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Found 49 papers, 20 papers with code
Distributionally Robust Policy Evaluation and Learning for Continuous Treatment with Observational Data
The proposed distributionally robust estimators are established using the Inverse Probability Weighting (IPW) method extended from the discrete one for policy evaluation and learning under continuous treatments.
TransPixeler: Advancing Text-to-Video Generation with Transparency
Text-to-video generative models have made significant strides, enabling diverse applications in entertainment, advertising, and education.
Generative Video Propagation
Large-scale video generation models have the inherent ability to realistically model natural scenes.
UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics
We introduce UniReal, a unified framework designed to address various image generation and editing tasks.
LayerFusion: Harmonized Multi-Layer Text-to-Image Generation with Generative Priors
In this paper, we propose a novel image generation pipeline based on Latent Diffusion Models (LDMs) that generates images with two layers: a foreground layer (RGBA) with transparency information and a background layer (RGB).
Generative Image Layer Decomposition with Visual Effects
Layered representations, which allow for independent editing of image components, are essential for user-driven content creation, yet existing approaches often struggle to decompose image into plausible layers with accurately retained transparent visual effects such as shadows and reflections.
A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models
The neural network model achieved a high accuracy of 98% in these challenging classification tasks, while the XGBoost model reached a success rate of 85. 26% in prediction tasks.
Removing Distributional Discrepancies in Captions Improves Image-Text Alignment
In this paper, we introduce a model designed to improve the prediction of image-text alignment, targeting the challenge of compositional understanding in current visual-language models.
SG-Adapter: Enhancing Text-to-Image Generation with Scene Graph Guidance
In this paper, we introduce the Scene Graph Adapter(SG-Adapter), leveraging the structured representation of scene graphs to rectify inaccuracies in the original text embeddings.
Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models
Specifically, for single-denoising-step pruning, we develop a novel ranking algorithm, Generalized Weighted Page Rank (G-WPR), to identify redundant tokens, and a similarity-based recovery method to restore tokens for the convolution operation.
Motion Inversion for Video Customization
In this work, we present a novel approach for motion customization in video generation, addressing the widespread gap in the exploration of motion representation within video generative models.
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
To address these challenges, this paper introduces a Distributionally Robust Metric (DRM) for CATE estimator selection.
MAGICK: A Large-scale Captioned Dataset from Matting Generated Images using Chroma Keying
Using our method we generate a dataset of 150000 objects with alpha.
The Causal Impact of Credit Lines on Spending Distributions
Consumer credit services offered by e-commerce platforms provide customers with convenient loan access during shopping and have the potential to stimulate sales.
Separate-and-Enhance: Compositional Finetuning for Text2Image Diffusion Models
Despite recent significant strides achieved by diffusion-based Text-to-Image (T2I) models, current systems are still less capable of ensuring decent compositional generation aligned with text prompts, particularly for the multi-object generation.
Data-driven Preference Learning Methods for Sorting Problems with Multiple Temporal Criteria
This study presents novel preference learning approaches to multiple criteria sorting problems in the presence of temporal criteria.
DeLELSTM: Decomposition-based Linear Explainable LSTM to Capture Instantaneous and Long-term Effects in Time Series
The tensorized LSTM assigns each variable with a unique hidden state making up a matrix $\mathbf{h}_t$, and the standard LSTM models all the variables with a shared hidden state $\mathbf{H}_t$.
Consistent Multimodal Generation via A Unified GAN Framework
We investigate how to generate multimodal image outputs, such as RGB, depth, and surface normals, with a single generative model.
Text-Anchored Score Composition: Tackling Condition Misalignment in Text-to-Image Diffusion Models
However, these models are learned based on the premise of perfect alignment between the text and extra conditions.
Probabilistic Learning of Multivariate Time Series with Temporal Irregularity
Multivariate sequential data collected in practice often exhibit temporal irregularities, including nonuniform time intervals and component misalignment.
Neural Partial Differential Equations with Functional Convolution
We present a lightweighted neural PDE representation to discover the hidden structure and predict the solution of different nonlinear PDEs.
Zero-shot Image-to-Image Translation
However, it is still challenging to directly apply these models for editing real images for two reasons.
Ranked #16 on
Text-based Image Editing
on PIE-Bench
Cross-modal Latent Space Alignment for Image to Avatar Translation
We present a novel method for automatic vectorized avatar generation from a single portrait image.
Challenges and perspectives in computational deconvolution of genomics data
Deciphering cell type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases.
Contrastive Learning for Diverse Disentangled Foreground Generation
We introduce a new method for diverse foreground generation with explicit control over various factors.
3D-FM GAN: Towards 3D-Controllable Face Manipulation
While concatenating GAN inversion and a 3D-aware, noise-to-image GAN is a straight-forward solution, it is inefficient and may lead to noticeable drop in editing quality.
A Fast Text-Driven Approach for Generating Artistic Content
In this work, we propose a complete framework that generates visual art.
Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing
We propose a new method to invert and edit such complex images in the latent space of GANs, such as StyleGAN2.
Emerging Artificial Intelligence Applications in Spatial Transcriptomics Analysis
Spatial transcriptomics (ST) has advanced significantly in the last few years.
Computational Methods for Single-Cell Multi-Omics Integration and Alignment
Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology.
Collaging Class-specific GANs for Semantic Image Synthesis
We propose a new approach for high resolution semantic image synthesis.
ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs
Intercellular heterogeneity is a major obstacle to successful precision medicine.
Few-shot Image Generation via Cross-domain Correspondence
Training generative models, such as GANs, on a target domain containing limited examples (e. g., 10) can easily result in overfitting.
Ranked #3 on
10-shot image generation
on Babies
IMAGINE: Image Synthesis by Image-Guided Model Inversion
We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images from only a single training sample.
Rethinking and Improving the Robustness of Image Style Transfer
Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has a remarkable ability to capture the visual style of an image.
Content-Aware GAN Compression
We then propose a novel content-aware method to guide the processes of both pruning and distillation.
Few-shot Image Generation with Elastic Weight Consolidation
Few-shot image generation seeks to generate more data of a given domain, with only few available training examples.
Ranked #4 on
10-shot image generation
on Babies
Learning to Caricature via Semantic Shape Transform
Caricature is an artistic drawing created to abstract or exaggerate facial features of a person.
Modeling Artistic Workflows for Image Generation and Editing
People often create art by following an artistic workflow involving multiple stages that inform the overall design.
Collaborative Distillation for Ultra-Resolution Universal Style Transfer
In this work, we present a new knowledge distillation method (named Collaborative Distillation) for encoder-decoder based neural style transfer to reduce the convolutional filters.
Controllable and Progressive Image Extrapolation
Image extrapolation aims at expanding the narrow field of view of a given image patch.
Im2Pencil: Controllable Pencil Illustration from Photographs
We propose a high-quality photo-to-pencil translation method with fine-grained control over the drawing style.
Flow-Grounded Spatial-Temporal Video Prediction from Still Images
Existing video prediction methods mainly rely on observing multiple historical frames or focus on predicting the next one-frame.
A Closed-form Solution to Photorealistic Image Stylization
Photorealistic image stylization concerns transferring style of a reference photo to a content photo with the constraint that the stylized photo should remain photorealistic.
Joint Image Filtering with Deep Convolutional Networks
In contrast to existing methods that consider only the guidance image, the proposed algorithm can selectively transfer salient structures that are consistent with both guidance and target images.
Universal Style Transfer via Feature Transforms
The whitening and coloring transforms reflect a direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer.
Generative Face Completion
In this paper, we propose an effective face completion algorithm using a deep generative model.
Diversified Texture Synthesis with Feed-forward Networks
Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis.
Robust High Quality Image Guided Depth Upsampling
A popular solution is upsampling the obtained noisy low resolution depth map with the guidance of the companion high resolution color image.
