Cascading Convolutional Temporal Colour Constancy

Computational Colour Constancy (CCC) consists of estimating the colour of one or more illuminants in a scene and using them to remove unwanted chromatic distortions. Much research has focused on illuminant estimation for CCC on single images, with few attempts of leveraging the temporal information intrinsic in sequences of correlated images (e.g., the frames in a video), a task known as Temporal Colour Constancy (TCC). The state-of-the-art for TCC is TCCNet, a deep-learning architecture that uses a ConvLSTM for aggregating the encodings produced by CNN submodules for each image in a sequence. We extend this architecture with different models obtained by (i) substituting the TCCNet submodules with C4, the state-of-the-art method for CCC targeting images; (ii) adding a cascading strategy to perform an iterative improvement of the estimate of the illuminant. We tested our models on the recently released TCC benchmark and achieved results that surpass the state-of-the-art. Analyzing the impact of the number of frames involved in illuminant estimation on performance, we show that it is possible to reduce inference time by training the models on few selected frames from the sequences while retaining comparable accuracy

Video-based crowd counting using a multi-scale optical flow pyramid network

This paper presents a novel approach to the task of video-based crowd counting, which can be formalized as the regression problem of learning a mapping from an input image to an output crowd density map. Convolutional neural networks (CNNs) have demonstrated striking accuracy gains in a range of computer vision tasks, including crowd counting. However, the dominant focus within the crowd counting literature has been on the single-frame case or applying CNNs to videos in a frame-by-frame fashion without leveraging motion information. This paper proposes a novel architecture that exploits the spatiotemporal information captured in a video stream by combining an optical flow pyramid with an appearance-based CNN. Extensive empirical evaluation on five public datasets comparing against numerous state-of-the-art approaches demonstrates the efficacy of the proposed architecture, with our methods reporting best results on all datasets. Finally, a set of transfer learning experiments shows that, once the proposed model is trained on one dataset, it can be transferred to another using a limited number of training examples and still exhibit high accurac

Developer-Friendly Segmentation using OpenVL, a High-Level Task-Based Abstraction

Research into computer vision techniques has far outpaced the development of interfaces (such as APIs) to support the techniques ’ accessibility, especially to developers who are not experts in the field. We present a new interface, specifically for segmentation methods, designed to be application-developer-friendly while retaining sufficient power and flexibility to solve a wide variety of problems. The interface presents segmentation at a higher level (above algorithms) and uses a task-based description derived from definitions of low-level segmentation. We show that through interpretation, the description can be used to invoke an appropriate method to provide the developer’s requested result. Our proof-of-concept implementation interprets the model description and invokes one of six segmentation methods with automatically derived parameters, which we demonstrate on a range of segmentation tasks. We also discuss how the concepts presented for segmentation may be extended to other computer vision problems. 1

Application of prime editing to the correction of mutations and phenotypes in adult mice with liver and eye diseases

© 2021, The Author(s), under exclusive licence to Springer Nature Limited.The use of prime editing—a gene-editing technique that induces small genetic changes without the need for donor DNA and without causing double strand breaks—to correct pathogenic mutations and phenotypes needs to be tested in animal models of human genetic diseases. Here we report the use of prime editors 2 and 3, delivered by hydrodynamic injection, in mice with the genetic liver disease hereditary tyrosinemia, and of prime editor 2, delivered by an adeno-associated virus vector, in mice with the genetic eye disease Leber congenital amaurosis. For each pathogenic mutation, we identified an optimal prime-editing guide RNA by using cells transduced with lentiviral libraries of guide-RNA-encoding sequences paired with the corresponding target sequences. The prime editors precisely corrected the disease-causing mutations and led to the amelioration of the disease phenotypes in the mice, without detectable off-target edits. Prime editing should be tested further in more animal models of genetic diseases.11Nsciescopu