Results 11 to 20 of about 1,012,747 (224)
KPConv: Flexible and Deformable Convolution for Point Clouds [PDF]
IEEE International Conference on Computer Vision, 2019 We present Kernel Point Convolution (KPConv), a new design of point convolution, i.e. that operates on point clouds without any intermediate representation. The convolution weights of KPConv are located in Euclidean space by kernel points, and applied to
Hugues Thomas +5 more
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ECO: Efficient Convolution Operators for Tracking [PDF]
Computer Vision and Pattern Recognition, 2016 In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever increasing tracking performance, their characteristic speed and real-time capability have
Martin Danelljan +3 more
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Incorporating Convolution Designs into Visual Transformers [PDF]
IEEE International Conference on Computer Vision, 2021 Motivated by the success of Transformers in natural language processing (NLP) tasks, there emerge some attempts (e.g., ViT and DeiT) to apply Transformers to the vision domain.
Kun Yuan +5 more
semanticscholar +1 more source
Free-Form Image Inpainting With Gated Convolution [PDF]
IEEE International Conference on Computer Vision, 2018 We present a generative image inpainting system to complete images with free-form mask and guidance. The system is based on gated convolutions learned from millions of images without additional labelling efforts. The proposed gated convolution solves the
Jiahui Yu +5 more
semanticscholar +1 more source
On the Integration of Self-Attention and Convolution [PDF]
Computer Vision and Pattern Recognition, 2021 Convolution and self-attention are two powerful techniques for representation learning, and they are usually considered as two peer approaches that are distinct from each other.
Xuran Pan +6 more
semanticscholar +1 more source
Generalized Quantum Convolution for Multidimensional Data
Entropy, 2023 The convolution operation plays a vital role in a wide range of critical algorithms across various domains, such as digital image processing, convolutional neural networks, and quantum machine learning.
Mingyoung Jeng +8 more
doaj +1 more source
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution [PDF]
Computer Vision and Pattern Recognition, 2020 Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature
Siyuan Qiao, Liang-Chieh Chen, A. Yuille
semanticscholar +1 more source
Diverse Branch Block: Building a Convolution as an Inception-like Unit [PDF]
Computer Vision and Pattern Recognition, 2021 We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block (DBB), which enhances the representational capacity of a single ...
Xiaohan Ding +3 more
semanticscholar +1 more source
Involution: Inverting the Inherence of Convolution for Visual Recognition [PDF]
Computer Vision and Pattern Recognition, 2021 Convolution has been the core ingredient of modern neural networks, triggering the surge of deep learning in vision. In this work, we rethink the inherent principles of standard convolution for vision tasks, specifically spatial-agnostic and channel ...
Duo Li +7 more
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Understanding Convolution for Semantic Segmentation [PDF]
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2017 Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over previous semantic segmentation systems.
Panqu Wang +6 more
semanticscholar +1 more source