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Foldover Features for Dynamic Object Behaviour Description in Microscopic Videos
IEEE Access, 2020 A behavior description helps analyze tiny objects, similar objects, objects with weak visual information, and objects with similar visual information. It plays a fundamental role in the identification and classification of dynamic objects in microscopic ...
Xialin Li +11 more
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IEEE Access, 2020 The main obstacle to image augmentation with Generative Adversarial Networks (GANs) is the need for a large amount of training data, but this is difficult for small datasets like Environmental Microorganisms (EMs). EM image analysis plays a vital role in
Hao Xu +9 more
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Learning Transferable Architectures for Scalable Image Recognition [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest.
Barret Zoph +3 more
semanticscholar +1 more source
InstructPix2Pix: Learning to Follow Image Editing Instructions [PDF]
Computer Vision and Pattern Recognition, 2022 We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image.
Tim Brooks +2 more
semanticscholar +1 more source
Nature Communications, 2023 Climate change is leading to more extreme weather hazards, forcing human populations to be displaced. We employ explainable machine learning techniques to model and understand internal displacement flows and patterns from observational data alone.
Michele Ronco +8 more
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Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks [PDF]
IEEE International Conference on Computer Vision, 2017 Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be
Jun-Yan Zhu +3 more
semanticscholar +1 more source
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning.
Peter Anderson +6 more
semanticscholar +1 more source
SwinIR: Image Restoration Using Swin Transformer [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images).
Jingyun Liang +5 more
semanticscholar +1 more source
Restormer: Efficient Transformer for High-Resolution Image Restoration [PDF]
Computer Vision and Pattern Recognition, 2021 Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
Syed Waqas Zamir +5 more
semanticscholar +1 more source
Multi-Scale Feature Channel Attention Generative Adversarial Network for Face Sketch Synthesis
IEEE Access, 2020 Face sketch synthesis for photos is an applied research topic and it is critical for criminal investigation. However, sketch synthesis remains some challenges because of the blur and artifacts in the generated face sketches. To mitigate these problems in
Jieying Zheng +4 more
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