16 citations · 27 across the 13 of their papers we have counts for
14 papers
Noise-Space Reward Alignment for Few-Step Generative Models
Woocheol Shin, Taeyoung Yun, Jiwon Sung +7
Few-step generative models produce high-quality samples in one or a few evaluations. However, steering them toward desired properties remains difficult, especially when only black-…
Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
RuiKang OuYang, Hanlin Yu, Xinyue Ai +7
Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, t…
Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models
Xinyue Ai, Yutong He, Albert Gu +4
Log-likelihood evaluation enables important capabilities in generative models, including model comparison, certain fine-tuning objectives, and many downstream applications. Yet par…
Accelerating Diffusion Planners in Offline RL via Reward-Aware Consistency Trajectory Distillation
Xintong Duan, Yutong He, Fahim Tajwar +3
Although diffusion models have achieved strong results in decision-making tasks, their slow inference speed remains a key limitation. While consistency models offer a potential sol…
Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge
Yudi Sang, Yanzhen Liu, Sutuke Yibulayimu +33
The segmentation of pelvic fracture fragments in CT and X-ray images is crucial for trauma diagnosis, surgical planning, and intraoperative guidance. However, accurately and effici…
State Combinatorial Generalization In Decision Making With Conditional Diffusion Models
Xintong Duan, Yutong He, Fahim Tajwar +3
Many real-world decision-making problems are combinatorial in nature, where states (e.g., surrounding traffic of a self-driving car) can be seen as a combination of basic elements…
Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion
Michail Dontas, Yutong He, Naoki Murata +3
This paper considers blind inverse image restoration, the task of predicting a target image from a degraded source when the degradation (i.e. the forward operator) is unknown. Exis…
Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation
Yutong He, Alexander Robey, Naoki Murata +7
Prompt engineering is an effective but labor-intensive way to control text-to-image (T2I) generative models. Its time-intensive nature and complexity have spurred the development o…
Weather Prediction with Diffusion Guided by Realistic Forecast Processes
Zhanxiang Hua, Yutong He, Chengqian Ma +1
Weather forecasting remains a crucial yet challenging domain, where recently developed models based on deep learning (DL) have approached the performance of traditional numerical w…
Manifold Preserving Guided Diffusion
Yutong He, Naoki Murata, Chieh-Hsin Lai +8
Despite the recent advancements, conditional image generation still faces challenges of cost, generalizability, and the need for task-specific training. In this paper, we propose M…
Towards reporting bias in visual-language datasets: bimodal augmentation by decoupling object-attribute association
Qiyu Wu, Mengjie Zhao, Yutong He +4
Reporting bias arises when people assume that some knowledge is universally understood and hence, do not necessitate explicit elaboration. In this paper, we focus on the wide exist…
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion
Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao +6
Consistency Models (CM) (Song et al., 2023) accelerate score-based diffusion model sampling at the cost of sample quality but lack a natural way to trade-off quality for speed. To…
Localized Text-to-Image Generation for Free via Cross Attention Control
Yutong He, Ruslan Salakhutdinov, J. Zico Kolter
Despite the tremendous success in text-to-image generative models, localized text-to-image generation (that is, generating objects or features at specific locations in an image whi…
Neural Generation Meets Real People: Building a Social, Informative Open-Domain Dialogue Agent
Ethan A. Chi, Ashwin Paranjape, Abigail See +18
We present Chirpy Cardinal, an open-domain social chatbot. Aiming to be both informative and conversational, our bot chats with users in an authentic, emotionally intelligent way.…