Search Results for author:
Found 12 papers, 0 papers with code
Deep Learning Approaches for Human Action Recognition in Video Data
The results of this study underscore the potential of composite models in achieving robust human action recognition and suggest avenues for future research in optimizing these models for real-world deployment.
Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach
This study addressed the complex task of sentiment analysis on a dataset of 119, 988 original tweets from Weibo using a Convolutional Neural Network (CNN), offering a new approach to Natural Language Processing (NLP).
LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network
Due to the difficulty of acquiring large-scale 3D human keypoint annotation, previous methods for 3D human pose estimation (HPE) have often relied on 2D image features and sequential 2D annotations.
Producing a Standard Dataset of Speed Climbing Training Videos Using Deep Learning Techniques
This dissertation presents a methodology for recording speed climbing training sessions with multiple cameras and annotating the videos with relevant data, including body position, hand and foot placement, and timing.
LiDARFormer: A Unified Transformer-based Multi-task Network for LiDAR Perception
The proposed LiDARFormer utilizes cross-space global contextual feature information and exploits cross-task synergy to boost the performance of LiDAR perception tasks across multiple large-scale datasets and benchmarks.
Research on The Cultivation Path of Craftsman Spirit in Higher Vocational Education Based on Survey Data
With the development of China's economy and society, the importance of "craftsman's spirit" has become more and more prominent.
Research on College Students' Innovation and Entrepreneurship Education from The Perspective of Artificial Intelligence Knowledge-Based Crowdsourcing
Based on the practical process of innovation and entrepreneurship education for college students in the author's university, this study analyzes and deconstructs the key concepts of AI knowledge-based crowdsourcing on the basis of literature research, and analyzes the objective fitting needs of combining AI knowledge-based crowdsourcing with college students' innovation and entrepreneurship education practice through a survey and research of a random sample of college students, and verifies that college students' knowledge and application of AI knowledge-based crowdsourcing in the learning and practice of innovation and entrepreneurship The study also verifies the awareness and application of AI knowledge-based crowdsourcing knowledge by university students in the learning and practice of innovation and entrepreneurship.
LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception
LidarMultiNet is extensively tested on both Waymo Open Dataset and nuScenes dataset, demonstrating for the first time that major LiDAR perception tasks can be unified in a single strong network that is trained end-to-end and achieves state-of-the-art performance.
LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network
This technical report presents the 1st place winning solution for the Waymo Open Dataset 3D semantic segmentation challenge 2022.
EICA Team at SemEval-2018 Task 2: Semantic and Metadata-based Features for Multilingual Emoji Prediction
The advent of social media has brought along a novel way of communication where meaning is composed by combining short text messages and visual enhancements, the so-called emojis.
EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering
In the main Subtask C, our primary submission was ranked fourth, with a MAP of 13. 48 and accuracy of 97. 08.
EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification
This paper describes our approach for SemEval-2017 Task 4 - Sentiment Analysis in Twitter (SAT).
