Semi Supervised Learning for Image Captioning
2 papers with code • 3 benchmarks • 3 datasets
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Most implemented papers
Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image Captioning
The original algorithm relies on computationally expensive data augmentation steps that involve perturbing the raw images and computing features for each perturbed image.
Text-Only Training for Image Captioning using Noise-Injected CLIP
We consider the task of image-captioning using only the CLIP model and additional text data at training time, and no additional captioned images.