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
Content-Aware GAN CompressionCode1
MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and LocalizationCode1
Robot Synesthesia: A Sound and Emotion Guided AI PainterCode1
Content Authentication for Neural Imaging Pipelines: End-to-end Optimization of Photo Provenance in Complex Distribution ChannelsCode1
Exploiting Deep Generative Prior for Versatile Image Restoration and ManipulationCode1
Harmfully Manipulated Images Matter in Multimodal Misinformation DetectionCode1
Generalized Consistency Trajectory Models for Image ManipulationCode1
S2FGAN: Semantically Aware Interactive Sketch-to-Face TranslationCode1
High-Fidelity GAN Inversion for Image Attribute EditingCode1
Wavelet-Driven Generalizable Framework for Deepfake Face Forgery DetectionCode1
FacialGAN: Style Transfer and Attribute Manipulation on Synthetic FacesCode1
Fake face detection via adaptive manipulation traces extraction networkCode1
Simple but Effective: CLIP Embeddings for Embodied AICode1
High Fidelity Visualization of What Your Self-Supervised Representation Knows AboutCode1
High Resolution Face Age EditingCode1
Self-Supervised Scene De-occlusionCode1
Analysing Statistical methods for Automatic Detection of Image ForgeryCode1
HyperInverter: Improving StyleGAN Inversion via HypernetworkCode1
SRFlow: Learning the Super-Resolution Space with Normalizing FlowCode1
Text as Neural Operator: Image Manipulation by Text InstructionCode1
CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image ManipulationCode1
SinGAN: Learning a Generative Model from a Single Natural ImageCode1
D2C: Diffusion-Decoding Models for Few-Shot Conditional GenerationCode1
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