9 accepted papers
Action chunking enables Vision Language Action (VLA) models to run in real time, but naive chunked execution often exhibits discontinuities at chunk boundaries. Real-Time Chunking (RTC) alleviates this issue but is external to the policy, leading to spurious multimodal switching and trajectories tha…
Current fMRI decoders face a performance-fidelity trade-off where efficient ID encoders outperform geometrically-aligned surface-based models. We argue this is an artifact of inefficient surface tokenization and the failure to use anatomy as a predictive signal. We present **NeurIPS**, a framework t…
With the rapid development and widespread application of autonomous driving technology, the accurate analysis and prevention of traffic accidents have become critical challenges. However, current traffic accident datasets are often constrained by limited scale and diversity, impeding progress in thi
Diffeomorphic-based cortical surface reconstruction typically involves a series of deformation processes to extract the cerebral cortex from brain magnetic resonance images (MRI). While most methods are designed for adult brains using Neural Ordinary Differential Equations (NODE) with fixed step siz…
Recent advancements in large language models (LLMs) have revolutionized various domains, bringing significant progress and new opportunities. Despite progress in speech-related tasks, LLMs have not been sufficiently explored in multi-talker scenarios. In this work, we present a pioneering effort to…
Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder worldwide, characterized by progressive motor and non-motor symptoms. Unfortunately, there are no definitive PD modifying therapies, so accurate course prediction in advance and appropriate medical adjustment are essent…
We introduce PointConvFormer, a novel building block for point cloud based deep network architectures. Inspired by generalization theory, PointConvFormer combines ideas from point convolution, where filter weights are only based on relative position, and Transformers which utilize feature-based atte…
We propose a novel end-to-end deep scene flow model, called PointPWC-Net, that directly processes 3D point cloud scenes with large motions in a coarse-to-fine fashion. Flow computed at the coarse level is upsampled and warped to a finer level, enabling the algorithm to accommodate for large motion w…
Unlike images which are represented in regular dense grids, 3D point clouds are irregular and unordered, hence applying convolution on them can be difficult. In this paper, we extend the dynamic filter to a new convolution operation, named PointConv. PointConv can be applied on point clouds to build…