Search Results for author:
Found 26 papers, 11 papers with code
WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection
2) Hierarchical structure: WildFake contains fake images synthesized by different types of generators from GANs, diffusion models, to other generative models.
ComFusion: Personalized Subject Generation in Multiple Specific Scenes From Single Image
Recent advancements in personalizing text-to-image (T2I) diffusion models have shown the capability to generate images based on personalized visual concepts using a limited number of user-provided examples.
Dataset Distillation in Latent Space
Dataset distillation (DD) is a newly emerging research area aiming at alleviating the heavy computational load in training models on large datasets.
Virtual Accessory Try-On via Keypoint Hallucination
Specifically, our approach learns the human body priors and hallucinates the target locations of specified foreground keypoints in the background.
Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow
Our approach, namely Diffusion-based Conditional Inpainting for Virtual Try-ON (DCI-VTON), effectively utilizes the power of the diffusion model, and the incorporation of the warping module helps to produce high-quality and realistic virtual try-on results.
SAFA: Sample-Adaptive Feature Augmentation for Long-Tailed Image Classification
Contrastive loss ensures the transferable semantic directions are class-irrelevant and mode seeking loss is adopted to produce diverse tail-class features and enlarge the feature space of tail classes.
Ranked #29 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Few-shot Image Generation Using Discrete Content Representation
Few-shot image translation disentangles an image into style vector and content map.
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta
In this work, we propose a novel Delta Generative Adversarial Network (DeltaGAN), which consists of a reconstruction subnetwork and a generation subnetwork.
Dual-Path Image Inpainting With Auxiliary GAN Inversion
Different from feed-forward methods, they seek for a closest latent code to the corrupted image and feed it to a pretrained generator.
Defending Graph Neural Networks via Tensor-Based Robust Graph Aggregation
In this paper, we propose a tensor-based framework for GNNs to learn robust graphs from adversarial graphs by aggregating predefined robust graphs to enhance the robustness of GNNs via tensor approximation.
Large-Scale Adversarial Attacks on Graph Neural Networks via Graph Coarsening
Graph Neural Networks (GNNs) are fragile to adversarial attacks.
Shadow Generation for Composite Image in Real-world Scenes
In this work, we focus on generating plausible shadow for the foreground object in the composite image.
Deep Image Harmonization by Bridging the Reality Gap
Image harmonization has been significantly advanced with large-scale harmonization dataset.
Parallel Multi-Resolution Fusion Network for Image Inpainting
Conventional deep image inpainting methods are based on auto-encoder architecture, in which the spatial details of images will be lost in the down-sampling process, leading to the degradation of generated results.
Disentangled Information Bottleneck
The information bottleneck (IB) method is a technique for extracting information that is relevant for predicting the target random variable from the source random variable, which is typically implemented by optimizing the IB Lagrangian that balances the compression and prediction terms.
BargainNet: Background-Guided Domain Translation for Image Harmonization
Therefore, we propose an image harmonization network with a novel domain code extractor and well-tailored triplet losses, which could capture the background domain information to guide the foreground harmonization.
Ranked #13 on
Image Harmonization
on iHarmony4
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta
In this work, we propose a novel Delta Generative Adversarial Network (DeltaGAN), which consists of a reconstruction subnetwork and a generation subnetwork.
F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation
In this paper, we propose a Fusing-and-Filling Generative Adversarial Network (F2GAN) to generate realistic and diverse images for a new category with only a few images.
Beyond without Forgetting: Multi-Task Learning for Classification with Disjoint Datasets
To address these issues, we propose our MTL with Selective Augmentation (MTL-SA) method to select the training samples in unlabeled datasets with confident pseudo labels and close data distribution to the labeled dataset.
MatchingGAN: Matching-based Few-shot Image Generation
Matching generator can match random vectors with a few conditional images from the same category and generate new images for this category based on the fused features.
Exploiting Motion Information from Unlabeled Videos for Static Image Action Recognition
Specifically, the VRE module includes a proxy task which imposes pseudo motion label constraint and temporal coherence constraint on unlabeled videos, while the MRA module could predict the motion information of a static action image by exploiting unlabeled videos.
DoveNet: Deep Image Harmonization via Domain Verification
Image composition is an important operation in image processing, but the inconsistency between foreground and background significantly degrades the quality of composite image.
Ranked #6 on
Image Harmonization
on HAdobe5k(1024$\times$1024)
A Proposal-based Approach for Activity Image-to-Video Retrieval
Activity image-to-video retrieval task aims to retrieve videos containing the similar activity as the query image, which is a challenging task because videos generally have many background segments irrelevant to the activity.
Image Harmonization Dataset iHarmony4: HCOCO, HAdobe5k, HFlickr, and Hday2night
Image composition is an important operation in image processing, but the inconsistency between foreground and background significantly degrades the quality of composite image.
Inductive Guided Filter: Real-time Deep Image Matting with Weakly Annotated Masks on Mobile Devices
A high efficient image matting method based on a weakly annotated mask is in demand for mobile applications.
Multi-shot Pedestrian Re-identification via Sequential Decision Making
In contrary to existing works that aggregate single frames features by time series model such as recurrent neural network, in this paper, we propose an interpretable reinforcement learning based approach to this problem.
