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Found 18 papers, 4 papers with code
A Joint Model for Graph-based Chinese Dependency Parsing
The experimental results on the Penn Chinese treebank (CTB5) show that our proposed joint model improved by 0. 38% on dependency parsing than the model of Yan et al. (2019).
LFP: Efficient and Accurate End-to-End Lane-Level Planning via Camera-LiDAR Fusion
For performance, the lane-level cross-modal query integration and feature enhancement module uses confidence score from ROI to combine low-confidence image queries with LiDAR queries, extracting complementary depth features.
DualSep: A Light-weight dual-encoder convolutional recurrent network for real-time in-car speech separation
Our proposed system supports both streaming and non-streaming modes.
Perception Helps Planning: Facilitating Multi-Stage Lane-Level Integration via Double-Edge Structures
In this work, we endeavor to integrate the perception of these elements into the planning task.
Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System
This paper explores multi-scenario optimization on large platforms using multi-agent reinforcement learning (MARL).
Research on Driver Facial Fatigue Detection Based on Yolov8 Model
In a society where traffic accidents frequently occur, fatigue driving has emerged as a grave issue.
Research on Image Recognition Technology Based on Multimodal Deep Learning
This project investigates the human multi-modal behavior identification algorithm utilizing deep neural networks.
CalibFormer: A Transformer-based Automatic LiDAR-Camera Calibration Network
The fusion of LiDARs and cameras has been increasingly adopted in autonomous driving for perception tasks.
EdgeCalib: Multi-Frame Weighted Edge Features for Automatic Targetless LiDAR-Camera Calibration
The edge features, which are prevalent in various environments, are aligned in both images and point clouds to determine the extrinsic parameters.
USTC FLICAR: A Sensors Fusion Dataset of LiDAR-Inertial-Camera for Heavy-duty Autonomous Aerial Work Robots
In this paper, we present the USTC FLICAR Dataset, which is dedicated to the development of simultaneous localization and mapping and precise 3D reconstruction of the workspace for heavy-duty autonomous aerial work robots.
Decomposed Prototype Learning for Few-Shot Scene Graph Generation
However, current methods for FSSGG are hindered by the high intra-class variance of predicate categories in SGG: On one hand, each predicate category commonly has multiple semantic meanings under different contexts.
Standoff Tracking Using DNN-Based MPC with Implementation on FPGA
This work studies the standoff tracking problem to drive an unmanned aerial vehicle (UAV) to slide on a desired circle over a moving target at a constant height.
Rethinking the Evaluation of Unbiased Scene Graph Generation
Current Scene Graph Generation (SGG) methods tend to predict frequent predicate categories and fail to recognize rare ones due to the severe imbalanced distribution of predicates.
Integrating Object-aware and Interaction-aware Knowledge for Weakly Supervised Scene Graph Generation
However, we argue that most existing WSSGG works only focus on object-consistency, which means the grounded regions should have the same object category label as text entities.
A deep learning method based on patchwise training for reconstructing temperature field
To solve the problem, this work proposes a novel deep learning method based on patchwise training to reconstruct the temperature field of electronic equipment accurately from limited observation.
Unified Style Transfer
Currently, it is hard to compare and evaluate different style transfer algorithms due to chaotic definitions of style and the absence of agreed objective validation methods in the study of style transfer.
An Infrared Communication System based on Handstand Pendulum
In this system, 940nm infrared light is mainly used for audio signal transmission, and an handstand pendulum based on PID is used to control the angle and stability of infrared light emission.
Hierarchical Fashion Graph Network for Personalized Outfit Recommendation
Fashion outfit recommendation has attracted increasing attentions from online shopping services and fashion communities. Distinct from other scenarios (e. g., social networking or content sharing) which recommend a single item (e. g., a friend or picture) to a user, outfit recommendation predicts user preference on a set of well-matched fashion items. Hence, performing high-quality personalized outfit recommendation should satisfy two requirements -- 1) the nice compatibility of fashion items and 2) the consistence with user preference.
