A small Transformer trained with a cosine-based triplet loss learns 16-dimensional embeddings that retrieve similar short driving trajectories from Argoverse 2 substantially better than FFT-based triplet training.
Words in motion: Extracting interpretable control vectors for motion transformers,
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Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories
A small Transformer trained with a cosine-based triplet loss learns 16-dimensional embeddings that retrieve similar short driving trajectories from Argoverse 2 substantially better than FFT-based triplet training.