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Found 53 papers, 24 papers with code
MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning
In this work, we propose MasHost, a Reinforcement Learning (RL)-based framework for autonomous and query-adaptive Mas design.
HRMedSeg: Unlocking High-resolution Medical Image segmentation via Memory-efficient Attention Modeling
To address this limitation, we propose a memory-efficient framework for high-resolution medical image segmentation, called HRMedSeg.
DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model
In this paper, we propose DenseFormer, a novel method that integrates the diffusion model into the depth completion task.
EmoHead: Emotional Talking Head via Manipulating Semantic Expression Parameters
Generating emotion-specific talking head videos from audio input is an important and complex challenge for human-machine interaction.
RS2AD: End-to-End Autonomous Driving Data Generation from Roadside Sensor Observations
To the best of our knowledge, this is the first approach to reconstruct vehicle-mounted LiDAR data from roadside sensor inputs.
Robust Nonlinear Data-Driven Predictive Control for Mixed Vehicle Platoons via Koopman Operator and Reachability Analysis
Mixed vehicle platoons, comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), hold significant potential for enhancing traffic performance.
CFFormer: Cross CNN-Transformer Channel Attention and Spatial Feature Fusion for Improved Segmentation of Low Quality Medical Images
However, most research tends to focus on integrating the spatial features of CNNs and Transformers, while overlooking the critical importance of channel features.
V2X-DGPE: Addressing Domain Gaps and Pose Errors for Robust Collaborative 3D Object Detection
V2X-DGPE employs a Knowledge Distillation Framework and a Feature Compensation Module to learn domain-invariant representations from multi-source data, effectively reducing the feature distribution gap between vehicles and roadside infrastructure.
CocoER: Aligning Multi-Level Feature by Competition and Coordination for Emotion Recognition
Although it is non-trivial to deduce a person's emotion by integrating multi-level feature (head, body and context), the emotion recognition results of each level is usually different from one another, which creates inconsistency in the prevailing feature alignment method and decrease recognition performance.
Conformal Symplectic Optimization for Stable Reinforcement Learning
Notably, RAD achieves up to a 155. 1% performance improvement over ADAM in Atari games, showcasing its efficacy in stabilizing and accelerating RL training.
Robust Data-Driven Predictive Control for Mixed Platoons under Noise and Attacks
This leads to a robust data-driven predictive control framework, solved in a tube-based control manner.
EDRF: Enhanced Driving Risk Field Based on Multimodal Trajectory Prediction and Its Applications
However, the probability of events occurring is often difficult to predict due to the uncertainty of drivers' or vehicles' behavior.
Vision-Driven 2D Supervised Fine-Tuning Framework for Bird's Eye View Perception
Visual bird's eye view (BEV) perception, due to its excellent perceptual capabilities, is progressively replacing costly LiDAR-based perception systems, especially in the realm of urban intelligent driving.
NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation
To alleviate the labor-intensive requirement of manual prompts, we introduce a Gaussian-Kernel Prompt Encoder (GKP-Encoder) to generate density maps driven by a single point, which guides segmentation predictions by mixing position prompts and semantic prompts.
ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image Segmentation
Finally, we design the Query-Decoupled Modality Decoder (QDMD) that leverages a one-to-one strategy to provide an independent decoding channel for every modality.
OE-BevSeg: An Object Informed and Environment Aware Multimodal Framework for Bird's-eye-view Vehicle Semantic Segmentation
Based on prior knowledge about the main composition of the BEV surrounding environment varying with the increase of distance intervals, long-sequence global modeling is utilized to improve the model's understanding and perception of the environment.
Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving
Perception is essential for autonomous driving system.
Emotion Loss Attacking: Adversarial Attack Perception for Skeleton based on Multi-dimensional Features
What's more, we are the first to prove the effectiveness of emotional features, and provide a new idea for measuring the distance between skeletal motions.
Reinforced Knowledge Distillation for Time Series Regression
As one of the most popular and effective methods in model compression, knowledge distillation (KD) attempts to transfer knowledge from single or multiple large-scale networks (i. e., Teachers) to a compact network (i. e., Student).
LLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices
To address this issue, we propose Knowledge Pruning (KP), a novel paradigm for time series learning in this paper.
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks
To address these challenges, researchers have developed various model compression techniques such as model quantization and model pruning.
Improve Knowledge Distillation via Label Revision and Data Selection
In addition to the supervision of ground truth, the vanilla KD method regards the predictions of the teacher as soft labels to supervise the training of the student model.
UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images
Moreover, to unleash the generalization capability of SAM across a variety of nuclei images, we devise a Domain-adaptive Tuning Encoder (DT-Encoder) to seamlessly harmonize visual features with domain-common and domain-specific knowledge, and further devise a Domain Query-enhanced Decoder (DQ-Decoder) by leveraging learnable domain queries for segmentation decoding in different nuclei domains.
VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
Learning a human-like driving policy from large-scale driving demonstrations is promising, but the uncertainty and non-deterministic nature of planning make it challenging.
Ranked #27 on
NavSim
on OpenScene
Robust Data-EnablEd Predictive Leading Cruise Control via Reachability Analysis
Data-driven predictive control promises model-free wave-dampening strategies for Connected and Autonomous Vehicles (CAVs) in mixed traffic flow.
Cloud Control of Connected Vehicle under Bi-directional Time-varying delay: An Application of Predictor-observer Structured Controller
This article is devoted to addressing the cloud control of connected vehicles, specifically focusing on analyzing the effect of bi-directional communication-induced delays.
Information Flow Topology in Mixed Traffic: A Comparative Study between "Looking Ahead" and "Looking Behind"
In mixed traffic where human-driven vehicles (HDVs) also exist, existing research mostly focuses on "looking ahead" (i. e., the CAVs receive information from preceding vehicles) strategies for CAVs, while recent work reveals that "looking behind" (i. e., the CAVs receive information from their rear vehicles) strategies might provide more possibilities for CAV longitudinal control.
SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation
Compared to the segment anything model, SPPNet shows roughly 20 times faster inference, with 1/70 parameters and computational cost.
Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data
More specifically, a feature-domain discriminator is employed to align teacher's and student's representations for universal knowledge transfer.
DualAttNet: Synergistic Fusion of Image-level and Fine-Grained Disease Attention for Multi-Label Lesion Detection in Chest X-rays
However, such networks are difficult to focus on lesion regions in chest X-rays due to their high resemblance in vision.
Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges
The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade.
Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge
To this end, we propose a novel approach for empathetic response generation, which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker's situation.
Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps
However, obtaining bi-phase images for the same area is difficult, and complex post-processing methods are required to update the existing databases. To solve these problems, we proposed a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications; in this approach, historical road information was fused with the latest images to directly obtain the latest state of the road. Considering that the texture of a road is complex, a multi-branch network, named the Map Encoding Branch (MEB) was proposed for representation learning, where the Boundary Enhancement Module (BEM) was used to improve the accuracy of boundary prediction, and the Residual Refinement Module (RRM) was used to optimize the prediction results.
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation.
Ranked #28 on
Bench2Drive
on Bench2Drive
Weakly Supervised Learning of Semantic Correspondence through Cascaded Online Correspondence Refinement
Following the spirit of multiple instance learning (MIL), we decompose the weakly supervised correspondence learning problem into three stages: image-level matching, region-level matching, and pixel-level matching.
Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow
This algorithm achieves both computation and communication efficiency, as well as trajectory data privacy, through parallel calculation.
M$^2$DQN: A Robust Method for Accelerating Deep Q-learning Network
Deep Q-learning Network (DQN) is a successful way which combines reinforcement learning with deep neural networks and leads to a widespread application of reinforcement learning.
Implementation and Experimental Validation of Data-Driven Predictive Control for Dissipating Stop-and-Go Waves in Mixed Traffic
In this paper, we present the first experimental results of data-driven predictive control for connected and autonomous vehicles (CAVs) in dissipating traffic waves.
DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow
For the control of connected and autonomous vehicles (CAVs), most existing methods focus on model-based strategies.
DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation
Deep learning architecture with convolutional neural network (CNN) achieves outstanding success in the field of computer vision.
Ranked #1 on
Medical Image Segmentation
on SegPC-2021
Experimental Validation of Multi-lane Formation Control for Connected and Automated Vehicles in Multiple Scenarios
Formation control methods of connected and automated vehicles have been proposed to smoothly switch the structure of vehicular formations in different scenarios.
Multi-vehicle experiment platform: A Digital Twin Realization Method
To confirm effectiveness of this method, a prototype system is developed, which consists of sand table testbed, its twin system and cloud.
Data-Driven Predictive Control for Connected and Autonomous Vehicles in Mixed Traffic
In this paper, instead of relying on a parametric car-following model, we introduce a data-driven predictive control strategy to achieve safe and optimal control for CAVs in mixed traffic.
Multi-lane Unsignalized Intersection Cooperation with Flexible Lane Direction based on Multi-vehicle Formation Control
Unsignalized intersection cooperation of connected and automated vehicles (CAVs) is able to eliminate green time loss of signalized intersections and improve traffic efficiency.
An Automatic Nuclei Image Segmentation Based on Multi-Scale Split-Attention U-Net
Nuclei segmentation is an important step in the task of medical image analysis.
Formation Control for Connected and Automated Vehicles on Multi-lane Roads: Relative Motion Planning and Conflict Resolution
Multi-vehicle coordinated decision making and control can improve traffic efficiency while guaranteeing driving safety.
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral Imagery
To be specific, we develop a new Feedback Structure and a Local-Global Spectral Block to alleviate the difficulty in spatial and spectral feature extraction.
Disease Prediction with a Maximum Entropy Method
In this paper, we propose a maximum entropy method for predicting disease risks.
Efficient erbium-doped thin-film lithium niobate waveguide amplifiers
Lithium niobate on insulator (LNOI) is an emerging photonic platform with great promises for future optical communications, nonlinear optics and microwave photonics.
Optics Applied Physics
Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stability
Most existing strategies for CAVs' longitudinal control focus on downstream traffic conditions, but neglect the impact of CAVs' behaviors on upstream traffic flow.
Nonlinear Monte Carlo Method for Imbalanced Data Learning
and the mean value of loss function is used as the empirical risk by Law of Large Numbers (LLN).
Leading Cruise Control in Mixed Traffic Flow
Numerical studies confirm the potential of LCC to strengthen the capability of CAVs in suppressing traffic instabilities and smoothing traffic flow.
Systems and Control Systems and Control Optimization and Control
Nonlinear Regression without i.i.d. Assumption
In this paper, we consider a class of nonlinear regression problems without the assumption of being independent and identically distributed.
