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Found 23 papers, 7 papers with code
KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web.
Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model
While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge.
Breathing Life into Faces: Speech-driven 3D Facial Animation with Natural Head Pose and Detailed Shape
To augment the richness of 3D facial animation, we construct a new 3D dataset with detailed shapes and learn to synthesize facial details in line with speech content.
Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering
2) the input text and image are often not perfectly matched, and thus the image may introduce noise into the model.
DiffColor: Toward High Fidelity Text-Guided Image Colorization with Diffusion Models
To address these issues, we propose a new method called DiffColor that leverages the power of pre-trained diffusion models to recover vivid colors conditioned on a prompt text, without any additional inputs.
EMoG: Synthesizing Emotive Co-speech 3D Gesture with Diffusion Model
In this paper, we present a novel framework (EMoG) to tackle the above challenges with denoising diffusion models: 1) To alleviate the one-to-many problem, we incorporate emotion clues to guide the generation process, making the generation much easier; 2) To model joint correlation, we propose to decompose the difficult gesture generation into two sub-problems: joint correlation modeling and temporal dynamics modeling.
Constrained Maximum Cross-Domain Likelihood for Domain Generalization
Finally, a Constrained Maximum Cross-domain Likelihood (CMCL) optimization problem is deduced, by solving which the joint distributions are naturally aligned.
Mitigating Both Covariate and Conditional Shift for Domain Generalization
Domain generalization (DG) aims to learn a model on several source domains, hoping that the model can generalize well to unseen target domains.
Image-to-Image Translation: Methods and Applications
Image-to-image translation (I2I) aims to transfer images from a source domain to a target domain while preserving the content representations.
LIRA: Lifelong Image Restoration from Unknown Blended Distortions
Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task.
Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration
Hybrid-distorted image restoration (HD-IR) is dedicated to restore real distorted image that is degraded by multiple distortions.
TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images
With TuiGAN, an image is translated in a coarse-to-fine manner where the generated image is gradually refined from global structures to local details.
ZstGAN: An Adversarial Approach for Unsupervised Zero-Shot Image-to-Image Translation
Image-to-image translation models have shown remarkable ability on transferring images among different domains.
Learning to Transfer: Unsupervised Meta Domain Translation
Unsupervised domain translation has recently achieved impressive performance with Generative Adversarial Network (GAN) and sufficient (unpaired) training data.
Image-to-Image Translation with Multi-Path Consistency Regularization
In this work, we introduce a new kind of loss, multi-path consistency loss, which evaluates the differences between direct translation $\mathcal{D}_s\to\mathcal{D}_t$ and indirect translation $\mathcal{D}_s\to\mathcal{D}_a\to\mathcal{D}_t$ with $\mathcal{D}_a$ as an auxiliary domain, to regularize training.
Exploring Explicit Domain Supervision for Latent Space Disentanglement in Unpaired Image-to-Image Translation
After pre-training, this network is used to extract the domain-specific features of each image.
Sequential Gating Ensemble Network for Noise Robust Multi-Scale Face Restoration
The SGU sequentially takes information from two different levels as inputs and decides the output based on one active input.
Unsupervised Single Image Deraining with Self-supervised Constraints
Most existing single image deraining methods require learning supervised models from a large set of paired synthetic training data, which limits their generality, scalability and practicality in real-world multimedia applications.
DeepIR: A Deep Semantics Driven Framework for Image Retargeting
We present \emph{Deep Image Retargeting} (\emph{DeepIR}), a coarse-to-fine framework for content-aware image retargeting.
Distribution Discrepancy Maximization for Image Privacy Preserving
Accordingly, we introduce a collaborative training scheme: a discriminator $D$ is trained to discriminate the reconstructed image from the encrypted image, and an encryption model $G_e$ is required to generate these two kinds of images to maximize the recognition rate of $D$, leading to the same training objective for both $D$ and $G_e$.
Multi-Scale Face Restoration with Sequential Gating Ensemble Network
Experiment results demonstrate that our SGEN is more effective at multi-scale human face restoration with more image details and less noise than state-of-the-art image restoration models.
Conditional Image-to-Image Translation
In this paper, we study a new problem, conditional image-to-image translation, which is to translate an image from the source domain to the target domain conditioned on a given image in the target domain.
Deliberation Networks: Sequence Generation Beyond One-Pass Decoding
In this work, we introduce the deliberation process into the encoder-decoder framework and propose deliberation networks for sequence generation.
