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24 changes: 23 additions & 1 deletion index.html
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Expand Up @@ -342,7 +342,29 @@ <h3>Real time hand gesture detection: from rock paper scissors to sign interpret
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<article class="project-card">
<div class="teaser" role="img" aria-label="Cooking action recognition">
<img src="assets/group_P.png" alt="" style="position:absolute; inset:0; width:100%; height:100%; object-fit:cover; z-index:2;">
<span class="teaser-label" style="z-index:3;">Group P</span>
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<div class="project-content">
<p class="project-meta">Action recognition, video understanding, self-supervised embeddings</p>
<h3>From Raw Footage to Recipe: Extracting Cooking Steps from Egocentric Video</h3>
<p class="project-abstract">
This project builds a system that watches egocentric cooking videos and automatically extracts the sequence of cooking actions performed, with the goal of reconstructing a recipe from raw footage alone.
Because most frames in a cooking video are irrelevant, the pipeline first applies a relevance classifier to filter out background activity, then routes the remaining clips through an RNN-based action classifier that identifies steps such as cutting, peeling, and boiling.
Video representations are produced by V-JEPA 2, which encodes each video as a sequence of 64-frame block embeddings without requiring labeled pretraining data.
The result is an end-to-end pipeline that turns an unstructured kitchen video into a structured, step-by-step recipe.
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<article class="project-card add-project-card">
<a href="https://github.com/Computer-Vision-2026/Computer-Vision-2026.github.io/edit/main/index.html" target="_blank" rel="noopener">
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