Search Results for author:
Found 13 papers, 8 papers with code
PointPWC-Net: Cost Volume on Point Clouds for (Self-)Supervised Scene Flow Estimation
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.
$C^2$AV-TSE: Context and Confidence-aware Audio Visual Target Speaker Extraction
Audio-Visual Target Speaker Extraction (AV-TSE) aims to mimic the human ability to enhance auditory perception using visual cues.
Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions
Recent advancements in large language models (LLMs) have revolutionized various domains, bringing significant progress and new opportunities.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
MetaSeg: Content-Aware Meta-Net for Omni-Supervised Semantic Segmentation
Noisy labels, inevitably existing in pseudo segmentation labels generated from weak object-level annotations, severely hampers model optimization for semantic segmentation.
BrainZ-BP: A Non-invasive Cuff-less Blood Pressure Estimation Approach Leveraging Brain Bio-impedance and Electrocardiogram
Clinically, treatment for patients with traumatic brain injuries (TBI) requires monitoring the ICP and BP of patients simultaneously.
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification
Federated Learning (FL) systems are susceptible to adversarial attacks, where malicious clients submit poisoned models to disrupt the convergence or plant backdoors that cause the global model to misclassify some samples.
FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
This paper introduces FedSecurity, an end-to-end benchmark that serves as a supplementary component of the FedML library for simulating adversarial attacks and corresponding defense mechanisms in Federated Learning (FL).
PointConvFormer: Revenge of the Point-based Convolution
Hence, we preserved the invariances from point convolution, whereas attention helps to select relevant points in the neighborhood for convolution.
The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions
This paper investigates different variants of PointConv, a convolution network on point clouds, to examine their robustness to input scale and rotation changes.
UVMBench: A Comprehensive Benchmark Suite for Researching Unified Virtual Memory in GPUs
The recent introduction of Unified Virtual Memory (UVM) in GPUs offers a new programming model that allows GPUs and CPUs to share the same virtual memory space, shifts the complex memory management from programmers to GPU driver/ hardware, and enables kernel execution even when memory is oversubscribed.
Hardware Architecture
PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds
We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on 3D point clouds in a coarse-to-fine fashion.
Visualizing Point Cloud Classifiers by Curvature Smoothing
In this paper, we propose a novel approach to visualize features important to the point cloud classifiers.
PointConv: Deep Convolutional Networks on 3D Point Clouds
Besides, our experiments converting CIFAR-10 into a point cloud showed that networks built on PointConv can match the performance of convolutional networks in 2D images of a similar structure.
Ranked #2 on
3D Part Segmentation
on IntrA
