SOTAVerified

Image Manipulation

Image Manipulation is the process of altering or transforming an existing image to achieve a desired effect or to modify its content. This can involve various techniques and tools to enhance, modify, or create images based on specific requirements.

Papers

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Showing 126–150 of 427 papers

TitleStatusHype
Entity-Level Text-Guided Image ManipulationCode1
Information Bottleneck Disentanglement for Identity SwappingCode1
Learning Accurate Dense Correspondences and When to Trust ThemCode1
High Resolution Face Age EditingCode1
Attribute-specific Control Units in StyleGAN for Fine-grained Image ManipulationCode1
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image GenerationCode1
Content-Aware GAN CompressionCode1
MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and LocalizationCode1
High Fidelity Visualization of What Your Self-Supervised Representation Knows AboutCode1
Content Authentication for Neural Imaging Pipelines: End-to-end Optimization of Photo Provenance in Complex Distribution ChannelsCode1
Image Shape Manipulation from a Single Augmented Training SampleCode1
Exploiting Deep Generative Prior for Versatile Image Restoration and ManipulationCode1
Fake face detection via adaptive manipulation traces extraction networkCode1
High-Fidelity GAN Inversion for Image Attribute EditingCode1
Generalized Consistency Trajectory Models for Image ManipulationCode1
High-fidelity GAN Inversion with Padding SpaceCode1
HyperInverter: Improving StyleGAN Inversion via HypernetworkCode1
Analysing Statistical methods for Automatic Detection of Image ForgeryCode1
Learning to Manipulate Artistic ImagesCode1
Lifespan Age Transformation SynthesisCode1
ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation DetectionCode1
Learning JPEG Compression Artifacts for Image Manipulation Detection and LocalizationCode1
MaskGAN: Towards Diverse and Interactive Facial Image ManipulationCode1
SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing ObjectsCode1
What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network PerformanceCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pix2PixHD-SIALPIPS (S1)0.44—Unverified
2TPSLPIPS (S1)0.12—Unverified