Papers Image-Variation
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Beyond Color and Lines: Zero-Shot Style-Specific Image Variations with Coordinated Semantics
Traditionally, style has been primarily considered in terms of artistic elements such as colors, brushstrokes, and lighting. However, identical semantic subjects, like people, boats, and houses, can vary significantly ac…
Image to textImage-VariationTowards Black-Box Membership Inference Attack for Diffusion Models
Given the rising popularity of AI-generated art and the associated copyright concerns, identifying whether an artwork was used to train a diffusion model is an important research topic. The work approaches this problem f…
Image-VariationInference AttackMembership Inference AttackConditional Diffusion on Web-Scale Image Pairs leads to Diverse Image Variations
Generating image variations, where a model produces variations of an input image while preserving the semantic context has gained increasing attention. Current image variation techniques involve adapting a text-to-image …
Image GenerationImage-VariationCreating Image Datasets in Agricultural Environments using DALL.E: Generative AI-Powered Large Language Model
This research investigated the role of artificial intelligence (AI), specifically the DALL.E model by OpenAI, in advancing data generation and visualization techniques in agriculture. DALL.E, an advanced AI image generat…
Decision MakingImage GenerationImage-VariationLanguage Modeling+2Diffusion Brush: A Latent Diffusion Model-based Editing Tool for AI-generated Images
Text-to-image generative models have made remarkable advancements in generating high-quality images. However, generated images often contain undesirable artifacts or other errors due to model limitations. Existing techni…
Image InpaintingImage-VariationReal-World Image Variation by Aligning Diffusion Inversion Chain
Recent diffusion model advancements have enabled high-fidelity images to be generated using text prompts. However, a domain gap exists between generated images and real-world images, which poses a challenge in generating…
Image GenerationImage-VariationSemantic SimilaritySemantic Textual Similarity+2Prompt-Free Diffusion: Taking "Text" out of Text-to-Image Diffusion Models
Text-to-image (T2I) research has grown explosively in the past year, owing to the large-scale pre-trained diffusion models and many emerging personalization and editing approaches. Yet, one pain point persists: the text …
Conditional Text-to-Image SynthesisImage GenerationImage-VariationPrompt Engineering+1The CLIP Model is Secretly an Image-to-Prompt Converter
The Stable Diffusion model is a prominent text-to-image generation model that relies on a text prompt as its input, which is encoded using the Contrastive Language-Image Pre-Training (CLIP). However, text prompts have li…
Image GenerationImage-VariationText to Image GenerationText-to-Image GenerationSCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings
Deep neural networks for image super-resolution (SR) have demonstrated superior performance. However, the large memory and computation consumption hinders their deployment on resource-constrained devices. Binary neural n…
Binarizationimage-classificationImage ClassificationImage Reconstruction+3Versatile Diffusion: Text, Images and Variations All in One Diffusion Model
Recent advances in diffusion models have set an impressive milestone in many generation tasks, and trending works such as DALL-E2, Imagen, and Stable Diffusion have attracted great interest. Despite the rapid landscape c…
AllDisentanglementImage CaptioningImage Generation+7Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset
This residual network has been a broad domain of research in deep learning. Many complex architectures are based upon residual networks. Residual networks are efficient due to skip connections. This paper highlights the…
ClassificationData AugmentationData VisualizationDetecting Image Manipulation+14AugStatic - A Light-Weight Image Augmentation Library
The rapid exponential increase in the data led to an abrupt mix of various data types, leading to a deficiency of helpful information. Creating new data with the existing different types of data are presented in this pap…
ClassificationData AugmentationData VisualizationDetecting Image Manipulation+13Augmented Balanced Image Dataset Generator Using AugStatic Library
The mixed data consists of various structured and unstructured data. The exponential boom of the amount of data has made the datasets of varying samples. This paper focuses on the image dataset generator that balances an…
ClassificationData AugmentationData VisualizationDetecting Image Manipulation+13Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation
The data in the real world consists of various kinds of painful features. A majorly found one is the class imbalance in which the number of examples in different classes in a dataset is unequal. The class imbalance is be…
ClassificationData AugmentationData VisualizationDetecting Image Manipulation+13Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset
although best-in-class AI can deliver extraordinary outcomes in experimentation, data scientists struggle to duplicate these outcomes on actual-world data. It's nothing unexpected-actual data mirrors the messy world that…
ClassificationData AugmentationData VisualizationDetecting Image Manipulation+13Deep PCB To COCO Convertor
Millions of datasets and many models use the input datasets in COCO format. In this paper, we are converting the Deep PCB dataset to COCO format. The Deep PCB is a manufacturing defect data set. It has 1500 image pairs. …
ClassificationData AugmentationData VisualizationDetecting Image Manipulation+15The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled Video
The ability to reliably estimate physiological signals from video is a powerful tool in low-cost, pre-clinical health monitoring. In this work we propose a new approach to remote photoplethysmography (rPPG) - the measure…
Contrastive LearningImage-VariationThe Geometry of Deep Generative Image Models and its Applications
Generative adversarial networks (GANs) have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets, such as natural images. These networks are trained to map random inputs in …
Image-VariationA Geometric Analysis of Deep Generative Image Models and Its Applications
Generative adversarial networks have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets, such as natural images. These networks are trained to map random inputs in their l…
Image-VariationMulti-Kernel Filtering for Nonstationary Noise: An Extension of Bilateral Filtering Using Image Context
Bilateral filtering (BF) is one of the most classical denoising filters, however, the manually initialized filtering kernel hampers its adaptivity across images with various characteristics. To deal with image variation …
ClusteringDenoisingImage-Variation