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+ This project explores how pretrained image-captioning models can be adapted to produce short action-focused captions for video activity timelines. Instead of generating long descriptive captions, we fine-tune BLIP, ViT-GPT2, and Microsoft GIT on COCO action captions so that the models output compact labels such as “person walking” or “coffee being poured.”
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+ For video inference, frames are sampled over time, captioned by the fine-tuned models, and de-duplicated into a simple activity timeline. The project compares original and fine-tuned models using BLEU-1, BLEU-2, METEOR, and ROUGE-L, and analyzes whether architecture choice still matters after all models are adapted to the same action-caption task.
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