Search Results for author:
Found 18 papers, 3 papers with code
Learning zeroth class dictionary for human action recognition
In this paper, a discriminative two-phase dictionary learning framework is proposed for classifying human action by sparse shape representations, in which the first-phase dictionary is learned on the selected discriminative frames and the second-phase dictionary is built for recognition using reconstruction errors of the first-phase dictionary as input features.
RGB Video Based Tennis Action Recognition Using a Deep Historical Long Short-Term Memory
Action recognition has attracted increasing attention from RGB input in computer vision partially due to potential applications on somatic simulation and statistics of sport such as virtual tennis game and tennis techniques and tactics analysis by video.
Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages
Our approach is extended from a basic monolingual STS framework to a shared multilingual encoder pretrained with translation task to incorporate rich-resource language data.
Joint Multi-User DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence
With the assistance of edge servers, user equipments (UEs) are able to run deep neural network (DNN) based AI applications, which are generally resource-hungry and compute-intensive, such that an individual UE can hardly afford by itself in real time.
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation
On Task 1b development data set, we achieve an accuracy of 96. 7\% with a model size smaller than 500KB.
Hamming OCR: A Locality Sensitive Hashing Neural Network for Scene Text Recognition
Thus, we propose a lightweight scene text recognition model named Hamming OCR.
Learning Graph Normalization for Graph Neural Networks
We conduct extensive experiments on benchmark datasets for different tasks, including node classification, link prediction, graph classification and graph regression, and confirm that the learned graph normalization leads to competitive results and that the learned weights suggest the appropriate normalization techniques for the specific task.
A Two-Stage Approach to Device-Robust Acoustic Scene Classification
To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed.
Ranked #1 on
Acoustic Scene Classification
on TAU Urban Acoustic Scenes 2019
(using extra training data)
SGMNet: Learning Rotation-Invariant Point Cloud Representations via Sorted Gram Matrix
Recently, various works that attempted to introduce rotation invariance to point cloud analysis have devised point-pair features, such as angles and distances.
PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Table Image Recognition to Latex
This paper presents our solution for the ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX.
CenterAtt: Fast 2-stage Center Attention Network
In this technical report, we introduce the methods of HIKVISION_LiDAR_Det in the challenge of waymo open dataset real-time 3D detection.
Visual-Semantic Transformer for Scene Text Recognition
In this work, we propose to model semantic and visual information jointly with a Visual-Semantic Transformer (VST).
Auto-MLM: Improved Contrastive Learning for Self-supervised Multi-lingual Knowledge Retrieval
Unexpectedly, MLM ignores the sentence-level training, and CL also neglects extraction of the internal info from the query.
PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking
This paper describes the PASH participation in TREC 2021 Deep Learning Track.
HCL: Improving Graph Representation with Hierarchical Contrastive Learning
Contrastive learning has emerged as a powerful tool for graph representation learning.
Stability-based Generalization Analysis for Mixtures of Pointwise and Pairwise Learning
Recently, some mixture algorithms of pointwise and pairwise learning (PPL) have been formulated by employing the hybrid error metric of "pointwise loss + pairwise loss" and have shown empirical effectiveness on feature selection, ranking and recommendation tasks.
Filter Pruning via Filters Similarity in Consecutive Layers
Filter pruning is widely adopted to compress and accelerate the Convolutional Neural Networks (CNNs), but most previous works ignore the relationship between filters and channels in different layers.
