Search Results for author:
Found 34 papers, 23 papers with code
Get What You Want, Not What You Don't: Image Content Suppression for Text-to-Image Diffusion Models
However, these models struggle to effectively suppress the generation of undesired content, which is explicitly requested to be omitted from the generated image in the prompt.
Faster Diffusion: Rethinking the Role of UNet Encoder in Diffusion Models
This finding inspired us to omit the encoder at certain adjacent time-steps and reuse cyclically the encoder features in the previous time-steps for the decoder.
MaTe3D: Mask-guided Text-based 3D-aware Portrait Editing
To address this limitation, we propose \textbf{MaTe3D}: mask-guided text-based 3D-aware portrait editing.
MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask
In this work, we identify that a crucial factor leading to the text-image mismatch issue is the inadequate cross-modality relation learning between the prompt and the output image.
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering
We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity.
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling
This paper considers a novel application of deep AUC maximization (DAM) for multi-instance learning (MIL), in which a single class label is assigned to a bag of instances (e. g., multiple 2D slices of a CT scan for a patient).
StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing
A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images.
Ranked #7 on
Text-based Image Editing
on PIE-Bench
3D-Aware Multi-Class Image-to-Image Translation with NeRFs
In the second step, based on the well-trained multi-class 3D-aware GAN architecture, that preserves view-consistency, we construct a 3D-aware I2I translation system.
Adaptive Texture Filtering for Single-Domain Generalized Segmentation
Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features.
OneRing: A Simple Method for Source-free Open-partial Domain Adaptation
In this paper, we investigate Source-free Open-partial Domain Adaptation (SF-OPDA), which addresses the situation where there exist both domain and category shifts between source and target domains.
MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation
We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116, 000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera locations in the upper hemisphere.
Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation
Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of prediction consistency.
Transferring Unconditional to Conditional GANs with Hyper-Modulation
GANs have matured in recent years and are able to generate high-resolution, realistic images.
A Novel Framework for Image-to-image Translation and Image Compression
Thus, we further propose a unified framework that allows both translation and autoencoding capabilities in a single codec.
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation
In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data.
Ranked #3 on
Source-Free Domain Adaptation
on VisDA-2017
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data
Therefore, we investigate knowledge distillation to transfer knowledge from a high-quality unconditioned generative model (e. g., StyleGAN) to a conditioned synthetic image generation modules in a variety of systems.
Generalized Source-free Domain Adaptation
In this paper, we propose a new domain adaptation paradigm called Generalized Source-free Domain Adaptation (G-SFDA), where the learned model needs to perform well on both the target and source domains, with only access to current unlabeled target data during adaptation.
Ranked #2 on
Source-Free Domain Adaptation
on VisDA-2017
TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets
In this paper, we propose a new transfer learning for I2I translation (TransferI2I).
MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains
Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs.
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs
To enable the training of deep I2I models on small datasets, we propose a novel transfer learning method, that transfers knowledge from pre-trained GANs.
Casting a BAIT for Offline and Online Source-free Domain Adaptation
When adapting to the target domain, the additional classifier initialized from source classifier is expected to find misclassified features.
Source-Free Domain Adaptation
Unsupervised Domain Adaptation
Globally Optimal Segmentation of Mutually Interacting Surfaces using Deep Learning
In this work, we propose to parameterize the surface cost functions in the graph model and leverage DL to learn those parameters.
Semi-supervised Learning for Few-shot Image-to-Image Translation
In this work, we go one step further and reduce the amount of required labeled data also from the source domain during training.
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content.
MineGAN: effective knowledge transfer from GANs to target domains with few images
We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs.
SDIT: Scalable and Diverse Cross-domain Image Translation
Recently, image-to-image translation research has witnessed remarkable progress.
Controlling biases and diversity in diverse image-to-image translation
The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances.
Mix and match networks: cross-modal alignment for zero-pair image-to-image translation
This paper addresses the problem of inferring unseen cross-modal image-to-image translations between multiple modalities.
Memory Replay GANs: Learning to Generate New Categories without Forgetting
In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion.
Memory Replay GANs: learning to generate images from new categories without forgetting
In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion.
Transferring GANs: generating images from limited data
Transferring the knowledge of pretrained networks to new domains by means of finetuning is a widely used practice for applications based on discriminative models.
Ranked #7 on
10-shot image generation
on Babies
Mix and match networks: encoder-decoder alignment for zero-pair image translation
We address the problem of image translation between domains or modalities for which no direct paired data is available (i. e. zero-pair translation).
Ensembles of Generative Adversarial Networks
The first one is based on the fact that in the minimax game which is played to optimize the GAN objective the generator network keeps on changing even after the network can be considered optimal.
Does Multimodality Help Human and Machine for Translation and Image Captioning?
This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge.
