20 accepted papers
Automatic medical report generation from multimodal longitudinal imaging is crucial for clinical diagnosis but remains challenging due to privacy constraints and evolving disease dynamics. While federated learning (FL) enables decentralized model training without data sharing, its extension to longi
We propose a novel continual self-supervised learning method (CSSL) considering medical domain knowledge in chest CT images. Our approach addresses the challenge of sequential learning by effectively capturing the relationship between previously learned knowledge and new information at different sta…
Dataset distillation is an effective technique for reducing the cost and complexity of model training while maintaining performance by compressing large datasets into smaller, more efficient versions. In this paper, we present a novel generative dataset distillation method that can improve the accur…
We present a novel Clustered Federated Learning (CFL) approach that efficiently shares knowledge among clusters to address non-Independent and Identically Distributed (non-IID) settings. Although conventional CFL has demonstrated strong performance in non-IID settings, a fundamental challenge, the l…
This paper presents a novel adversarial defense method that exploits non-transferability of attack across foundation models. Existing adversarial training methods have insufficient robustness to adversarial examples under powerful adversarial attacks in a white-box setting. We clarify that there is…
This paper presents a new task of caption unification and a novel caption unification method for multi-view lifelogging images based on in-context learning with heterogeneous semantic contents. Most of the existing image captioning models target a single image and do not consider the common semantic…
To alleviate the negative impacts of noisy labels, most of the noisy label learning (NLL) methods dynamically divide the training data into two types, "clean samples" and "noisy samples", in the training process. However, the conventional selection of clean samples heavily depends on the features le…
This paper presents a personalized text-to-image diffusion model for multiple object editing that can improve visual fidelity of the target image and editing ability with a segmentation-based restriction and continual learning. Multiple personalization tasks face the problem of destabilization, espe…
We present a novel prompt-based personalized federated learning (pFL) method to address data heterogeneity and privacy concerns in traditional medical visual question answering (VQA) methods. Specifically, we regard medical datasets from different organs as clients and use pFL to train personalized…
We propose a novel binauralization method that is robust to camera rotation. Since binaural audio can bring a 3D sensation to the listener, it can enhance the immersive experience of the video. Researchers have been explored binaural audio generation from monaural audio to deepen the experience of a…
This paper presents a method for estimation of visual cognitive contents from human brain signals via a newly derived visual question answering (VQA) model. The proposed method can estimate a wide range of cognitive contents from functional magnetic resonance imaging data when subjects viewed images…
Variational Autoencoder (VAE)-based generative models offer flexible representation learning by incorporating meta-priors, general premises considered beneficial for downstream tasks. However, the incorporated meta-priors often involve ad-hoc model deviations from the original likelihood architectur…
We propose a novel graph convolutional network (GCN)-based recommendation model that incorporates a contrastive loss. Although GCN-based recommendation models achieve high recommendation performance, existing models suffer from over-fitting since they explicitly encode the interactions as a graph. T…
Domain shift causes performance drop in cross-domain object detection. To alleviate the domain shift, a prevailing approach is global feature alignment with adversarial learning. However, such simple feature alignment has defects of unawareness of fore-ground/background regions and well-aligned/poor…
This paper proposes a novel generative adversarial network to improve the performance of image manipulation using natural language descriptions that contain desired attributes. Text-guided image manipulation aims to semantically manipulate an image aligned with the text description while preserving…
The global outbreak of the Coronavirus 2019 (COVID-19) has overloaded worldwide healthcare systems. Computer-aided diagnosis for COVID-19 fast detection and patient triage is becoming critical. This paper proposes a novel self-knowledge distillation based self-supervised learning method for COVID-19…
This paper proposes a novel self-supervised learning method for learning better representations with small batch sizes. Many self-supervised learning methods based on certain forms of the siamese network have emerged and received significant attention. However, these methods need to use large batch…
"This paper solves a generalized version of the problem of multi-source model adaptation for semantic segmentation. Model adaptation is proposed as a new domain adaptation problem which requires access to a pre-trained model instead of data for the source domain. A general multi-source setting of mo…
Unpaired image-to-image (I2I) translation methods have been developed for several years. Present methods do not take into consideration semantic information of the original image, which may perform well on simple datasets of uncomplicated scenes, however, fail in complex datasets of scenes involving…
Unsupervised domain adaptation, which leverages label information from other domains to solve tasks on a domain without any labels, can alleviate the problem of the scarcity of labels and expensive labeling costs faced by supervised semantic segmentation. In this paper, we utilize adversarial learni…