10 papers
NAB: Neural Adaptive Binning for Sparse-View CT reconstruction
Wangduo Xie, Matthew B. Blaschko
Computed Tomography (CT) plays a vital role in inspecting the internal structures of industrial objects. Furthermore, achieving high-quality CT reconstruction from sparse views is…
Guided Model Merging for Hybrid Data Learning: Leveraging Centralized Data to Refine Decentralized Models
Junyi Zhu, Ruicong Yao, Taha Ceritli +6
Current network training paradigms primarily focus on either centralized or decentralized data regimes. However, in practice, data availability often exhibits a hybrid nature, wher…
Balancing Multimodal Training Through Game-Theoretic Regularization
Konstantinos Kontras, Thomas Strypsteen, Christos Chatzichristos +3
Multimodal learning holds promise for richer information extraction by capturing dependencies across data sources. Yet, current training methods often underperform due to modality…
Diversity-Driven View Subset Selection for Indoor Novel View Synthesis
Zehao Wang, Han Zhou, Matthew B. Blaschko +2
Novel view synthesis of indoor scenes can be achieved by capturing a monocular video sequence of the environment. However, redundant information caused by artificial movements in t…
ChromaFormer: A Scalable and Accurate Transformer Architecture for Land Cover Classification
Mingshi Li, Dusan Grujicic, Ben Somers +3
Remote sensing imagery from systems such as Sentinel provides full coverage of the Earth's surface at around 10-meter resolution. The remote sensing community has transitioned to e…
Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better
Enshu Liu, Junyi Zhu, Zinan Lin +8
Diffusion Models (DM) and Consistency Models (CM) are two types of popular generative models with good generation quality on various tasks. When training DM and CM, intermediate we…