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Found 25 papers, 5 papers with code
基于注意力的蒙古语说话人特征提取方法(Attention based Mongolian Speaker Feature Extraction)
“说话人特征提取模型提取到的说话人特征之间区分性低, 使得蒙古语声学模型无法学习到区分性信息, 导致模型无法适应不同说话人。提出一种基于注意力的说话人自适应方法, 方法引入神经图灵机进行自适应, 增加记忆模块存放说话人特征, 采用注意力机制计算记忆模块中说话人特征与当前语音说话人特征的相似权重矩阵, 通过权重矩阵重新组合成说话人特征s-vector, 进而提高说话人特征之间的区分性。在IMUT-MCT数据集上, 进行说话人特征提取方法的消融实验、模型自适应实验和案例分析。实验结果表明, 对比不同说话人特征s-vector、i-vector与d-vector, s-vector比其他两种方法的SER和WER分别降低4. 96%、1. 08%;在不同的蒙古语声学模型上进行比较, 提出的方法相对于基线均有性能提升。”
Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems
Empathetic dialogue assembles emotion understanding, feeling projection, and appropriate response generation.
Incorporating Circumstances into Narrative Event Prediction
We also introduce a regularization of attention weights to leverage the alignment between events and local circumstances.
Topological Data Mapping of Online Hate Speech, Misinformation, and General Mental Health: A Large Language Model Based Study
We then performed various machine-learning classifications based on these embeddings in order to understand the role of hate speech/misinformation in various communities.
Coordinating Supply, Demand, and Repair Resources for Optimal Postdisaster Operation of Interdependent Electric Power and Natural Gas Distribution Systems
In this paper, we focus on the interdependent electric power and natural gas distribution systems (IENDS) and propose a comprehensive "supply - demand - repair" strategy to help the IENDS tide over the emergency periods after disasters by coordinating various emergency resources.
Two-Stream Networks for Weakly-Supervised Temporal Action Localization With Semantic-Aware Mechanisms
In this paper, we hypothesize that snippets with similar representations should be considered as the same action class despite the absence of supervision signals on each snippet.
Multiple Instance Learning
Weakly Supervised Action Localization
+2
Task-Specific Data Augmentation and Inference Processing for VIPriors Instance Segmentation Challenge
And in order to make the most of visual inductive priors, we designed a task-specific inference processing strategy.
Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report
In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding
In this paper, to address these problems, we introduce a seminal knowledge prompting paradigm and further propose a knowledge-prompting-based PLM framework KP-PLM.
Strong Instance Segmentation Pipeline for MMSports Challenge
The goal of ACM MMSports2022 DeepSportRadar Instance Segmentation Challenge is to tackle the segmentation of individual humans including players, coaches and referees on a basketball court.
The Third Place Solution for CVPR2022 AVA Accessibility Vision and Autonomy Challenge
Finally, our approach achieves 63. 008\%AP@0. 50:0. 95 on the test set of CVPR2022 AVA Challenge.
The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge--Track 3: Referring Video Object Segmentation
The referring video object segmentation task (RVOS) aims to segment object instances in a given video referred by a language expression in all video frames.
Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution
Firstly, we designed a context branch based on channel splitting network with transformer to obtain sufficient context information.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering
Extractive Question Answering (EQA) is one of the most important tasks in Machine Reading Comprehension (MRC), which can be solved by fine-tuning the span selecting heads of Pre-trained Language Models (PLMs).
Efficient Progressive High Dynamic Range Image Restoration via Attention and Alignment Network
In this paper, we propose a lightweight neural network called Efficient Attention-and-alignment-guided Progressive Network (EAPNet) for the challenge NTIRE 2022 HDR Track 1 and Track 2.
The MSXF TTS System for ICASSP 2022 ADD Challenge
The end to end TTS system is VITS, and the pre-training self-supervised model is wav2vec 2. 0.
The Second Place Solution for ICCV2021 VIPriors Instance Segmentation Challenge
Finally, our approach can achieve 40. 2\%AP@0. 50:0. 95 on the test set of ICCV2021 VIPriors instance segmentation challenge.
TBN-ViT: Temporal Bilateral Network with Vision Transformer for Video Scene Parsing
Based on VSPW, we design a Temporal Bilateral Network with Vision Transformer.
Stronger Baseline for Person Re-Identification
Person re-identification (re-ID) aims to identify the same person of interest across non-overlapping capturing cameras, which plays an important role in visual surveillance applications and computer vision research areas.
PIMNet: A Parallel, Iterative and Mimicking Network for Scene Text Recognition
In this paper, we propose a Parallel, Iterative and Mimicking Network (PIMNet) to balance accuracy and efficiency.
Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection
We propose to use an MLP decoder instead of the "deconv-conv" decoder in the mask head, which alleviates the issue and promotes robustness significantly.
A Dialogue-based Information Extraction System for Medical Insurance Assessment
In the Chinese medical insurance industry, the assessor's role is essential and requires significant efforts to converse with the claimant.
Comment on "All-optical machine learning using diffractive deep neural networks"
Lin et al. (Reports, 7 September 2018, p. 1004) reported a remarkable proposal that employs a passive, strictly linear optical setup to perform pattern classifications.
