Search Results for author:
Found 42 papers, 22 papers with code
WS-DETR: Robust Water Surface Object Detection through Vision-Radar Fusion with Detection Transformer
Robust object detection for Unmanned Surface Vehicles (USVs) in complex water environments is essential for reliable navigation and operation.
CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR
Compared to linear-frequency domain or time-domain speech enhancement, the key advantage of Mel-spectrogram enhancement is that Mel-frequency presents speech in a more compact way and thus is easier to learn, which will benefit both speech quality and ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning
With the increasing prevalence of autonomous vehicles (AVs), their vulnerability to various types of attacks has grown, presenting significant security challenges.
VINP: Variational Bayesian Inference with Neural Speech Prior for Joint ASR-Effective Speech Dereverberation and Blind RIR Identification
Reverberant speech, denoting the speech signal degraded by the process of reverberation, contains crucial knowledge of both anechoic source speech and room impulse response (RIR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Unit Region Encoding: A Unified and Compact Geometry-aware Representation for Floorplan Applications
The floorplans are represented as the latent encodings on a set of boundary-adaptive unit region partition based on the clustering of the proposed geometry-aware density map.
Learning-based Detection of GPS Spoofing Attack for Quadrotors
Safety-critical cyber-physical systems (CPS), such as quadrotor UAVs, are particularly prone to cyber attacks, which can result in significant consequences if not detected promptly and accurately.
ROLO-SLAM: Rotation-Optimized LiDAR-Only SLAM in Uneven Terrain with Ground Vehicle
In this article, a LiDAR-based SLAM method is presented to improve the accuracy of pose estimations for ground vehicles in rough terrains, which is termed Rotation-Optimized LiDAR-Only (ROLO) SLAM.
Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Models
Multi-modal large language models (MLLMs) have achieved remarkable success in fine-grained visual understanding across a range of tasks.
LongSafetyBench: Long-Context LLMs Struggle with Safety Issues
With the development of large language models (LLMs), the sequence length of these models continues to increase, drawing significant attention to long-context language models.
BitStack: Any-Size Compression of Large Language Models in Variable Memory Environments
By leveraging weight decomposition, BitStack can dynamically adjust the model size with minimal transmission between running memory and storage devices.
MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time
Large Language Models (LLMs) acquire extensive knowledge and remarkable abilities from extensive text corpora, making them powerful tools for various applications.
ChemDFM-X: Towards Large Multimodal Model for Chemistry
Rapid developments of AI tools are expected to offer unprecedented assistance to the research of natural science including chemistry.
Xinyu: An Efficient LLM-based System for Commentary Generation
To address the advanced requirements, we present an argument ranking model for arguments and establish a comprehensive evidence database that includes up-to-date events and classic books, thereby strengthening the substantiation of the evidence with retrieval augmented generation (RAG) technology.
Segment-Anything Models Achieve Zero-shot Robustness in Autonomous Driving
In the task of semantic segmentation for autonomous driving, it is significant to study the zero-shot adversarial robustness of SAM.
Recent Advances in Data-driven Intelligent Control for Wireless Communication: A Comprehensive Survey
As the volume of data continues to escalate, the integration of data-driven methods has become indispensable for enabling adaptive and intelligent control mechanisms in future wireless communication systems.
UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model
In this paper, we propose UnifiedMLLM, a comprehensive model designed to represent various tasks using a unified representation.
Case2Code: Learning Inductive Reasoning with Synthetic Data
In this paper, we hope to focus on evaluating and teaching LLMs to conduct inductive reasoning, that is, LLMs are supposed to infer underlying rules by observing examples or sequential transformations.
MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results
Participants were tasked with reconstructing 3D models for 20 selected food items of varying difficulty levels: easy, medium, and hard.
Sparsity-Accelerated Training for Large Language Models
Large language models (LLMs) have demonstrated proficiency across various natural language processing (NLP) tasks but often require additional training, such as continual pre-training and supervised fine-tuning.
SpeechAlign: Aligning Speech Generation to Human Preferences
However, the integration of human feedback to align speech outputs to human preferences is often neglected.
Medical Image Synthesis via Fine-Grained Image-Text Alignment and Anatomy-Pathology Prompting
To address these challenges, we propose a novel medical image synthesis model that leverages fine-grained image-text alignment and anatomy-pathology prompts to generate highly detailed and accurate synthetic medical images.
Direct Shooting Method for Numerical Optimal Control: A Modified Transcription Approach
Direct shooting is an efficient method to solve numerical optimal control.
NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism
We present NewsBench, a novel evaluation framework to systematically assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism.
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning
These methods regularize the representation space by pulling similar sentence representations closer and pushing away the dissimilar ones and have been proven effective in various NLP tasks, e. g., semantic textual similarity (STS) tasks.
InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance
With the rapid development of large language models (LLMs), they are not only used as general-purpose AI assistants but are also customized through further fine-tuning to meet the requirements of different applications.
SpeechAgents: Human-Communication Simulation with Multi-Modal Multi-Agent Systems
In this paper, we propose SpeechAgents, a multi-modal LLM based multi-agent system designed for simulating human communication.
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning
One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task.
Watermarking LLMs with Weight Quantization
Abuse of large language models reveals high risks as large language models are being deployed at an astonishing speed.
PerturbScore: Connecting Discrete and Continuous Perturbations in NLP
Through experimental results, we find that we can build a connection between discrete and continuous perturbations and use the proposed PerturbScore to learn such correlation, surpassing previous methods used in discrete perturbation measuring.
SeqXGPT: Sentence-Level AI-Generated Text Detection
Therefore, it is important to build strong AI-generated text (AIGT) detectors.
The Uncertainty-based Retrieval Framework for Ancient Chinese CWS and POS
Automatic analysis for modern Chinese has greatly improved the accuracy of text mining in related fields, but the study of ancient Chinese is still relatively rare.
RVAE-EM: Generative speech dereverberation based on recurrent variational auto-encoder and convolutive transfer function
In this work, we propose a generative dereverberation method.
SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities
Multi-modal large language models are regarded as a crucial step towards Artificial General Intelligence (AGI) and have garnered significant interest with the emergence of ChatGPT.
Origin Tracing and Detecting of LLMs
The extraordinary performance of large language models (LLMs) heightens the importance of detecting whether the context is generated by an AI system.
Learning For Predictive Control: A Dual Gaussian Process Approach
Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data.
The Open-World Lottery Ticket Hypothesis for OOD Intent Classification
Most existing methods of Out-of-Domain (OOD) intent classification rely on extensive auxiliary OOD corpora or specific training paradigms.
EventHPE: Event-based 3D Human Pose and Shape Estimation
Event camera is an emerging imaging sensor for capturing dynamics of moving objects as events, which motivates our work in estimating 3D human pose and shape from the event signals.
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
3D Shape Segmentation via Shape Fully Convolutional Networks
3D shapes are represented as graph structures in the SFCN architecture, based on novel graph convolution and pooling operations, which are similar to convolution and pooling operations used on images.
Stochastic Collapsed Variational Inference for Sequential Data
Stochastic variational inference for collapsed models has recently been successfully applied to large scale topic modelling.
Stochastic Collapsed Variational Inference for Hidden Markov Models
In this paper, we propose a stochastic collapsed variational inference algorithm for hidden Markov models, in a sequential data setting.
