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Found 46 papers, 25 papers with code
Omegance: A Single Parameter for Various Granularities in Diffusion-Based Synthesis
In this work, we introduce a single parameter $\omega$, to effectively control granularity in diffusion-based synthesis.
Inherently Interpretable Tree Ensemble Learning
Tree ensemble models like random forests and gradient boosting machines are widely used in machine learning due to their excellent predictive performance.
Combining Generative and Geometry Priors for Wide-Angle Portrait Correction
In this work, we propose encapsulating the generative face prior as a guided natural manifold to facilitate the correction of facial regions.
AITTI: Learning Adaptive Inclusive Token for Text-to-Image Generation
Specifically, the core of our method is a lightweight adaptive mapping network, which can customize the inclusive tokens for the concepts to be de-biased, making the tokens generalizable to unseen concepts regardless of their original bias distributions.
MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems.
MIPI 2024 Challenge on Demosaic for HybridEVS Camera: Methods and Results
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems.
MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems.
When StyleGAN Meets Stable Diffusion: a W+ Adapter for Personalized Image Generation
Text-to-image diffusion models have remarkably excelled in producing diverse high-quality and photo-realistic images.
When StyleGAN Meets Stable Diffusion: a $\mathscr{W}_+$ Adapter for Personalized Image Generation
Text-to-image diffusion models have remarkably excelled in producing diverse, high-quality, and photo-realistic images.
XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting with High Volatility
In the realm of Mobility-on-Demand (MoD) systems, the forecasting of rider demand is a cornerstone for operational decision-making and system optimization.
Tree-GPT: Modular Large Language Model Expert System for Forest Remote Sensing Image Understanding and Interactive Analysis
This empowers LLMs with the ability to comprehend images, acquire accurate knowledge, generate code, and perform data analysis in a local environment.
Survey on Deep Face Restoration: From Non-blind to Blind and Beyond
We also discuss notable benchmarks commonly utilized in the field.
MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces
To evaluate our proposed MetaF2N, we have collected a real-world low-quality dataset with one or multiple faces in each image, and our MetaF2N achieves superior performance on both synthetic and real-world datasets.
Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models
In this work, we introduce a novel Referring Diffusional segmentor (Ref-Diff) for this task, which leverages the fine-grained multi-modal information from generative models.
VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization
In this paper, we propose a VQGAN-based framework (i. e., VQ-Font) to enhance glyph fidelity through token prior refinement and structure-aware enhancement.
Learning Generative Structure Prior for Blind Text Image Super-resolution
To restrict the generative space of StyleGAN so that it obeys the structure of characters yet remains flexible in handling different font styles, we store the discrete features for each character in a codebook.
NUWA-LIP: Language-Guided Image Inpainting With Defect-Free VQGAN
Language-guided image inpainting aims to fill the defective regions of an image under the guidance of text while keeping the non-defective regions unchanged.
Learning Dual Memory Dictionaries for Blind Face Restoration
Generally, it is a challenging and intractable task to improve the photo-realistic performance of blind restoration and adaptively handle the generic and specific restoration scenarios with a single unified model.
From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution
Notably, LQ face images, which may have the same degradation process as natural images, can be robustly restored with photo-realistic textures by exploiting their strong structural priors.
Semantic-shape Adaptive Feature Modulation for Semantic Image Synthesis
Obviously, a fine-grained part-level semantic layout will benefit object details generation, and it can be roughly inferred from an object's shape.
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors
Unlike image-space methods, our FeMaSR restores HR images by matching distorted LR image {\it features} to their distortion-free HR counterparts in our pretrained HR priors, and decoding the matched features to obtain realistic HR images.
Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation
Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i. e., class centers) in a hyper-sphere manifold.
Learning Semantic Person Image Generation by Region-Adaptive Normalization
In the first stage, we predict the target semantic parsing maps to eliminate the difficulties of pose transfer and further benefit the latter translation of per-region appearance style.
Artificial Intelligence Enhanced Rapid and Efficient Diagnosis of Mycoplasma Pneumoniae Pneumonia in Children Patients
Artificial intelligence methods have been increasingly turning into a potentially powerful tool in the diagnosis and management of diseases.
Measuring Global Multi-Scale Place Connectivity using Geotagged Social Media Data
In this study, we introduced a global multi-scale place connectivity index (PCI) based on spatial interactions among places revealed by geotagged tweets as a spatiotemporal-continuous and easy-to-implement measurement.
Social and Information Networks
Progressive Semantic-Aware Style Transformation for Blind Face Restoration
Compared with previous networks, the proposed PSFR-GAN makes full use of the semantic (parsing maps) and pixel (LQ images) space information from different scales of input pairs.
Ranked #4 on
Blind Face Restoration
on CelebA-Test
Blind Face Restoration via Deep Multi-scale Component Dictionaries
Next, with the degraded input, we match and select the most similar component features from their corresponding dictionaries and transfer the high-quality details to the input via the proposed dictionary feature transfer (DFT) block.
Face Super-Resolution Guided by 3D Facial Priors
State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high- resolution facial patterns by exploring local appearance knowledge.
A GRU-based Mixture Density Network for Data-Driven Dynamic Stochastic Programming
To fill the gap, in this work, we propose an innovative data-driven dynamic stochastic programming (DD-DSP) framework for time-series decision-making problem, which involves three components: GRU, Gaussian Mixture Model (GMM) and SP.
Enhanced Blind Face Restoration With Multi-Exemplar Images and Adaptive Spatial Feature Fusion
First, given a degraded observation, we select the optimal guidance based on the weighted affine distance on landmark sets, where the landmark weights are learned to make the guidance image optimized to HQ image reconstruction.
DDKSP: A Data-Driven Stochastic Programming Framework for Car-Sharing Relocation Problem
Car-sharing issue is a popular research field in sharing economy.
A Two-Stage Stochastic Programming Model for Car-Sharing Problem using Kernel Density Estimation
Car-sharing problem is a popular research field in sharing economy.
Learning Symmetry Consistent Deep CNNs for Face Completion
As for missing pixels on both of half-faces, we present a generative reconstruction subnet together with a perceptual symmetry loss to enforce symmetry consistency of recovered structures.
Ranked #1 on
Facial Inpainting
on VggFace2
Identity Preserving Face Completion for Large Ocular Region Occlusion
Different from the state-of-the-art face inpainting methods that have no control over the synthesized content and can only handle frontal face pose, our approach can faithfully recover the missing content under various head poses while preserving the identity.
Learning Warped Guidance for Blind Face Restoration
For better recovery of fine facial details, we modify the problem setting by taking both the degraded observation and a high-quality guided image of the same identity as input to our guided face restoration network (GFRNet).
Ranked #1 on
Image Super-Resolution
on WebFace - 8x upscaling
Shift-Net: Image Inpainting via Deep Feature Rearrangement
To this end, the encoder feature of the known region is shifted to serve as an estimation of the missing parts.
A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC
Recent years have witnessed the proliferation of Massive Open Online Courses (MOOCs).
Neural Generative Question Answering
Empirical study shows the proposed model can effectively deal with the variations of questions and answers, and generate right and natural answers by referring to the facts in the knowledge-base.
