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Found 24 papers, 18 papers with code
Break-for-Make: Modular Low-Rank Adaptations for Composable Content-Style Customization
Based on the adapters broken apart for separate training content and style, we then make the entity parameter space by reconstructing the content and style PLPs matrices, followed by fine-tuning the combined adapter to generate the target object with the desired appearance.
CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion
Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images.
Three Heads Are Better Than One: Complementary Experts for Long-Tailed Semi-supervised Learning
For instance, on CIFAR-10-LT, CPE improves test accuracy by over 2. 22% compared to baselines.
MotionCrafter: One-Shot Motion Customization of Diffusion Models
The essence of a video lies in its dynamic motions, including character actions, object movements, and camera movements.
$Z^*$: Zero-shot Style Transfer via Attention Rearrangement
Despite the remarkable progress in image style transfer, formulating style in the context of art is inherently subjective and challenging.
ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
We apply ProSpect in various personalized attribute-aware image generation applications, such as image-guided or text-driven manipulations of materials, style, and layout, achieving previously unattainable results from a single image input without fine-tuning the diffusion models.
Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer
Large-scale text-to-video diffusion models have demonstrated an exceptional ability to synthesize diverse videos.
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning
Our framework consists of three key components, i. e., a parallel contrastive learning scheme for style representation and style transfer, a domain enhancement module for effective learning of style distribution, and a generative network for style transfer.
Region-Aware Diffusion for Zero-shot Text-driven Image Editing
Different from current mask-based image editing methods, we propose a novel region-aware diffusion model (RDM) for entity-level image editing, which could automatically locate the region of interest and replace it following given text prompts.
Inversion-Based Style Transfer with Diffusion Models
Our key idea is to learn artistic style directly from a single painting and then guide the synthesis without providing complex textual descriptions.
DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization
Despite the impressive results of arbitrary image-guided style transfer methods, text-driven image stylization has recently been proposed for transferring a natural image into a stylized one according to textual descriptions of the target style provided by the user.
Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models
Pretrained vision-language models (VLMs) such as CLIP have shown impressive generalization capability in downstream vision tasks with appropriate text prompts.
Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided Diffusion
Extensive experimental results on the quality and quantity of the generated digital art paintings confirm the effectiveness of the combination of the diffusion model and multimodal guidance.
Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning
Our framework consists of three key components, i. e., a multi-layer style projector for style code encoding, a domain enhancement module for effective learning of style distribution, and a generative network for image style transfer.
CrossRectify: Leveraging Disagreement for Semi-supervised Object Detection
Semi-supervised object detection has recently achieved substantial progress.
StyTr2: Image Style Transfer With Transformers
The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content.
Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer
Vision transformers (ViTs) have recently received explosive popularity, but the huge computational cost is still a severe issue.
Ranked #11 on
Efficient ViTs
on ImageNet-1K (with DeiT-T)
User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning
This framework leverages user interactions to retouch and rank images for aesthetic assessment based on deep reinforcement learning (DRL), and generates personalized aesthetic distribution that is more in line with the aesthetic preferences of different users.
StyTr$^2$: Image Style Transfer with Transformers
The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content.
Towards Corruption-Agnostic Robust Domain Adaptation
However, due to unpredictable corruptions (e. g., noise and blur) in real data like web images, domain adaptation methods are increasingly required to be corruption robust on target domains.
On Evolving Attention Towards Domain Adaptation
However, considering that the configuration of attention, i. e., the type and the position of attention module, affects the performance significantly, it is more generalized to optimize the attention configuration automatically to be specialized for arbitrary UDA scenario.
Ranked #1 on
Partial Domain Adaptation
on Office-Home
Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization
Weakly supervised object localization(WSOL) remains an open problem given the deficiency of finding object extent information using a classification network.
Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation
For intra-domain propagation, we propose an effective self-training strategy to mitigate the noises in pseudo-labeled target domain data and improve the feature discriminability in the target domain.
Image Retargeting by Content-Aware Synthesis
We propose to retarget the textural regions by content-aware synthesis and non-textural regions by fast multi-operators.
