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Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models [PDF]
Computer Vision and Pattern Recognition, 2023 We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation. Text-to-image diffusion models have the remarkable ability
Jiarui Xu +5 more
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U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications [PDF]
IEEE Access, 2020 U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for ...
N. Siddique +3 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
Joint Learning of Intrinsic Images and Semantic Segmentation [PDF]
, 2018 Semantic segmentation of outdoor scenes is problematic when there are variations in imaging conditions. It is known that albedo (reflectance) is invariant to all kinds of illumination effects. Thus, using reflectance images for semantic segmentation task
A Garcia-Garcia +13 more
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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
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
Estimation of Depth Information of Image Based on Energy Distribution
Chemical Engineering Transactions, 2015 Image segmentation is an effective way to analysis image, which has received more and more attention. The traditional image segmentation technology can't get the result of the semantic image segmentation.
J.Y. Li, Q.B. Yuan
doaj +1 more source
Binocular Image Segmentation Based on Graph Cuts Multi-feature Selection [PDF]
Jisuanji kexue, 2021 Binocular image segmentation is crucial for subsequent applications such as stereoscopic object synthesis and 3D reconstruction.Since binocular images contain scene depth information,it is difficult to obtain ideal segmentation results by applying ...
JIN Hai-yan, PENG Jing, ZHOU Ting, XIAO Zhao-lin
doaj +1 more source
IEEE Access, 2021 Image segmentation is a key technology in the field of computer image processing. Among them, segmentation methods based on active contour models have been developed rapidly in recent years due to their effective processing of complex images such as ...
Xinghuo Ye, Qianyi Wang
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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