This product is an open source software. If you would like to access the source code, please visit the below link. (You do not need to click "ADD TO CART" for this item)

<<To ACCESS Source Code: CLICK HERE>>

Authors: Ka Leong Cheng, Yueqi Xie, Qifeng Chen

Reversible image conversion (RIC) aims to build a reversible transformation between specific visual content (e.g., short videos) and an embedding image, where the original content can be restored from the embedding when necessary. This work develops Invertible Image Conversion Net (IICNet) as a generic solution to various RIC tasks due to its strong capacity and task-independent design. Unlike previous encoder-decoder based methods, IICNet maintains a highly invertible structure based on invertible neural networks (INNs) to better preserve the information during conversion.

We use a relation module and a channel squeeze layer to improve the INN nonlinearity to extract cross-image relations and the network flexibility, respectively. Experimental results demonstrate that IICNet outperforms the specifically-designed methods on existing RIC tasks and can generalize well to various newly-explored tasks. With our generic IICNet, we no longer need to hand-engineer task-specific embedding networks for rapidly occurring visual content.

Authors:
Department of Computer Science and Engineering Assistance Professor Prof. Qifeng Chen

Key Features:
IICNet yields state-of-the-art performance on some studied RIC tasks, such as spatial-temporal video embedding and mononizing binocular images. We also introduce and apply our IICNet on some unexplored tasks, which are embedding dual-view images and composition and decomposition. The success on the stenography task further shows the generalization of our IICNet. We hope the generalization and high performance of the proposed framework could help in more practical applications.

Specification:
Please refer to the published paper for specificaion

Category:
Source Code

Reference:
https://cqf.io/papers/IICNet_ICCV2021.pdf

Contact Us:
ttsamuel@ust.hk