Search Results for author:
Found 23 papers, 12 papers with code
DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset
This paper describes in detail the construction process and data statistics of DuSQL.
Physical formula enhanced multi-task learning for pharmacokinetics prediction
Overall, our work illustrates the benefits and potential of using PEMAL in AIDD and other scenarios with data scarcity and noise.
ICMC-ASR: The ICASSP 2024 In-Car Multi-Channel Automatic Speech Recognition Challenge
To promote speech processing and recognition research in driving scenarios, we build on the success of the Intelligent Cockpit Speech Recognition Challenge (ICSRC) held at ISCSLP 2022 and launch the ICASSP 2024 In-Car Multi-Channel Automatic Speech Recognition (ICMC-ASR) Challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Knowledge Enhanced Conditional Imputation for Healthcare Time-series
This study presents a novel approach to addressing the challenge of missing data in multivariate time series, with a particular focus on the complexities of healthcare data.
U2-KWS: Unified Two-pass Open-vocabulary Keyword Spotting with Keyword Bias
Open-vocabulary keyword spotting (KWS), which allows users to customize keywords, has attracted increasingly more interest.
NExT-Chat: An LMM for Chat, Detection and Segmentation
The development of large language models (LLMs) has greatly advanced the field of multimodal understanding, leading to the emergence of large multimodal models (LMMs).
Spike-Triggered Contextual Biasing for End-to-End Mandarin Speech Recognition
However, unlike shallow fusion methods that directly bias the posterior of the ASR model, deep biasing methods implicitly integrate contextual information, making it challenging to control the degree of bias.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Adaptive Contextual Biasing for Transducer Based Streaming Speech Recognition
By incorporating additional contextual information, deep biasing methods have emerged as a promising solution for speech recognition of personalized words.
Contextualized End-to-End Speech Recognition with Contextual Phrase Prediction Network
In this study, we introduce a contextual phrase prediction network for an attention-based deep bias method.
VPGTrans: Transfer Visual Prompt Generator across LLMs
While developing a new multimodal LLM (MLLM) by pre-training on tremendous image-text pairs from scratch can be exceedingly resource-consuming, connecting an existing LLM with a comparatively lightweight visual prompt generator (VPG) becomes a feasible paradigm.
The NPU-ASLP System for Audio-Visual Speech Recognition in MISP 2022 Challenge
This paper describes our NPU-ASLP system for the Audio-Visual Diarization and Recognition (AVDR) task in the Multi-modal Information based Speech Processing (MISP) 2022 Challenge.
Visually Grounded Commonsense Knowledge Acquisition
In this work, we present CLEVER, which formulates CKE as a distantly supervised multi-instance learning problem, where models learn to summarize commonsense relations from a bag of images about an entity pair without any human annotation on image instances.
Prompt Tuning for Discriminative Pre-trained Language Models
Recent works have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing (NLP) tasks.
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models
We show that PEVL enables state-of-the-art performance of detector-free VLP models on position-sensitive tasks such as referring expression comprehension and phrase grounding, and also improves the performance on position-insensitive tasks with grounded inputs.
Ranked #1 on
Visual Commonsense Reasoning
on VCR (Q-AR) test
Fine-Grained Scene Graph Generation with Data Transfer
Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in images.
Ranked #1 on
Predicate Classification
on Visual Genome
CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models
Pre-Trained Vision-Language Models (VL-PTMs) have shown promising capabilities in grounding natural language in image data, facilitating a broad variety of cross-modal tasks.
Pre-Trained Models: Past, Present and Future
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI).
RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users
In this paper, we collect a novel radar dataset that contains radar data in the form of Range-Azimuth-Doppler tensors along with the bounding boxes on the tensor for dynamic road users, category labels, and 2D bounding boxes on the Cartesian Bird-Eye-View range map.
Visual Distant Supervision for Scene Graph Generation
In this work, we propose visual distant supervision, a novel paradigm of visual relation learning, which can train scene graph models without any human-labeled data.
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing
For better distribution matching, we require that at least 80% of SQL patterns in the training data are covered by generated queries.
DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a Variational Graph Autoencoder
Graph neural networks (GNNs) achieve remarkable performance for tasks on graph data.
tau-FPL: Tolerance-Constrained Learning in Linear Time
Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications.
