Related references
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Alina Kloss et al.
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Manuel Arias Chao et al.
Summary: A novel hybrid framework is proposed to combine physics-based performance models with deep learning algorithms for prognostics of complex safety-critical systems, improving prediction horizon by 127% compared to purely data-driven approaches. Physics-based performance models are used to infer unobservable model parameters related to system health and combined with sensor readings as input to a deep neural network, demonstrating superior performance over traditional data-driven methods.
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Celso M. de Melo et al.
Summary: Deep learning has achieved success in various domains, but the requirement for large amounts of labeled data presents a major bottleneck. Synthetic data is emerging as a potential solution, aided by advances in rendering pipelines, generative adversarial models, and fusion models. Domain adaptation techniques are also closing the statistical gap between synthetic and real data. The use of synthetic data and deep neural networks provides insights into the cognitive and neural functioning of biological systems.
TRENDS IN COGNITIVE SCIENCES
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A. Tewari et al.
Summary: Synthesizing photo-realistic images and videos is a key focus in computer graphics research. Neural rendering combines classical computer graphics techniques with machine learning to create algorithms for synthesizing images from real-world observations. This field has seen significant progress in recent years, with methods that can handle static scenes as well as non-rigidly deforming objects, scene editing, and composition. These methods have the advantage of being 3D-consistent and can be used for generative tasks. This report provides a comprehensive overview of state-of-the-art neural rendering methods, fundamental concepts, and open challenges.
COMPUTER GRAPHICS FORUM
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Article
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Alexander Vilesov et al.
Summary: With the rise of non-contact vital sign sensing during the COVID-19 pandemic, remote heart-rate monitoring has become increasingly important. However, previous studies have shown that using cameras can lead to a performance loss for individuals with darker skin tones. In this paper, the authors propose a solution by analyzing light transport and introducing a fairer modality - radar - for multi-modal fusion. The results show improved performance and fairness compared to existing methods, and a dataset with a focus on skin tone representation is made publicly available.
ACM TRANSACTIONS ON GRAPHICS
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G. Cai et al.
Summary: Mathematical representation of object shape is crucial for solving inverse rendering problems. Explicit representations are efficient for differentiable rendering but have difficulty handling topology changes. Implicit representations offer better support for topology changes but are harder to use for physics-based differentiable rendering. We introduce a new physics-based inverse rendering pipeline that utilizes both implicit and explicit representations. Our technique combines the benefits of both representations by supporting topology changes and differentiable rendering of complex effects.
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Isabella Huang et al.
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IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Tzofi Klinghoffer et al.
Summary: This paper presents a framework to understand the building blocks of the emerging field of end-to-end design of camera hardware and algorithms, highlighting the transformation from physics-driven to data-driven and task-specific camera design. It emphasizes the prevalence of methods that combine both physics and data in imaging and computer vision.
2022 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL PHOTOGRAPHY (ICCP)
(2022)
Proceedings Paper
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Chris Rockwell et al.
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Computer Science, Artificial Intelligence
Yunhao Ba et al.
Summary: This research presents a large-scale dataset of real-world rainy and clean image pairs, and proposes a method to remove the degradations caused by rain streaks and accumulation. By collecting a real paired deraining dataset and using a robust deep neural network, the model outperforms existing deraining methods on real rainy images.
COMPUTER VISION, ECCV 2022, PT VII
(2022)
Proceedings Paper
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Zhen Wang et al.
Summary: Accelerated by telemedicine, advances in Remote Photoplethysmography (rPPG) are offering a feasible path for non-contact physiological measurement. However, limited datasets and lack of diversity in existing rPPG datasets result in accuracy disparities on different demographic groups. This paper proposes a biophysical learning method to generate physio-realistic synthetic rPPG videos and collects a diverse rPPG dataset to ensure healthcare equity.
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Boyuan Chen et al.
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NATURE COMPUTATIONAL SCIENCE
(2022)
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Computer Science, Artificial Intelligence
Bahram Jalali et al.
Summary: Physics has been successful in explaining nature using low-dimensional deterministic models, while artificial intelligence (AI) has achieved astonishing performance in domains like image classification and speech recognition through data-driven computational frameworks. However, AI's inconsistent predictions and computational complexity conflict with Moore's Law. This paper discusses how a symbiosis of physics and AI can overcome these challenges.
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(2022)
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Baigan Zhao et al.
Summary: This paper proposed a novel network for monocular VO problem that learns the latent subspace of optical flow and models sequential dynamics for motion estimation. By training the encoder separately in an unsupervised manner and using different network structures and training schemes, a more generalized and effective feature representation is achieved. Experiments on KITTI and Malaga datasets show that the LS-RCNN-VO model outperforms existing learning-based VO approaches.
Editorial Material
Multidisciplinary Sciences
Achuta Kadambi
Summary: Studying computer science can help ensure that medical devices are fair for all races and sexes.
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Jinshan Pan et al.
Summary: This study proposes an algorithm that addresses image restoration problems using generative models with adversarial learning, guided by physics models and trained in an end-to-end fashion for various low-level vision tasks, demonstrating superior performance compared to existing algorithms through extensive experiments.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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Yeying Jin et al.
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2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
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2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
(2021)
Proceedings Paper
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Yashraj Narang et al.
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2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
(2021)
Proceedings Paper
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Kai Zhang et al.
Summary: PhySG is an end-to-end inverse rendering pipeline that reconstructs geometry, materials, and illumination from images. It uses mixtures of spherical Gaussians and MLPs to represent specular BRDFs and geometry. The method is shown to work on scenes with challenging reflectance characteristics.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
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Proceedings Paper
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Zeyuan Chen et al.
Summary: Studies show that fine-tuning pre-trained models on synthetic data with real hazy images, combining multiple physical priors into a prior loss committee, significantly improves dehazing performance and achieves a new technological level in practical dehazing tasks.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
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Proceedings Paper
Computer Science, Artificial Intelligence
Feilong Zhang et al.
Summary: This paper proposes a method that combines model-based alternative projection and deep neural networks for phase retrieval, aiming to achieve interpretability and effectiveness. The iterative process of phase retrieval is unfolded into a feed-forward neural network, embedding the physical model into its structure. Additionally, a complex-valued U-Net is proposed for image priori definition in dual planes.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
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Luca Schweri et al.
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