Temporal Modeling
Learning patterns across time - understanding how things change frame-to-frame or over longer sequences.
Think of it like
Reading a book page-by-page, understanding not just the words on one page but the story across chapters.
Example
In video: a persons hand position at frame 5 predicts frame 6. In trajectories: car position at time t predicts time t+1.
How it actually works
RNNs/LSTMs for sequential processing. Temporal convolutions (dilated, 1D) for parallel processing. Transformers for long-range dependencies (self-attention across time). 3D convolutions (C3D, I3D) combine spatial-temporal. Optical flow captures explicit motion. Challenges: long sequences (slow, memory), long-term dependencies (vanishing gradients), diverse temporal scales (slow burn to fast action). Contrastive learning (temporal augmentation) improves models.
For product teams
Video understanding, prediction, forecasting, anomaly detection.
For engineers
Temporal convolution, recurrence, or attention. Positional encoding for time. Metric: depends on task (action classification, prediction accuracy).
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