Search Results for author:
Found 9 papers, 3 papers with code
CLAPSep: Leveraging Contrastive Pre-trained Models for Multi-Modal Query-Conditioned Target Sound Extraction
Such systems consist of two components: a query network that converts user queries into conditional embeddings, and a separation network that extracts the target sound based on conditional embeddings.
Ranked #1 on
Target Sound Extraction
on AudioSet
MVPatch: More Vivid Patch for Adversarial Camouflaged Attacks on Object Detectors in the Physical World
Our method employs a model-perception-based approach that reduces the object confidence scores of several object detectors to boost the transferability of adversarial patches.
Extending Whisper with prompt tuning to target-speaker ASR
Target-speaker automatic speech recognition (ASR) aims to transcribe the desired speech of a target speaker from multi-talker overlapped utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Nonlinear Kernel Support Vector Machine with 0-1 Soft Margin Loss
Recent advance on linear support vector machine with the 0-1 soft margin loss ($L_{0/1}$-SVM) shows that the 0-1 loss problem can be solved directly.
Mathematical modeling of the vascular system
In this article, we review the mathematical modeling for the vascular system.
A continuum and computational framework for viscoelastodynamics: finite deformation linear models
Based on the consistent modeling framework, a provably energy stable numerical scheme is constructed for incompressible viscohyperelasticity using inf-sup stable elements.
Numerical Analysis Computational Engineering, Finance, and Science Numerical Analysis
Non-Cooperative Game Theory Based Rate Adaptation for Dynamic Video Streaming over HTTP
The proposed algorithm is proxy-free.
A Comprehensive Study and Comparison of Core Technologies for MPEG 3D Point Cloud Compression
Point cloud based 3D visual representation is becoming popular due to its ability to exhibit the real world in a more comprehensive and immersive way.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
