstrated in the figure below.

Results of unsupervised video alignment on videos of people pitching baseball using the distance between frames in the TCC space. The reference video used for alignment is shown in the upper left panel.

Label/modality transfer between videos
Just as TCC finds similar frames by using a nearest neighbor search in the embedding space, it can transfer metadata associated with any frame in one video to its matching frame in another video. This metadata can be in the form of temporal semantic labels or other modalities, such as sound or text. In the video below we show two examples where we can transfer the sound of liquid being poured into a cup from one video to another.

Per-frame Retrieval
With TCC, each frame in a video can be used as a query for retrieval of similar frames by looking up the nearest neighbors in the learned embedding space. The embeddings are powerful enough to differentiate between frames that look quite similar, such as frames just before or after the release of a bowling ball.

We can perform retrieval from videos on a per-frame basis, i.e., any frame can be used to look up similar frames in a large collection of videos. The retrieved nearest neighbors show that the model captures fine-grained differences in the scene.

ReleasecodebaseAcknowledgementsThis is joint work with Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. The authors would like to thank Alexandre Passos, Allen Lavoie, Anelia Angelova, Bryan Seybold, Priya Gupta, Relja Arandjelović, Sergio Guadarrama, Sourish Chaudhuri, and Vincent Vanhoucke for their help with this project. The videos used in this project come from the PennAction dataset. We thank the creators of PennAction for curating such an interesting dataset.