mlsanityData to 7 Oct 2026

Regional Style and Color Transfer

arXiv 2404.13880 ↗First posted 22 Apr 2024cs.CV cs.AI cs.LGcited by 30

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Abstract (arXiv)

This paper presents a novel contribution to the field of regional style transfer. Existing methods often suffer from the drawback of applying style homogeneously across the entire image, leading to stylistic inconsistencies or foreground object twisted when applied to image with foreground elements such as person figures. To address this limitation, we propose a new approach that leverages a segmentation network to precisely isolate foreground objects within the input image. Subsequently, style transfer is applied exclusively to the background region. The isolated foreground objects are then carefully reintegrated into the style-transferred background. To enhance the visual coherence between foreground and background, a color transfer step is employed on the foreground elements prior to their rein-corporation. Finally, we utilize feathering techniques to achieve a seamless amalgamation of foreground and background, resulting in a visually unified and aesthetically pleasing final composition. Extensive evaluations demonstrate that our proposed approach yields significantly more natural stylistic transformations compared to conventional methods.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 22 of its 45 references that are in the map. First posted on arXiv 22 Apr 2024.

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22 of 45 references.

References · 22

  1. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  2. 2016Pyramid Scene Parsing Networkcited by 2,472
  3. 2023NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Spacecited by 88
  4. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  5. 2023PLGSLAM: Progressive Neural Scene Represenation with Local to Global Bundle Adjustmentcited by 62
  6. 2023Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networkscited by 54
  7. 2024Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an examplecited by 50
  8. 2024Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitatecited by 46
  9. 2018Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagerycited by 42
  10. 2023MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learningcited by 41
  11. 2023AutoReP: Automatic ReLU Replacement for Fast Private Network Inferencecited by 29
  12. 2023CPSeg: Finer-grained Image Semantic Segmentation via Chain-of-Thought Language Promptingcited by 28
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  2. 2023MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Trainingcited by 25
  3. 2019Confidence Trigger Detection: Accelerating Real-time Tracking-by-detection Systemscited by 20
  4. 2024Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenescited by 15
  5. 2024Few-shot Name Entity Recognition on StackOverflowcited by 14
  6. 2023Influence Pathway Discovery on Social Mediacited by 11
  7. 2023Segment Any Buildingcited by 8
  8. 2024Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Mediacited by 7
  9. 2019Joint Coverage and Power Control in Highly Dynamic and Massive UAV Networks: An Aggregative Game-theoretic Learning Approachcited by 4
  10. 2024Explainable AI for Fair Sepsis Mortality Predictive Modelcited by 4

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Citing papers per year: 2024: 26; 2025: 4.

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