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Found 30 papers, 17 papers with code
Diverse Image-to-Image Translation via Disentangled Representations
Our model takes the encoded content features extracted from a given input and the attribute vectors sampled from the attribute space to produce diverse outputs at test time.
Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
In this work, we propose a simple yet effective regularization term to address the mode collapse issue for cGANs.
Multimodal Unsupervised Image-To-Image Translation
Translation
DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
In this work, we present an approach based on disentangled representation for generating diverse outputs without paired training images.
Few-Shot Viewpoint Estimation
Through extensive experimentation on the ObjectNet3D and Pascal3D+ benchmark datasets, we demonstrate that our framework, which we call MetaView, significantly outperforms fine-tuning the state-of-the-art models with few examples, and that the specific architectural innovations of our method are crucial to achieving good performance.
Self-supervised Audio Spatialization with Correspondence Classifier
Spatial audio is an essential medium to audiences for 3D visual and auditory experience.
Progressive Domain Adaptation for Object Detection
This intermediate domain is constructed by translating the source images to mimic the ones in the target domain.
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
Few-shot classification aims to recognize novel categories with only few labeled images in each class.
Ranked #6 on
Cross-Domain Few-Shot
on CUB
Regularizing Meta-Learning via Gradient Dropout
With the growing attention on learning-to-learn new tasks using only a few examples, meta-learning has been widely used in numerous problems such as few-shot classification, reinforcement learning, and domain generalization.
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.
RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval
Image generation from scene description is a cornerstone technique for the controlled generation, which is beneficial to applications such as content creation and image editing.
Text as Neural Operator: Image Manipulation by Text Instruction
In recent years, text-guided image manipulation has gained increasing attention in the multimedia and computer vision community.
Semantic View Synthesis
We tackle a new problem of semantic view synthesis -- generating free-viewpoint rendering of a synthesized scene using a semantic label map as input.
Continuous and Diverse Image-to-Image Translation via Signed Attribute Vectors
Generating a smooth sequence of intermediate results bridges the gap of two different domains, facilitating the morphing effect across domains.
Unsupervised Discovery of Disentangled Manifolds in GANs
Interpretable generation process is beneficial to various image editing applications.
Unsupervised Sound Localization via Iterative Contrastive Learning
Sound localization aims to find the source of the audio signal in the visual scene.
Regularizing Generative Adversarial Networks under Limited Data
Recent years have witnessed the rapid progress of generative adversarial networks (GANs).
Ranked #1 on
Image Generation
on CIFAR-100
Stylizing 3D Scene via Implicit Representation and HyperNetwork
Our framework consists of two components: an implicit representation of the 3D scene with the neural radiance fields model, and a hypernetwork to transfer the style information into the scene representation.
Learning to Stylize Novel Views
Second, we develop point cloud aggregation modules to gather the style information of the 3D scene, and then modulate the features in the point cloud with a linear transformation matrix.
Incremental False Negative Detection for Contrastive Learning
Self-supervised learning has recently shown great potential in vision tasks through contrastive learning, which aims to discriminate each image, or instance, in the dataset.
Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing
The audio-visual video parsing task aims to temporally parse a video into audio or visual event categories.
Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features
Recent studies show that paddings in convolutional neural networks encode absolute position information which can negatively affect the model performance for certain tasks.
Vector Quantized Image-to-Image Translation
For example, it provides style variability for image generation and extension, and equips image-to-image translation with further extension capabilities.
Adaptively-Realistic Image Generation from Stroke and Sketch with Diffusion Model
Generating images from hand-drawings is a crucial and fundamental task in content creation.
Robust Dynamic Radiance Fields
Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene.
Consistent View Synthesis with Pose-Guided Diffusion Models
In this work, we propose a pose-guided diffusion model to generate a consistent long-term video of novel views from a single image.
Motion-Conditioned Diffusion Model for Controllable Video Synthesis
In response to this gap, we introduce MCDiff, a conditional diffusion model that generates a video from a starting image frame and a set of strokes, which allow users to specify the intended content and dynamics for synthesis.
Single-Image 3D Human Digitization with Shape-Guided Diffusion
We present an approach to generate a 360-degree view of a person with a consistent, high-resolution appearance from a single input image.
Exploiting Diffusion Prior for Generalizable Dense Prediction
Contents generated by recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf dense predictors to estimate due to the immitigable domain gap.
ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models
In this paper, we present a method that leverages pretrained text-to-image models as a prior, and learn to generate multi-view images in a single denoising process from real-world data.
Taming Latent Diffusion Model for Neural Radiance Field Inpainting
These two problems are further reinforced with the use of pixel-distance losses.
