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Found 25 papers, 11 papers with code
PromptMap: An Alternative Interaction Style for AI-Based Image Generation
We found that PromptMap supported users in crafting prompts by providing them with examples.
Spherical Dense Text-to-Image Synthesis
Specifically, we propose MultiStitchDiffusion (MSTD) and MultiPanFusion (MPF) by integrating MultiDiffusion into StitchDiffusion and PanFusion, respectively.
Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution
Satellite imagery is a cornerstone for numerous Remote Sensing (RS) applications; however, limited spatial resolution frequently hinders the precision of such systems, especially in multi-label scene classification tasks as it requires a higher level of detail and feature differentiation.
TKG-DM: Training-free Chroma Key Content Generation Diffusion Model
To address this limitation, we present a novel Training-Free Chroma Key Content Generation Diffusion Model (TKG-DM), which optimizes the initial random noise to produce images with foreground objects on a specifiable color background.
Just Leaf It: Accelerating Diffusion Classifiers with Hierarchical Class Pruning
Diffusion models, known for their generative capabilities, have recently shown unexpected potential in image classification tasks by using Bayes' theorem.
Distill the Best, Ignore the Rest: Improving Dataset Distillation with Loss-Value-Based Pruning
Dataset distillation has gained significant interest in recent years, yet existing approaches typically distill from the entire dataset, potentially including non-beneficial samples.
Zoomed In, Diffused Out: Towards Local Degradation-Aware Multi-Diffusion for Extreme Image Super-Resolution
Large-scale, pre-trained Text-to-Image (T2I) diffusion models have gained significant popularity in image generation tasks and have shown unexpected potential in image Super-Resolution (SR).
A Low-Resolution Image is Worth 1x1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift
Transformer-based Super-Resolution (SR) models have recently advanced image reconstruction quality, yet challenges remain due to computational complexity and an over-reliance on large patch sizes, which constrain fine-grained detail enhancement.
SpotDiffusion: A Fast Approach For Seamless Panorama Generation Over Time
Generating high-resolution images with generative models has recently been made widely accessible by leveraging diffusion models pre-trained on large-scale datasets.
Federated Learning for Blind Image Super-Resolution
This distinction is crucial as it circumvents the need to precisely model real-world degradations, which limits contemporary blind image SR research.
ObjBlur: A Curriculum Learning Approach With Progressive Object-Level Blurring for Improved Layout-to-Image Generation
We present ObjBlur, a novel curriculum learning approach to improve layout-to-image generation models, where the task is to produce realistic images from layouts composed of boxes and labels.
Latent Dataset Distillation with Diffusion Models
To address both challenges, this paper proposes Latent Dataset Distillation with Diffusion Models (LD3M) that combine diffusion in latent space with dataset distillation.
Diffusion Models, Image Super-Resolution And Everything: A Survey
Diffusion Models (DMs) have disrupted the image Super-Resolution (SR) field and further closed the gap between image quality and human perceptual preferences.
Dynamic Attention-Guided Diffusion for Image Super-Resolution
To address this, we propose ``You Only Diffuse Areas'' (YODA), a dynamic attention-guided diffusion process for image SR. YODA selectively focuses on spatial regions defined by attention maps derived from the low-resolution images and the current denoising time step.
DWA: Differential Wavelet Amplifier for Image Super-Resolution
This work introduces Differential Wavelet Amplifier (DWA), a drop-in module for wavelet-based image Super-Resolution (SR).
Are Visual Recognition Models Robust to Image Compression?
For example, for segmentation mIoU is reduced from 44. 5 to 30. 5 mIoU when compressing to 0. 1 bpp using the best compression model we evaluated.
Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolution
This paper presents a novel Diffusion-Wavelet (DiWa) approach for Single-Image Super-Resolution (SISR).
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances
With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving research area.
DT2I: Dense Text-to-Image Generation from Region Descriptions
Our results demonstrate the capability of our approach to generate plausible images of complex scenes using region captions.
Combining Transformer Generators with Convolutional Discriminators
Transformer models have recently attracted much interest from computer vision researchers and have since been successfully employed for several problems traditionally addressed with convolutional neural networks.
AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style
In this paper, we propose a method for attribute controlled image synthesis from layout which allows to specify the appearance of individual objects without affecting the rest of the image.
Adversarial Text-to-Image Synthesis: A Review
With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area.
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis
We create additional training samples by concatenating question and answer (QA) pairs and employ a standard VQA model to provide the T2I model with an auxiliary learning signal.
Hybrid-S2S: Video Object Segmentation with Recurrent Networks and Correspondence Matching
In this work, we study an RNN-based architecture and address some of these issues by proposing a hybrid sequence-to-sequence architecture named HS2S, utilizing a dual mask propagation strategy that allows incorporating the information obtained from correspondence matching.
