6 papers · 1 filter
Guiding Diffusion with Deep Geometric Moments: Balancing Fidelity and Variation
Sangmin Jung, Utkarsh Nath, Yezhou Yang +5
Text-to-image generation models have achieved remarkable capabilities in synthesizing images, but often struggle to provide fine-grained control over the output. Existing guidance…
DecompDreamer: A Composition-Aware Curriculum for Structured 3D Asset Generation
Utkarsh Nath, Rajeev Goel, Rahul Khurana +5
Current text-to-3D methods excel at generating single objects but falter on compositional prompts. We argue this failure is fundamental to their optimization schedules, as simultan…
Deep Geometric Moments Promote Shape Consistency in Text-to-3D Generation
Utkarsh Nath, Rajeev Goel, Eun Som Jeon +5
To address the data scarcity associated with 3D assets, 2D-lifting techniques such as Score Distillation Sampling (SDS) have become a widely adopted practice in text-to-3D generati…
Learning Low-Rank Feature for Thorax Disease Classification
Rajeev Goel, Utkarsh Nath, Yancheng Wang +3
Deep neural networks, including Convolutional Neural Networks (CNNs) and Visual Transformers (ViT), have achieved stunning success in medical image domain. We study thorax disease…
Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks
Jinyung Hong, Eun Som Jeon, Changhoon Kim +5
Biased attributes, spuriously correlated with target labels in a dataset, can problematically lead to neural networks that learn improper shortcuts for classifications and limit th…
RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge Distillation
Utkarsh Nath, Yancheng Wang, Yingzhen Yang
Deep Neural Networks are vulnerable to adversarial attacks. Neural Architecture Search (NAS), one of the driving tools of deep neural networks, demonstrates superior performance in…