Search Results for author:
Found 53 papers, 36 papers with code
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
Our approach models an image as a composition of label and latent attributes in a probabilistic model.
Towards Open-Set Identity Preserving Face Synthesis
We then recombine the identity vector and the attribute vector to synthesize a new face of the subject with the extracted attribute.
Mask-Guided Portrait Editing with Conditional GANs
Portrait editing is a popular subject in photo manipulation.
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
We propose a novel attributes encoder for extracting multi-level target face attributes, and a new generator with carefully designed Adaptive Attentional Denormalization (AAD) layers to adaptively integrate the identity and the attributes for face synthesis.
Face X-ray for More General Face Forgery Detection
For this reason, face X-ray provides an effective way for detecting forgery generated by most existing face manipulation algorithms.
GIQA: Generated Image Quality Assessment
Generative adversarial networks (GANs) have achieved impressive results today, but not all generated images are perfect.
PriorGAN: Real Data Prior for Generative Adversarial Nets
To address these two issues, we propose a novel prior that captures the whole real data distribution for GANs, which are called PriorGANs.
Improving Person Re-identification with Iterative Impression Aggregation
Not only does such a simple method improve the performance of the baseline models, it also achieves comparable performance with latest advanced re-ranking methods.
GreedyFool: Distortion-Aware Sparse Adversarial Attack
Sparse adversarial samples are a special branch of adversarial samples that can fool the target model by only perturbing a few pixels.
Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition
A key challenge in video enhancement and action recognition is to fuse useful information from neighboring frames.
CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation
We present the full-resolution correspondence learning for cross-domain images, which aids image translation.
Identity-Driven DeepFake Detection
Our approach takes as input the suspect image/video as well as the target identity information (a reference image or video).
Unsupervised Pre-training for Person Re-identification
In this paper, we present a large scale unlabeled person re-identification (Re-ID) dataset "LUPerson" and make the first attempt of performing unsupervised pre-training for improving the generalization ability of the learned person Re-ID feature representation.
Ranked #1 on
Person Re-Identification
on Market-1501
(using extra training data)
High-Fidelity and Arbitrary Face Editing
However, we observe that the generator tends to find a tricky way to hide information from the original image to satisfy the constraint of cycle consistency, making it impossible to maintain the rich details (e. g., wrinkles and moles) of non-editing areas.
Uformer: A General U-Shaped Transformer for Image Restoration
Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.
Ranked #2 on
Deblurring
on RealBlur-R (trained on GoPro)
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows
By further pretraining on the larger dataset ImageNet-21K, we achieve 87. 5% Top-1 accuracy on ImageNet-1K and high segmentation performance on ADE20K with 55. 7 mIoU.
Ranked #25 on
Semantic Segmentation
on ADE20K val
Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translation
In this paper, we uncover that the negative examples play a critical role in the performance of contrastive learning for image translation.
Dual Path Learning for Domain Adaptation of Semantic Segmentation
In this paper, based on the observation that domain adaptation frameworks performed in the source and target domain are almost complementary in terms of image translation and SSL, we propose a novel dual path learning (DPL) framework to alleviate visual inconsistency.
Exploring Temporal Coherence for More General Video Face Forgery Detection
The first stage is a fully temporal convolution network (FTCN).
Ranked #4 on
DeepFake Detection
on FakeAVCeleb
SimMIM: A Simple Framework for Masked Image Modeling
We also leverage this approach to facilitate the training of a 3B model (SwinV2-G), that by $40\times$ less data than that in previous practice, we achieve the state-of-the-art on four representative vision benchmarks.
Representation Learning
Self-Supervised Image Classification
+1
PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers
This paper explores a better prediction target for BERT pre-training of vision transformers.
Vector Quantized Diffusion Model for Text-to-Image Synthesis
Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality.
Ranked #1 on
Text-to-Image Generation
on Oxford 102 Flowers
(using extra training data)
General Facial Representation Learning in a Visual-Linguistic Manner
In this paper, we study the transfer performance of pre-trained models on face analysis tasks and introduce a framework, called FaRL, for general Facial Representation Learning in a visual-linguistic manner.
Ranked #1 on
Face Parsing
on CelebAMask-HQ
(using extra training data)
StyleSwin: Transformer-based GAN for High-resolution Image Generation
To this end, we believe that local attention is crucial to strike the balance between computational efficiency and modeling capacity.
Ranked #1 on
Image Generation
on CelebA 256x256
(FID metric)
Protecting Celebrities from DeepFake with Identity Consistency Transformer
In this work we propose Identity Consistency Transformer, a novel face forgery detection method that focuses on high-level semantics, specifically identity information, and detecting a suspect face by finding identity inconsistency in inner and outer face regions.
Semi-Supervised Image-to-Image Translation using Latent Space Mapping
Thanks to the low dimensional feature space, it is easier to find the desired mapping function, resulting in improved quality of translation results as well as the stability of the translation model.
Large-Scale Pre-training for Person Re-identification with Noisy Labels
Since theses ID labels automatically derived from tracklets inevitably contain noises, we develop a large-scale Pre-training framework utilizing Noisy Labels (PNL), which consists of three learning modules: supervised Re-ID learning, prototype-based contrastive learning, and label-guided contrastive learning.
Ranked #7 on
Person Re-Identification
on CUHK03
Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation
These properties, which we aggregately refer to as optimization friendliness, are identified and analyzed by a set of attention- and optimization-related diagnosis tools.
Ranked #2 on
Instance Segmentation
on COCO test-dev
(using extra training data)
Improved Vector Quantized Diffusion Models
When trained on ImageNet, we dramatically improve the FID score from 11. 89 to 4. 83, demonstrating the superiority of our proposed techniques.
I^2R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation
In this paper, we present the Intra- and Inter-Human Relation Networks (I^2R-Net) for Multi-Person Pose Estimation.
Ranked #2 on
Multi-Person Pose Estimation
on OCHuman
Semantic Image Synthesis via Diffusion Models
Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable success in various image generation tasks compared with Generative Adversarial Nets (GANs).
Bootstrapped Masked Autoencoders for Vision BERT Pretraining
The first design is motivated by the observation that using a pretrained MAE to extract the features as the BERT prediction target for masked tokens can achieve better pretraining performance.
MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining
Second, masked self-distillation is also consistent with vision-language contrastive from the perspective of training objective as both utilize the visual encoder for feature aligning, and thus is able to learn local semantics getting indirect supervision from the language.
SinDiffusion: Learning a Diffusion Model from a Single Natural Image
We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image.
Ranked #1 on
Image Generation
on Places50
CLIP2GAN: Towards Bridging Text with the Latent Space of GANs
The key idea of our CLIP2GAN is to bridge the output feature embedding space of CLIP and the input latent space of StyleGAN, which is realized by introducing a mapping network.
X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion
We demonstrate for the first time that using a text2image model to generate images or zero-shot recognition model to filter noisily crawled images for different object categories is a feasible way to make Copy-Paste truly scalable.
Ranked #7 on
Instance Segmentation
on LVIS v1.0 val
CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet
Recent studies have shown that CLIP has achieved remarkable success in performing zero-shot inference while its fine-tuning performance is not satisfactory.
Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion
This paper presents a 3D generative model that uses diffusion models to automatically generate 3D digital avatars represented as neural radiance fields.
Improving CLIP Fine-tuning Performance
Experiments suggest that the feature map distillation approach significantly boosts the fine-tuning performance of CLIP models on several typical downstream vision tasks.
Efficient Diffusion Training via Min-SNR Weighting Strategy
Denoising diffusion models have been a mainstream approach for image generation, however, training these models often suffers from slow convergence.
DIRE for Diffusion-Generated Image Detection
We find that existing detectors struggle to detect images generated by diffusion models, even if we include generated images from a specific diffusion model in their training data.
CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning
Our framework, termed as domain-aware sign language retrieval via Cross-lingual Contrastive learning or CiCo for short, outperforms the pioneering method by large margins on various datasets, e. g., +22. 4 T2V and +28. 0 V2T R@1 improvements on How2Sign dataset, and +13. 7 T2V and +17. 1 V2T R@1 improvements on PHOENIX-2014T dataset.
Ranked #1 on
Sign Language Retrieval
on CSL-Daily
Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models
Text-to-Image diffusion models have made tremendous progress over the past two years, enabling the generation of highly realistic images based on open-domain text descriptions.
Designing a Better Asymmetric VQGAN for StableDiffusion
The training cost of our asymmetric VQGAN is cheap, and we only need to retrain a new asymmetric decoder while keeping the vanilla VQGAN encoder and StableDiffusion unchanged.
HQ-50K: A Large-scale, High-quality Dataset for Image Restoration
This paper introduces a new large-scale image restoration dataset, called HQ-50K, which contains 50, 000 high-quality images with rich texture details and semantic diversity.
AltFreezing for More General Video Face Forgery Detection
In this paper, we propose to capture both spatial and temporal artifacts in one model for face forgery detection.
InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions.
CCEdit: Creative and Controllable Video Editing via Diffusion Models
The versatility of our framework is demonstrated through a diverse range of choices in both structure representations and personalized T2I models, as well as the option to provide the edited key frame.
PersonMAE: Person Re-Identification Pre-Training with Masked AutoEncoders
Pre-training is playing an increasingly important role in learning generic feature representation for Person Re-identification (ReID).
ART$\boldsymbol{\cdot}$V: Auto-Regressive Text-to-Video Generation with Diffusion Models
Second, it preserves the high-fidelity generation ability of the pre-trained image diffusion models by making only minimal network modifications.
MicroCinema: A Divide-and-Conquer Approach for Text-to-Video Generation
We present MicroCinema, a straightforward yet effective framework for high-quality and coherent text-to-video generation.
Towards More Unified In-context Visual Understanding
Thanks to this design, the model is capable of handling in-context vision understanding tasks with multimodal output in a unified pipeline. Experimental results demonstrate that our model achieves competitive performance compared with specialized models and previous ICL baselines.
VolumeDiffusion: Flexible Text-to-3D Generation with Efficient Volumetric Encoder
The 3D volumes are then trained on a diffusion model for text-to-3D generation using a 3D U-Net.
