Search Results for author:
Found 34 papers, 10 papers with code
UP-SLAM: Adaptively Structured Gaussian SLAM with Uncertainty Prediction in Dynamic Environments
Recent 3D Gaussian Splatting (3DGS) techniques for Visual Simultaneous Localization and Mapping (SLAM) have significantly progressed in tracking and high-fidelity mapping.
Explicit Solution of Tunable Input-to-State Safe-Based Controller Under High-Relative-Degree Constraints
This paper investigates the safety analysis and verification of nonlinear systems subject to high-relative-degree constraints and unknown disturbance.
A Stackelberg Game Approach for Signal Temporal Logic Control Synthesis with Uncontrollable Agents
Specifically, we assume that the system controller, acting as the leader, first commits to a plan, after which the uncontrollable agents, acting as followers, take a best response based on the committed plan and their own objectives.
Channel Merging: Preserving Specialization for Merged Experts
To address the inefficiency, model merging strategies have emerged, merging all LLMs into one model to reduce the memory footprint during inference.
Formal Verification and Control with Conformal Prediction
We focus on learning-enabled autonomous systems (LEASs) in which the complexity of learning-enabled components (LECs) is a major bottleneck that hampers the use of existing model-based verification and design techniques.
Boosting Box-supervised Instance Segmentation with Pseudo Depth
This innovative approach empowers the network to simultaneously predict masks and depth, enhancing its ability to capture nuanced depth-related information during the instance segmentation process.
Conformal Predictive Programming for Chance Constrained Optimization
We propose conformal predictive programming (CPP), a framework to solve chance constrained optimization problems, i. e., optimization problems with constraints that are functions of random variables.
Signal Temporal Logic Control Synthesis among Uncontrollable Dynamic Agents with Conformal Prediction
We propose a predictive control synthesis framework that guarantees, with high probability, the satisfaction of signal temporal logic (STL) tasks that are defined over a controllable system in the presence of uncontrollable stochastic agents.
Sleep When Everything Looks Fine: Self-Triggered Monitoring for Signal Temporal Logic Tasks
Existing works on online monitoring usually assume that the monitor can acquire system information periodically at each time instant.
Diffusion-based Data Augmentation for Nuclei Image Segmentation
Therefore, augmenting a dataset with only a few labeled images to improve the segmentation performance is of significant research and application value.
Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images
Afterward, we train a separate segmentation model for each category using the images in the corresponding category.
Safe-by-Construction Autonomous Vehicle Overtaking using Control Barrier Functions and Model Predictive Control
In order to tackle the overtaking task in such challenging scenarios, we introduce a novel integrated framework tailored for vehicle overtaking maneuvers.
Improving Neural Indoor Surface Reconstruction with Mask-Guided Adaptive Consistency Constraints
3D scene reconstruction from 2D images has been a long-standing task.
Efficient STL Control Synthesis under Asynchronous Temporal Robustness Constraints
Given an ATR bound, we compute a sequence of control inputs so that the specification is satisfied by the system as long as each sub-trajectory is shifted not more than the ATR bound.
ShiftNAS: Improving One-shot NAS via Probability Shift
To address the problem of uniform sampling, we propose ShiftNAS, a method that can adjust the sampling probability based on the complexity of subnets.
Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification
The Contrastive Language-Image Pretraining (CLIP) model has been widely used in various downstream vision tasks.
LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning
To this end, we propose LoRAPrune, a new framework that delivers an accurate structured pruned model in a highly memory-efficient manner.
CrossFusion: Interleaving Cross-modal Complementation for Noise-resistant 3D Object Detection
The combination of LiDAR and camera modalities is proven to be necessary and typical for 3D object detection according to recent studies.
Model Predictive Control for Signal Temporal Logic Specifications with Time Interval Decomposition
However, instead of applying MPC directly for the entire task horizon, we decompose the STL formula into several sub-formulae with disjoint time horizons, and shrinking horizon MPC is applied for each short-horizon sub-formula iteratively.
Model Predictive Monitoring of Dynamical Systems for Signal Temporal Logic Specifications
To this end, effective approaches for the computation of feasible sets of STL formulae are provided.
MKIoU Loss: Towards Accurate Oriented Object Detection in Aerial Images
Thus, the Gaussian Angle Loss (GA Loss) is presented to solve this problem by adding a corrected loss for square targets.
Multi-subgoal Robot Navigation in Crowds with History Information and Interactions
Firstly, based on subgraph network, the history information of all agents is aggregated before encoding interactions through a graph neural network, so as to improve the ability of the robot to anticipate the future scenarios implicitly.
Trinary Tools for Continuously Valued Binary Classifiers
Machine learning practitioners must perform model selection, calibration, discretization, performance assessment, tuning, and fairness assessment.
Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy
However, the performance of architecture is limited by the type of operations and prior knowledge.
Online Monitoring of Dynamic Systems for Signal Temporal Logic Specifications with Model Information
We show that, by explicitly utilizing the model information of the dynamic system, the proposed online monitoring algorithm can falsify or certify of the specification in advance compared with existing algorithms, where no model information is used.
Real-time Rail Recognition Based on 3D Point Clouds
Accurate rail location is a crucial part in the railway support driving system for safety monitoring.
Conditional Generative Data-free Knowledge Distillation
In this paper, we propose a conditional generative data-free knowledge distillation (CGDD) framework for training lightweight networks without any training data.
Conditional Image Generation
Data-free Knowledge Distillation
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Across-Task Neural Architecture Search via Meta Learning
In this paper, an across-task neural architecture search (AT-NAS) is proposed to address the problem through combining gradient-based meta-learning with EA-based NAS to learn over the distribution of tasks.
Oriented Object Detection in Aerial Images Based on Area Ratio of Parallelogram
Oriented object detection is a challenging task in aerial images since the objects in aerial images are displayed in arbitrary directions and are frequently densely packed.
RepNAS: Searching for Efficient Re-parameterizing Blocks
However, it is still challenging to search for efficient networks due to the gap between the searched constraint and real inference time exists.
Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization
A specific view-attribute is composed by the extracted attribute feature and four view scores which are predicted by view predictor as the confidences for attribute from different views.
Effective Model Compression via Stage-wise Pruning
To overcome the unfull training, a stage-wise pruning(SWP) method is proposed, which splits a deep supernet into several stage-wise supernets to reduce the candidate number and utilize inplace distillation to supervise the stage training.
Boxer: Interactive Comparison of Classifier Results
The approach focuses on allowing the user to identify interesting subsets of training and testing instances and comparing performance of the classifiers on these subsets.
