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Found 27 papers, 7 papers with code
近四十年湘方言语音研究的回顾与展望——基于知识图谱绘制和文献计量分析(Review and Prospect of the Phonetic Research of Xiang Dialects in Recent Forty Years:Based on Knowledge Mapping and Bibliometric Analysis)
“湘方言语音研究已取得丰硕的研究成果, 本文以中国知网中的“学术期刊库”为数据来源, 采用文献计量分析的方法, 通过CiteSpace等工具, 从发文信息、聚类分析及演进趋势等维度对相关文献进行统计分析和可视化知识图谱绘制, 全方位考察近四十年的研究概貌, 提出“类型学”、“语音层次”将会是湘方言语音研究较新的、有待进一步开拓的领域, 为今后开拓新的研究方向提供理论依据, 为湖南语保工程的资源进行深度开发利用提供数据支撑。”
Improving the Multi-label Atomic Activity Recognition by Robust Visual Feature and Advanced Attention @ ROAD++ Atomic Activity Recognition 2024
In the multi-label atomic activity recognition task, the robustness of visual feature extraction remains a key challenge, which directly affects the model performance and generalization ability.
SoccerNet 2024 Challenges Results
The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team.
Cross-Modal Denoising: A Novel Training Paradigm for Enhancing Speech-Image Retrieval
To address this issue, we introduce an effective framework and a novel learning task named cross-modal denoising (CMD) to enhance cross-modal interaction to achieve finer-level cross-modal alignment.
PVUW 2024 Challenge on Complex Video Understanding: Methods and Results
Moreover, we provide a new motion expression guided video segmentation dataset MeViS to study the natural language-guided video understanding in complex environments.
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
Compass: A Decentralized Scheduler for Latency-Sensitive ML Workflows
We consider ML query processing in distributed systems where GPU-enabled workers coordinate to execute complex queries: a computing style often seen in applications that interact with users in support of image processing and natural language processing.
Reference-dependent asset pricing with a stochastic consumption-dividend ratio
First, we consider a general model in which the agent's preferences include both contemporaneous gain-loss utility, that is, utility from the difference between current consumption and previously held expectations about current consumption, and prospective gain-loss utility, that is, utility from the difference between intertemporal beliefs about future consumption.
Towards Efficient Verification of Quantized Neural Networks
Quantization replaces floating point arithmetic with integer arithmetic in deep neural network models, providing more efficient on-device inference with less power and memory.
Cascade: A Platform for Delay-Sensitive Edge Intelligence
Interactive intelligent computing applications are increasingly prevalent, creating a need for AI/ML platforms optimized to reduce per-event latency while maintaining high throughput and efficient resource management.
The Robust Semantic Segmentation UNCV2023 Challenge Results
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
Enhancing the Unified Streaming and Non-streaming Model with Contrastive Learning
Specifically, the top-layer hidden representation at the same frame of the streaming and non-streaming modes are regarded as a positive pair, encouraging the representation of the streaming mode close to its non-streaming counterpart.
FreConv: Frequency Branch-and-Integration Convolutional Networks
In the high-frequency branch, a derivative-filter-like architecture is designed to extract the high-frequency information while a light extractor is employed in the low-frequency branch because the low-frequency information is usually redundant.
HUSP-SP: Faster Utility Mining on Sequence Data
High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity.
Improving CTC-based ASR Models with Gated Interlayer Collaboration
The CTC-based automatic speech recognition (ASR) models without the external language model usually lack the capacity to model conditional dependencies and textual interactions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Multi-Level Modeling Units for End-to-End Mandarin Speech Recognition
Thus only considering the writing of Chinese characters as modeling units is insufficient to capture speech features.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Transformers Meet Visual Learning Understanding: A Comprehensive Review
Three image tasks and two video tasks of computer vision are investigated.
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement
Our attack technique targets the inherent vulnerabilities of NLP models, allowing us to generate samples even without interacting with the victim NLP model, as long as it is based on pre-trained language models (PLMs).
A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking
In this paper, we focus on how to utilize the language understanding and generation ability of pre-trained language models for DST.
Quantifying Robustness to Adversarial Word Substitutions
Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations.
BBS-KWS:The Mandarin Keyword Spotting System Won the Video Keyword Wakeup Challenge
We propose a mandarin keyword spotting system (KWS) with several novel and effective improvements, including a big backbone (B) model, a keyword biasing (B) mechanism and the introduction of syllable modeling units (S).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
ε-weakened Robustness of Deep Neural Networks
This paper introduces a notation of $\varepsilon$-weakened robustness for analyzing the reliability and stability of deep neural networks (DNNs).
Deep Transfer Learning for Identifications of Slope Surface Cracks
In this paper, a deep transfer learning framework is proposed to effectively and efficiently identify slope surface cracks for the sake of fast monitoring and early warning of geohazards such as landslides.
Learning from Shader Program Traces
We investigate this learning task for a variety of applications: our model can learn to predict a low-noise output image from shader programs that exhibit sampling noise; this model can also learn from a simplified shader program that approximates the reference solution with less computation, as well as learn the output of postprocessing filters like defocus blur and edge-aware sharpening.
Hyperparameter Optimization in Black-box Image Processing using Differentiable Proxies
We present a fully automatic system to optimize the parameters of black-box hardware and software image processing pipelines according to any arbitrary (i. e., application-specific) metric.
How to Write High-quality News on Social Network? Predicting News Quality by Mining Writing Style
SNQAM performs excellently on predicting quality, presenting interpretable quality score and giving accessible suggestions on how to improve it according to writing guidelines we referred to.
