CLIP4IDC is a image difference captioning model based on CLIP (ViT-B). The model achieve competitive results on CLEVR-Change, Spot-the-Diff and Image Editing Request. For CLEVR-Change. The official data can be found here: google drive link provided by Robust Change Captioning (ICCV19). Extracting this file will create data directory.

2022年6月1日 · Due to the excellent zero-shot performance of the recently proposed CLIP, we thus propose CLIP4IDC to transfer a CLIP model for the IDC task to address those issues. 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 ...

3 天之前 · Due to the excellent zero-shot performance of the recently proposed CLIP, we thus propose CLIP4IDC to transfer a CLIP model for the IDC task to address those issues. 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 ...

Experiments on three IDC bench-mark datasets, CLEVR-Change, Spot-the-Diff, and Image-Editing-Request, demonstrate the effectiveness of CLIP4IDC. Tasks involving understanding and expressing vi-sual contents are hard for machines because mod-elling relationships between the visual and tex-tual domains requires sophisticated computational reasoning.

2022年6月1日 · We thus propose CLIP4IDC to transfer a CLIP model for the IDC task to attain these improvements. Different from directly fine-tuning CLIP to generate sentences, a task-specific domain...

Image Difference Captioning (IDC) aims at generating sentences to describe differences between two similar-looking images. Conventional approaches learn an IDC model with a pre-trained and usually frozen visual feature extractor. Accordingly, two

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2022年6月1日 · Due to the excellent zero-shot performance of the recently proposed CLIP, we thus propose CLIP4IDC to transfer a CLIP model for the IDC task to address those issues. 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 ...

propose CLIP4IDC to transfer a CLIP model for the IDC task to attain these improvements. Different from directly fine-tuning CLIP to generate sentences, a task-specific do-main adaptation is used to improve the extracted features. Specifically, the target is to train CLIP on raw pixels to re-late the image pairs to the described changes ...

Image Difference Captioning (IDC) aims at generating sentences to describe the differences between two similar-looking images. The conventional approaches learn...