Search Results for author:
Found 41 papers, 17 papers with code
Enforcing Paraphrase Generation via Controllable Latent Diffusion
Paraphrase generation aims to produce high-quality and diverse utterances of a given text.
MMCert: Provable Defense against Adversarial Attacks to Multi-modal Models
Moreover, we compare our MMCert with a state-of-the-art certified defense extended from unimodal models.
Cross Pseudo-Labeling for Semi-Supervised Audio-Visual Source Localization
Audio-Visual Source Localization (AVSL) is the task of identifying specific sounding objects in the scene given audio cues.
Dual Mean-Teacher: An Unbiased Semi-Supervised Framework for Audio-Visual Source Localization
Audio-Visual Source Localization (AVSL) aims to locate sounding objects within video frames given the paired audio clips.
PoisonedRAG: Knowledge Poisoning Attacks to Retrieval-Augmented Generation of Large Language Models
We formulate knowledge poisoning attacks as an optimization problem, whose solution is a set of poisoned texts.
MAPO: Advancing Multilingual Reasoning through Multilingual Alignment-as-Preference Optimization
To enhance reasoning abilities in non-dominant languages, we propose a Multilingual-Alignment-as-Preference Optimization framework (MAPO), aiming to align the reasoning processes in other languages with the dominant language.
From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models
This paper introduces RAISE (Reasoning and Acting through Scratchpad and Examples), an advanced architecture enhancing the integration of Large Language Models (LLMs) like GPT-4 into conversational agents.
DUMA: a Dual-Mind Conversational Agent with Fast and Slow Thinking
The fast thinking model serves as the primary interface for external interactions and initial response generation, evaluating the necessity for engaging the slow thinking model based on the complexity of the complete response.
ChatHome: Development and Evaluation of a Domain-Specific Language Model for Home Renovation
This paper presents the development and evaluation of ChatHome, a domain-specific language model (DSLM) designed for the intricate field of home renovation.
Analyzing Robustness of End-to-End Neural Models for Automatic Speech Recognition
Therefore, the models we develop for various tasks should be robust to such kinds of noisy data, which led to the thriving field of robust machine learning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
DSLA: Dynamic smooth label assignment for efficient anchor-free object detection
The dynamic smooth label is assigned to supervise the classification branch.
Time Domain Adversarial Voice Conversion for ADD 2022
In this paper, we describe our speech generation system for the first Audio Deep Synthesis Detection Challenge (ADD 2022).
Audio Deep Fake Detection System with Neural Stitching for ADD 2022
This paper describes our best system and methodology for ADD 2022: The First Audio Deep Synthesis Detection Challenge\cite{Yi2022ADD}.
Role of limiting dispersal on metacommunity stability and persistence
Nevertheless, at the local scale, the spread of the inhomogeneous steady states increases up to a critical value of the limiting factor, favoring the metacommunity persistence, and then starts decreasing for further decrease in the limiting factor with varying local interaction.
GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio
This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10, 000 hours of high quality labeled audio suitable for supervised training, and 40, 000 hours of total audio suitable for semi-supervised and unsupervised training.
Ranked #1 on
Speech Recognition
on GigaSpeech
Semantic Data Augmentation for End-to-End Mandarin Speech Recognition
Augmentation related issues, such as comparison of different strategies and ratios for data combination are also investigated.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Speech SIMCLR: Combining Contrastive and Reconstruction Objective for Self-supervised Speech Representation Learning
Self-supervised visual pretraining has shown significant progress recently.
TMT: A Transformer-based Modal Translator for Improving Multimodal Sequence Representations in Audio Visual Scene-aware Dialog
Audio Visual Scene-aware Dialog (AVSD) is a task to generate responses when discussing about a given video.
DiDiSpeech: A Large Scale Mandarin Speech Corpus
This paper introduces a new open-sourced Mandarin speech corpus, called DiDiSpeech.
Audio and Speech Processing
A Further Study of Unsupervised Pre-training for Transformer Based Speech Recognition
In this paper, we conduct a further study on MPC and focus on three important aspects: the effect of pre-training data speaking style, its extension on streaming model, and how to better transfer learned knowledge from pre-training stage to downstream tasks.
A Reinforced Generation of Adversarial Examples for Neural Machine Translation
Neural machine translation systems tend to fail on less decent inputs despite its significant efficacy, which may significantly harm the credibility of this systems-fathoming how and when neural-based systems fail in such cases is critical for industrial maintenance.
TCT: A Cross-supervised Learning Method for Multimodal Sequence Representation
Multimodalities provide promising performance than unimodality in most tasks.
Cross-task pre-training for on-device acoustic scene classification
To utilize the acoustic event information to improve the performance of ASC tasks, we present the cross-task pre-training mechanism which utilizes acoustic event information from the pre-trained AED model for ASC tasks.
Improving Transformer-based Speech Recognition Using Unsupervised Pre-training
Speech recognition technologies are gaining enormous popularity in various industrial applications.
The Field-of-View Constraint of Markers for Mobile Robot with Pan-Tilt Camera
In the field of navigation and visual servo, it is common to calculate relative pose by feature points on markers, so keeping markers in camera's view is an important problem.
EPOSIT: An Absolute Pose Estimation Method for Pinhole and Fish-Eye Cameras
This paper presents a generic 6DOF camera pose estimation method, which can be used for both the pinhole camera and the fish-eye camera.
Human Following for Wheeled Robot with Monocular Pan-tilt Camera
Human following on mobile robots has witnessed significant advances due to its potentials for real-world applications.
High Performance Visual Object Tracking with Unified Convolutional Networks
In this paper, we propose an end-to-end framework to learn the convolutional features and perform the tracking process simultaneously, namely, a unified convolutional tracker (UCT).
Camera Pose Correction in SLAM Based on Bias Values of Map Points
Firstly, the relationship between the camera pose estimation error and bias values of map points is derived based on the optimized function in VSLAM.
FastPose: Towards Real-time Pose Estimation and Tracking via Scale-normalized Multi-task Networks
Given the results of MTN, we adopt an occlusion-aware Re-ID feature strategy in the pose tracking module, where pose information is utilized to infer the occlusion state to make better use of Re-ID feature.
DELTA: A DEep learning based Language Technology plAtform
In this paper we present DELTA, a deep learning based language technology platform.
Ranked #3 on
Text Classification
on Yahoo! Answers
Action Machine: Rethinking Action Recognition in Trimmed Videos
On NTU RGB-D, Action Machine achieves the state-of-the-art performance with top-1 accuracies of 97. 2% and 94. 3% on cross-view and cross-subject respectively.
Ranked #1 on
Action Recognition
on UTD-MHAD
An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes
Real-time motion detection in non-stationary scenes is a difficult task due to dynamic background, changing foreground appearance and limited computational resource.
Motion Detection
Motion Detection In Non-Stationary Scenes
+1
Optical Flow Based Online Moving Foreground Analysis
Obtained by moving object detection, the foreground mask result is unshaped and can not be directly used in most subsequent processes.
Towards End-to-End Code-Switching Speech Recognition
Code-switching speech recognition has attracted an increasing interest recently, but the need for expert linguistic knowledge has always been a big issue.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes
Real-time moving object detection in unconstrained scenes is a difficult task due to dynamic background, changing foreground appearance and limited computational resource.
A comparable study of modeling units for end-to-end Mandarin speech recognition
We find that all types of modeling units can achieve approximate character error rate (CER) in CTC model and the performance of Chinese character attention model is better than syllable attention model.
End-to-end Video-level Representation Learning for Action Recognition
From the frame/clip-level feature learning to the video-level representation building, deep learning methods in action recognition have developed rapidly in recent years.
UCT: Learning Unified Convolutional Networks for Real-time Visual Tracking
Convolutional neural networks (CNN) based tracking approaches have shown favorable performance in recent benchmarks.
End-to-end Flow Correlation Tracking with Spatial-temporal Attention
Discriminative correlation filters (DCF) with deep convolutional features have achieved favorable performance in recent tracking benchmarks.
Learning Gating ConvNet for Two-Stream based Methods in Action Recognition
For the two-stream style methods in action recognition, fusing the two streams' predictions is always by the weighted averaging scheme.
