Raccoon Vision Translator

This is a video captioning agent for the Video Captioning track. Give it a clip and it writes four captions in four styles: formal, sarcastic, humorous_tech, and humorous_non_tech. All four come from the same source description, so the same real thing that happened in the clip gets described four different ways, not four different guesses. Before any of that, there's a small local step. ffmpeg pulls a handful of downscaled frames and the audio track, and a voice activity check decides if it's even worth running Whisper on that clip. A lot of video content is silent, so there's no reason to transcribe it every time. The real work happens through one model: Gemma 4 (31B), called via Hugging Face's Inference Providers. It looks at the sampled frames and writes a description of what's happening. Then it looks again, checks its own draft against the frames a second time, and fixes anything that was wrong or too vague. That gets cached once per clip, and all four styled captions are written from it. Each style also gets to see what the earlier styles already wrote, so they don't all collapse into the same sentence with different adjectives swapped in. Before anything gets written to results.json, each caption is checked against the word count rules the task requires, and it gets one retry if it fails that check. A bad caption shouldn't be able to take down the whole submission. One thing worth mentioning. An earlier version of this tried splitting the work across several different vision models feeding into each other. It didn't go well. The models disagreed with each other and the captions got less accurate, not more. Going back to one model doing both the looking and the writing turned out faster and a lot more reliable.