CRefNet: Learning Consistent Reflectance Estimation With a Decoder-Sharing Transformer.

We present CRefNet, a hybrid transformer-convolutional deep neural network for consistent reflectance estimation in intrinsic image decomposition. Estimating consistent reflectance is particularly challenging when the same material appears differently due to changes in illumination. Our method achieves enhanced global reflectance consistency via a novel transformer module that converts image features to reflectance features. At the same time, this module also exploits long-range data interactions. We introduce reflectance reconstruction as a novel auxiliary task that shares a common decoder with the reflectance estimation task, and which substantially improves the quality of reconstructed reflectance maps. Finally, we improve local reflectance consistency via a new rectified gradient filter that effectively suppresses small variations in predictions without any overhead at inference time. Our experiments show that our contributions enable CRefNet to predict highly consistent reflectance maps and to outperform the state of the art by 10% WHDR.

Panel-Page-Aware Comic Genre Understanding.

Using a sequence of discrete still images to tell a story or introduce a process has become a tradition in the field of digital visual media. With the surge in these media and the requirements in downstream tasks, acquiring their main topics or genres in a very short time is urgently needed. As a representative form of the media, comic enjoys a huge boom as it has gone digital. However, different from natural images, comic images are divided by panels, and the images are not visually consistent from page to page. Therefore, existing works tailored for natural images perform poorly in analyzing comics. Considering the identification of comic genres is tied to the overall story plotting, a long-term understanding that makes full use of the semantic interactions between multi-level comic fragments needs to be fully exploited. In this paper, we propose [Formula: see text]Comic, a Panel-Page-aware Comic genre classification model, which takes page sequences of comics as the input and produces class-wise probabilities. [Formula: see text]Comic utilizes detected panel boxes to extract panel representations and deploys self-attention to construct panel-page understanding, assisted with interdependent classifiers to model label correlation. We develop the first comic dataset for the task of comic genre classification with multi-genre labels. Our approach is proved by experiments to outperform state-of-the-art methods on related tasks. We also validate the extensibility of our network to perform in the multi-modal scenario. Finally, we show the practicability of our approach by giving effective genre prediction results for whole comic books.