| # LinkedIn Post Draft |
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| I built a small experiment around representation-first operational vision with I-JEPA. |
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| The idea is simple: |
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| YOLO gives precise object labels and boxes. |
| I-JEPA gives frozen visual representations that can be probed for scene structure, context, and approximate semantic similarity. |
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| So instead of treating I-JEPA as a detector, I used it as a representation layer: |
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| - YOLO boxes as benchmark labels |
| - I-JEPA patch saliency as a rough "where is visual structure strongest?" signal |
| - class prototypes from object-crop embeddings |
| - a tiny LogisticRegression head trained on frozen I-JEPA embeddings |
| - object/context/scene similarity to reason about whether something is isolated, embedded, or part of a group-like scene |
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| What I like about the tiny head: it can be only tens of thousands of trainable parameters, while the large I-JEPA model stays frozen. If that small layer can classify objects from embeddings, the representation is doing most of the heavy lifting. |
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| One interesting observation: rare classes such as manholes were weak with only a few prototype support samples, but became much more recognizable as support coverage increased. That is a nice reminder that representation quality and support coverage interact. |
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| This is not just about replacing YOLO box-for-box. It is a practical probe into representation-first vision: |
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| Can frozen self-supervised models help us understand both objects and the surrounding scene context, with only a tiny classifier on top? |
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| Repo / demo: |
| <add link here> |
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| #AI #ComputerVision #HuggingFace #SelfSupervisedLearning #JEPA #OperationalAI |
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