Awesome

Pro_CCaps

Automatic image colourisation studies how to colourisegreyscale images. Existing approaches exploit convolu-tional layers that extract image-level features learning thecolourisation on the entire image, but miss entities-levelones due to pooling strategies. We believe that entity-levelfeatures are of paramount importance to deal with the in-trinsic multimodality of the problem (i.e., the same objectcan have different colours, and the same colour can havedifferent properties). Models based on capsule layers aimto identify entity-level features in the image from differentpoints of view, but they do not keep track of global features.Our network architecture integrates entity-level featuresinto the image-level features to generate a plausible im-age colourisation. We observed that results obtained withdirect integration of such two representations are largelydominated by the image-level features, thus resulting inunsaturated colours for the entities. To limit such an is-sue, we propose a gradual growth of the reconstructionphase of the model while training.By advantaging ofprior knowledge from each growing step, we obtain a sta-ble collaboration between image-level and entity-level fea-tures that ultimately generates stable and vibrant colouri-sations. Experimental results on three benchmark datasets,and a user study, demonstrate that our approach has com-petitive performance with respect to the state-of-the-art andprovides more consistent colourisation.

Architecture

Results

Usage

TODO: upload the trained model online

# train the model
python main.py

# reproduce published results
python Generate_Validation_Results.py

Paper published at WACV2021

https://openaccess.thecvf.com/content/WACV2022/html/Pucci_Pro-CCaps_Progressively_Teaching_Colourisation_to_Capsules_WACV_2022_paper.html

Please if you use this repository for you research, consider the possibility citing me:
@inproceedings{pucci2022pro, title={Pro-CCaps: Progressively Teaching Colourisation to Capsules}, author={Pucci, Rita and Micheloni, Christian and Foresti, Gian Luca and Martinel, Niki}, booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, pages={2271--2279}, year={2022} }