26 citations · 56 across the 13 of their papers we have counts for
14 papers
Omnilingual SONAR: Cross-Lingual and Cross-Modal Sentence Embeddings Bridging Massively Multilingual Text and Speech
Omnilingual SONAR Team, João Maria Janeiro, Pere-Lluís Huguet Cabot +17
Cross-lingual sentence encoders typically cover only a few hundred languages and often trade downstream quality for stronger alignment, limiting their adoption. We introduce OmniSO…
Self-Refining Video Sampling
Sangwon Jang, Taekyung Ki, Jaehyeong Jo +3
Modern video generators still struggle with complex physical dynamics, often falling short of physical realism. Existing approaches address this using external verifiers or additio…
Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation
Taekyung Ki, Sangwon Jang, Jaehyeong Jo +2
Talking head generation creates lifelike avatars from static portraits for virtual communication and content creation. However, current models do not yet convey the feeling of trul…
Frame Guidance: Training-Free Guidance for Frame-Level Control in Video Diffusion Models
Sangwon Jang, Taekyung Ki, Jaehyeong Jo +4
Advancements in diffusion models have significantly improved video quality, directing attention to fine-grained controllability. However, many existing methods depend on fine-tunin…
Silent Branding Attack: Trigger-free Data Poisoning Attack on Text-to-Image Diffusion Models
Sangwon Jang, June Suk Choi, Jaehyeong Jo +2
Text-to-image diffusion models have achieved remarkable success in generating high-quality contents from text prompts. However, their reliance on publicly available data and the gr…
Continuous Diffusion Model for Language Modeling
Jaehyeong Jo, Sung Ju Hwang
Diffusion models have emerged as a promising alternative to autoregressive models in modeling discrete categorical data. However, diffusion models that directly work on discrete da…
Identity Decoupling for Multi-Subject Personalization of Text-to-Image Models
Sangwon Jang, Jaehyeong Jo, Kimin Lee +1
Text-to-image diffusion models have shown remarkable success in generating personalized subjects based on a few reference images. However, current methods often fail when generatin…
Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes
Jaehyeong Jo, Sung Ju Hwang
Learning the distribution of data on Riemannian manifolds is crucial for modeling data from non-Euclidean space, which is required by many applications in diverse scientific fields…
DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Models
Sohyun An, Hayeon Lee, Jaehyeong Jo +2
Existing NAS methods suffer from either an excessive amount of time for repetitive sampling and training of many task-irrelevant architectures. To tackle such limitations of existi…
Text-Conditioned Sampling Framework for Text-to-Image Generation with Masked Generative Models
Jaewoong Lee, Sangwon Jang, Jaehyeong Jo +5
Token-based masked generative models are gaining popularity for their fast inference time with parallel decoding. While recent token-based approaches achieve competitive performanc…
Graph Generation with Diffusion Mixture
Jaehyeong Jo, Dongki Kim, Sung Ju Hwang
Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures. Although diffusion models have achie…
Exploring Chemical Space with Score-based Out-of-distribution Generation
Seul Lee, Jaehyeong Jo, Sung Ju Hwang
A well-known limitation of existing molecular generative models is that the generated molecules highly resemble those in the training set. To generate truly novel molecules that ma…
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
Jaehyeong Jo, Seul Lee, Sung Ju Hwang
Generating graph-structured data requires learning the underlying distribution of graphs. Yet, this is a challenging problem, and the previous graph generative methods either fail…
Edge Representation Learning with Hypergraphs
Jaehyeong Jo, Jinheon Baek, Seul Lee +3
Graph neural networks have recently achieved remarkable success in representing graph-structured data, with rapid progress in both the node embedding and graph pooling methods. Yet…