Results 31 to 40 of about 6,662,539 (239)
OneFormer: One Transformer to Rule Universal Image Segmentation [PDF]
Computer Vision and Pattern Recognition, 2022 Universal Image Segmentation is not a new concept. Past attempts to unify image segmentation include scene parsing, panoptic segmentation, and, more recently, new panoptic architectures.
Jitesh Jain +5 more
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UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation [PDF]
IEEE Transactions on Medical Imaging, 2019 The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive ...
Zongwei Zhou +3 more
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MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation [PDF]
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023 There has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achieving performances equivalent to those in natural images challenging ...
Saikat Roy +7 more
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Environment Conservation Journal, 2020 Image segmentation is an essential and critical step in huge number of applications of image processing. Accuracy of image segmentation influence retrieved information for further processing in classification and other task.
Neeraj Kumari +3 more
doaj +1 more source
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by ...
Vijay Badrinarayanan +2 more
semanticscholar +1 more source
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model [PDF]
International Conference on Medical Imaging with Deep Learning, 2022 Diffusion probabilistic model (DPM) recently becomes one of the hottest topic in computer vision. Its image generation application such as Imagen, Latent Diffusion Models and Stable Diffusion have shown impressive generation capabilities, which aroused ...
Junde Wu +4 more
semanticscholar +1 more source
Image Segmentation Using Text and Image Prompts [PDF]
Computer Vision and Pattern Recognition, 2021 Image segmentation is usually addressed by training a model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive as it requires re-training the model on a dataset that encompasses these ...
Timo Lüddecke, Alexander S. Ecker
semanticscholar +1 more source
CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation [PDF]
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021 Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation. The convolutional operations used in these networks, however, inevitably have limitations in modeling the long-range dependency due to their ...
Yutong Xie +3 more
semanticscholar +1 more source
CRIS: CLIP-Driven Referring Image Segmentation [PDF]
Computer Vision and Pattern Recognition, 2021 Referring image segmentation aims to segment a referent via a natural linguistic expression. Due to the distinct data properties between text and image, it is challenging for a network to well align text and pixel-level features.
Zhaoqing Wang +6 more
semanticscholar +1 more source
MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer [PDF]
AAAI Conference on Artificial Intelligence, 2023 The Diffusion Probabilistic Model (DPM) has recently gained popularity in the field of computer vision, thanks to its image generation applications, such as Imagen, Latent Diffusion Models, and Stable Diffusion, which have demonstrated impressive ...
Junde Wu +4 more
semanticscholar +1 more source