TengdaHan / TemporalAlignNet

[CVPR'22 Oral] Temporal Alignment Networks for Long-term Video. Tengda Han, Weidi Xie, Andrew Zisserman.

Temporal Alignment Networks for long-term Video

Tengda Han, Weidi Xie, Andrew Zisserman. CVPR2022 Oral.

[project page] [PDF] [Arxiv] [Video]

News

• [23.08.30] 📣 📣 We released WhisperX ASR output and InternVideo & CLIP-L14 visual features for HowTo100M here.
• [22.09.14] Fixed a bug that affects the ROC-AUC calculation on HTM-Align dataset. Other metrics are not affected. Details
• [22.09.14] Fixed a few typos and some incorrect annotations in HTM-Align. This download link is up-to-date.
• [22.08.04] Released HTM-370K and HTM-1.2M here, the sentencified version of HowTo100M. Thank you for your patience, I'm working on the rest.

TLDR

• Natural instructional videos (e.g. from YouTube) has the visual-textual alignment problem, that introduces lots of noise and makes them hard to learn.
• Our model learns to predict:
  1. if the ASR sentence is alignable with the video,
  2. if yes, the most corresponding video timestamps.
• Our model is trained without human annotation, and can be used to clean-up the noisy instructional videos (as the output, we release an Auto-Aligned HTM dataset, HTM-AA).
• In our paper, we show the auto-aligned HTM dataset can improve the backbone visual representation quality comparing with original HTM.

Datasets (Check project page for details)

• HTM-Align: A manually annotated 80-video subset for alignment evaluation.
• HTM-AA: A large-scale video-text paired dataset automatically aligned using our TAN without using any manual annotations.
• Sentencified HTM: The original HTM dataset but the ASR is processed into full sentences.

Tool

• Sentencify-text: A pipeline to pre-process ASR text segments and get full sentences.

Training TAN

• See instructions in [train]

Using output of TAN for end-to-end training.

• See instructions in [end2end]

Checkpoints of TAN

• HTM370K-E6D6-Stage2

Reference

@InProceedings{han2022align,
  title={Temporal Alignment Network for long-term Video},  
  author={Tengda Han and Weidi Xie and Andrew Zisserman},  
  booktitle={CVPR},  
  year={2022}}