Although many color constancy methods exist, they are all based on specific assumptions such as the set of possible light sources, or the spatial and spectral characteristics of images. As a consequence, no algorithm can be considered as universal. However, with the large variety of available methods, the question is how to select the method that induces equivalent classes for different image characteristics. Furthermore, the subsequent question is how to combine the different algorithms in a proper way. To achieve selection and combining of color constancy algorithms, in this paper, natural image statistics are used to identify the most important characteristics of color images. Then, based on these image characteristics, the proper color constancy algorithm (or best combination of algorithms) is selected for a specific image. To capture the image characteristics, the Weibull parameterization (e.g. texture and contrast) is used. Experiments show that, on a large data set of 11,000 images, our approach outperforms current state-of-the-art single algorithms, as well as simple alternatives for combining several algorithms.

Domain-generalized urban-scene semantic segmentation (USSS) aims to learn generalized semantic predictions across diverse urban-scene styles. Unlike generic domain gap challenges, USSS is unique in that the semantic categories are often similar in different urban scenes, while the styles can vary significantly due to changes in urban landscapes, weather conditions, lighting, and other factors. Existing approaches typically rely on convolutional neural networks (CNNs) to learn the content of urban scenes. In this paper, we propose a Content-enhanced Mask TransFormer (CMFormer) for domain-generalized USSS. The main idea is to enhance the focus of the fundamental component, the mask attention mechanism, in Transformer segmentation models on content information. We have observed through empirical analysis that a mask representation effectively captures pixel segments, albeit with reduced robustness to style variations. Conversely, its lower-resolution counterpart exhibits greater ability to accommodate style variations, while being less proficient in representing pixel segments. To harness the synergistic attributes of these two approaches, we introduce a novel content-enhanced mask attention mechanism. It learns mask queries from both the image feature and its down-sampled counterpart, aiming to simultaneously encapsulate the content and address stylistic variations. These features are fused into a Transformer decoder and integrated into a multi-resolution content-enhanced mask attention learning scheme. Extensive experiments conducted on various domain-generalized urban-scene segmentation datasets demonstrate that the proposed CMFormer significantly outperforms existing CNN-based methods by up to 14.0% mIoU and the contemporary HGFormer by up to 1.7% mIoU. The source code is publicly available at https://github.com/BiQiWHU/CMFormer.

Scene appearance changes drastically throughout the day. Existing semantic segmentation methods mainly focus on well-lit daytime scenarios and are not well designed to cope with such great appearance changes. Naively using domain adaption does not solve this problem because it usually learns a fixed mapping between the source and target domain and thus have limited generalization capability on all-day scenarios ( i . e ., from dawn to night). In this paper, in contrast to existing methods, we tackle this challenge from the perspective of image formulation itself, where the image appearance is determined by both intrinsic ( e . g ., semantic category, structure) and extrinsic ( e . g ., lighting) properties. To this end, we propose a novel intrinsic-extrinsic interactive learning strategy. The key idea is to interact between intrinsic and extrinsic representations during the learning process under spatial-wise guidance. In this way, the intrinsic representation becomes more stable and, at the same time, the extrinsic representation gets better at depicting the changes. Consequently, the refined image representation is more robust to generate pixel-wise predictions for all-day scenarios. To achieve this, we propose an All-in-One Segmentation Network (AO-SegNet) in an end-to-end manner. Large scale experiments are conducted on three real datasets (Mapillary, BDD100K and ACDC) and our proposed synthetic All-day CityScapes dataset. The proposed AO-SegNet shows a significant performance gain against the state-of-the-art under a variety of CNN and ViT backbones on all the datasets.

We present a novel architecture for manipulating facial expressions, head poses, and lighting conditions from a single monocular image. Recent methods based on Generative Adversarial Networks show promising results in expression manipulation. However, the variation is either defined by a limited number of classes or not well suitable for explicit manipulation of different attributes such as pose and lighting conditions. Besides, state-of-the-art methods are mostly focused on frontal faces. Therefore, in this paper, a new Generative Adversarial Network architecture is proposed by explicitly conditioning on the appearance image space which is the product of direct manipulation of facial expressions, light and pose conditions of the face model in 3D space. In addition, the method provides face annotations (i.e. facial units, landmarks, etc.) and only requires video sequences for training. Therefore, it is unsupervised. Unlike other face manipulation methods, the proposed method does not require target specific training. Large scale experiments show that our method outperforms state-of-the-art methods for different scenarios.