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Found 23 papers, 4 papers with code
HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy
As LMs grow in size, fine-tuning the full parameters of LMs requires a prohibitively large amount of GPU memory.
Evaluate What You Can't Evaluate: Unassessable Quality for Generated Response
There are risks in using eference-free evaluators based on LLMs to evaluate the quality of dialogue responses.
Cooperverse: A Mobile-Edge-Cloud Framework for Universal Cooperative Perception with Mixed Connectivity and Automation
A Dynamic Feature Sharing (DFS) methodology is introduced to support this CP system under certain constraints and a Random Priority Filtering (RPF) method is proposed to conduct DFS with high performance.
PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism
We investigate response generation for multi-turn dialogue in generative-based chatbots.
VINet: Lightweight, Scalable, and Heterogeneous Cooperative Perception for 3D Object Detection
Utilizing the latest advances in Artificial Intelligence (AI), the computer vision community is now witnessing an unprecedented evolution in all kinds of perception tasks, particularly in object detection.
DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection
Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms.
Continual Vision-based Reinforcement Learning with Group Symmetries
Continual reinforcement learning aims to sequentially learn a variety of tasks, retaining the ability to perform previously encountered tasks while simultaneously developing new policies for novel tasks.
A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation
Perceiving the environment is one of the most fundamental keys to enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing the safety, mobility, and sustainability issues of contemporary transportation systems.
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation
Building dialogue generation systems in a zero-shot scenario remains a huge challenge, since the typical zero-shot approaches in dialogue generation rely heavily on large-scale pre-trained language generation models such as GPT-3 and T5.
An Understanding-Oriented Robust Machine Reading Comprehension Model
In this paper, we propose an understanding-oriented machine reading comprehension model to address three kinds of robustness issues, which are over sensitivity, over stability and generalization.
PillarGrid: Deep Learning-based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDAR
3D object detection plays a fundamental role in enabling autonomous driving, which is regarded as the significant key to unlocking the bottleneck of contemporary transportation systems from the perspectives of safety, mobility, and sustainability.
Spatiotemporal Transformer Attention Network for 3D Voxel Level Joint Segmentation and Motion Prediction in Point Cloud
The current challenges of this solution are how to effectively combine different perception tasks into a single backbone and how to efficiently learn the spatiotemporal features directly from point cloud sequences.
Cyber Mobility Mirror: A Deep Learning-based Real-World Object Perception Platform Using Roadside LiDAR
Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for the next-generation transportation systems.
Deep Understanding based Multi-Document Machine Reading Comprehension
Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings.
Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Survey
Object detection plays a fundamental role in enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing safety, mobility, and sustainability issues of contemporary transportation systems.
Cyber Mobility Mirror for Enabling Cooperative Driving Automation in Mixed Traffic: A Co-Simulation Platform
In this study, a \textit{Cyber Mobility Mirror (CMM)} Co-Simulation Platform is designed for enabling CDA by providing authentic perception information.
Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Study
To advance the development of visual guidance systems, we introduce a novel vision-cloud data fusion methodology, integrating camera image and Digital Twin information from the cloud to help intelligent vehicles make better decisions.
A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training
We investigate response selection for multi-turn conversation in retrieval-based chatbots.
Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles
With the rapid development of intelligent vehicles and Advanced Driving Assistance Systems (ADAS), a mixed level of human driver engagements is involved in the transportation system.
Long-Term Prediction of Lane Change Maneuver Through a Multilayer Perceptron
Behavior prediction plays an essential role in both autonomous driving systems and Advanced Driver Assistance Systems (ADAS), since it enhances vehicle's awareness of the imminent hazards in the surrounding environment.
Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer
The best result, with a 0. 937 AUC score, was obtained with the proposed network.
TechKG: A Large-Scale Chinese Technology-Oriented Knowledge Graph
In this work, we introduce TechKG, a large scale Chinese knowledge graph that is technology-oriented.
Neural Relation Classification with Text Descriptions
State-of-the-art methods usually concentrate on building deep neural networks based classification models on the training data in which the relations of the labeled entity pairs are given.
