Reconstructed Slides: From Mixture of Experts to Mixture of Agents with Super Fast Inference - Daniel Kim & Daria Soboleva

Source Video

From Mixture of Experts to Mixture of Agents with Super Fast Inference - Daniel Kim & Daria Soboleva

Method

This deck is reconstructed from the existing video frame captures by detecting likely slide regions with OpenCV, cropping/upscaling those regions, deduplicating similar crops, and OCRing the cropped slide images locally. It is a cleaner companion to the full-stage frame deck.

Reconstructed Slides

• Source frame: slide-001.jpg
• Crop: full [0, 0, 960, 540] score 160.14

• Source frame: slide-002.jpg
• Crop: full [0, 0, 960, 540] score 160.41

• Source frame: slide-003.jpg
• Crop: full [0, 0, 960, 540] score 176.17

• Source frame: slide-004.jpg
• Crop: full [0, 0, 960, 540] score 177.66

• Source frame: slide-005.jpg
• Crop: full [0, 0, 960, 540] score 166.59

• Source frame: slide-006.jpg
• Crop: full [0, 0, 960, 540] score 166.83

• Source frame: slide-007.jpg
• Crop: full [0, 0, 960, 540] score 166.36

• Source frame: slide-008.jpg
• Crop: full [0, 0, 960, 540] score 162.23

• Source frame: slide-009.jpg
• Crop: full [0, 0, 960, 540] score 176.19

• Source frame: slide-010.jpg
• Crop: full [0, 0, 960, 540] score 164.5

Dense Scene-Detected Slide Candidates

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