Search Results for author:
Found 9 papers, 6 papers with code
Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variations
Due to the data absence, the textual description of the target domain and the vision-language models, e. g., CLIP, are utilized to effectively guide the generator.
AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks
To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corresponding patches of the content image.
Source-free Subject Adaptation for EEG-based Visual Recognition
In this paper, we introduce a novel and practical problem setup, namely source-free subject adaptation, where the source subject data are unavailable and only the pre-trained model parameters are provided for subject adaptation.
Exploiting Domain Transferability for Collaborative Inter-level Domain Adaptive Object Detection
Domain adaptation for object detection (DAOD) has recently drawn much attention owing to its capability of detecting target objects without any annotations.
Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual Recognition
This paper tackles the problem of subject adaptive EEG-based visual recognition.
Subject Adaptive EEG-based Visual Recognition
It limits recognition systems to work only for the subjects involved in model training, which is undesirable for real-world scenarios where new subjects are frequently added.
Feature Stylization and Domain-aware Contrastive Learning for Domain Generalization
To these ends, we propose a novel domain generalization framework where feature statistics are utilized for stylizing original features to ones with novel domain properties.
Ranked #34 on
Domain Generalization
on Office-Home
Domain-Aware Universal Style Transfer
To this end, we design a novel domainness indicator that captures the domainness value from the texture and structural features of reference images.
Continuous Face Aging Generative Adversarial Networks
Face aging is the task aiming to translate the faces in input images to designated ages.
