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Found 4 papers, 1 papers with code
PiTL: Cross-modal Retrieval with Weakly-supervised Vision-language Pre-training via Prompting
To further reduce the amount of supervision, we propose Prompts-in-The-Loop (PiTL) that prompts knowledge from large language models (LLMs) to describe images.
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision
Moreover, in other V-L downstream tasks considered, our WFH models are on par with models trained with paired V-L data, revealing the utility of unpaired data.
CLIP4IDC: CLIP for Image Difference Captioning
Different from directly fine-tuning CLIP to generate sentences, we introduce an adaptation training process to adapt CLIP's visual encoder to capture and align differences in image pairs based on the textual descriptions.
Tackling the Unannotated: Scene Graph Generation with Bias-Reduced Models
Recent scene graph generation (SGG) models have shown their capability of capturing the most frequent relations among visual entities.
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