In Defense of the Triplet Loss for Person Re-Identification

Why the Triplet Loss Still Wins for Person Re-identification

A plain idea that many had wrote off is proving stronger than expected.
Researchers found that a clear setup, where a model learns a space so similar faces stay close, using a variant of the triplet loss, can beat more complex tricks.
This is about finding the same person in different photos — the problem called person re-identification — and it matters for safety, search, and helping cameras match people across scenes.
Instead of breaking the job into steps and adding extra layers later, training all at once — end-to-end — helps the model learn better signals.
They tried both models trained from scratch and ones with prior training, and surprise: the triplet way often outperforms many published methods by a wide margin.
So the simple path still works; sometimes the fancy add-ons don’t buy you much, and this could change how people build these systems going forward.
It also means less fuss to train and could make tools faster for everyday use, which seems small but its impact is real.

Read article comprehensive review in Paperium.net:
In Defense of the Triplet Loss for Person Re-Identification

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