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Found 31 papers, 15 papers with code
Industrial-Grade Sensor Simulation via Gaussian Splatting: A Modular Framework for Scalable Editing and Full-Stack Validation
Then in practice, we refactor three crucial components through GS, to leverage its explicit scene representation and real-time rendering: (1) choosing the 2D neural Gaussian representation for physics-compliant scene and sensor modeling, (2) proposing a scene editing pipeline to leverage Gaussian primitives library for data augmentation, and (3) coupling a controllable diffusion model for scene expansion and harmonization.
Para-Lane: Multi-Lane Dataset Registering Parallel Scans for Benchmarking Novel View Synthesis
To evaluate end-to-end autonomous driving systems, a simulation environment based on Novel View Synthesis (NVS) techniques is essential, which synthesizes photo-realistic images and point clouds from previously recorded sequences under new vehicle poses, particularly in cross-lane scenarios.
Evaluating the Influence of Satellite Systems on Terrestrial Networks: Analyzing S-Band Interference
Given the escalating demand for spectrum, there is an ongoing global discourse on the feasibility of sharing certain frequencies currently utilized by terrestrial networks (TNs) with NTNs.
Language Driven Occupancy Prediction
To alleviate the inaccurate supervision, we propose a semantic transitive labeling pipeline to generate dense and finegrained 3D language occupancy ground truth.
StraGo: Harnessing Strategic Guidance for Prompt Optimization
Prompt engineering is pivotal for harnessing the capabilities of large language models (LLMs) across diverse applications.
AMPO: Automatic Multi-Branched Prompt Optimization
Prompt engineering is very important to enhance the performance of large language models (LLMs).
LiDAR-GS:Real-time LiDAR Re-Simulation using Gaussian Splatting
LiDAR simulation plays a crucial role in closed-loop simulation for autonomous driving.
MBDS: A Multi-Body Dynamics Simulation Dataset for Graph Networks Simulators
The datasets employed for training and evaluating physical simulation techniques are typically generated by researchers themselves, often resulting in limited data volume and quality.
Evaluating S-Band Interference: Impact of Satellite Systems on Terrestrial Networks
The co-existence of terrestrial and non-terrestrial networks (NTNs) is essential for achieving global coverage in sixth-generation cellular networks.
Convex Relaxation for Solving Large-Margin Classifiers in Hyperbolic Space
Hyperbolic spaces have increasingly been recognized for their outstanding performance in handling data with inherent hierarchical structures compared to their Euclidean counterparts.
Spectral regularization for adversarially-robust representation learning
To this end, we propose a new spectral regularizer for representation learning that encourages black-box adversarial robustness in downstream classification tasks.
Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision Transformers
The experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack, i. e., achieving 95%+ attack success rate, and also being hard to be detected and removed.
Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision Transfomers
The experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack i. e. achieving 95%+ attack success rate and also being hard to be detected and removed.
Supervised Domain Adaptation for Recognizing Retinal Diseases from Wide-Field Fundus Images
This paper addresses the emerging task of recognizing multiple retinal diseases from wide-field (WF) and ultra-wide-field (UWF) fundus images.
Neural networks learn to magnify areas near decision boundaries
This holds in deep networks trained on high-dimensional image classification tasks, and even in self-supervised representation learning.
Backdoor Defense via Suppressing Model Shortcuts
Recent studies have demonstrated that deep neural networks (DNNs) are vulnerable to backdoor attacks during the training process.
The Numerical Stability of Hyperbolic Representation Learning
Given the exponential growth of the volume of the ball w. r. t.
SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud
To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud.
Efficient and Systematic Partitioning of Large and Deep Neural Networks for Parallelization
We propose the Flex-Edge Recursive Graph and the Double Recursive Algorithm, successfully limiting our parallelization strategy generation to a linear complexity with a good quality of parallelization strategy.
Progressive Self-Guided Loss for Salient Object Detection
We present a simple yet effective progressive self-guided loss function to facilitate deep learning-based salient object detection (SOD) in images.
A Simple and Efficient Registration of 3D Point Cloud and Image Data for Indoor Mobile Mapping System
Registration of 3D LiDAR point clouds with optical images is critical in the combination of multi-source data.
A Method of Generating Measurable Panoramic Image for Indoor Mobile Measurement System
This paper designs a technique route to generate high-quality panoramic image with depth information, which involves two critical research hotspots: fusion of LiDAR and image data and image stitching.
Learning Efficient Parameter Server Synchronization Policies for Distributed SGD
We apply a reinforcement learning (RL) based approach to learning optimal synchronization policies used for Parameter Server-based distributed training of machine learning models with Stochastic Gradient Descent (SGD).
ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings
We present ClusterVO, a stereo Visual Odometry which simultaneously clusters and estimates the motion of both ego and surrounding rigid clusters/objects.
Morphing and Sampling Network for Dense Point Cloud Completion
3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community.
Ranked #8 on
Point Cloud Completion
on ShapeNet
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning
First, the framework features a practical hierarchical architecture for decomposing the stochastic gradient into its norm and normalized block gradients, and efficiently quantizes them using a uniform quantizer and a low-dimensional codebook on a Grassmann manifold, respectively.
A Dilated Inception Network for Visual Saliency Prediction
In this work, we proposed an end-to-end dilated inception network (DINet) for visual saliency prediction.
Towards Robust Curve Text Detection with Conditional Spatial Expansion
It is challenging to detect curve texts due to their irregular shapes and varying sizes.
Learning Markov Clustering Networks for Scene Text Detection
MCN predicts instance-level bounding boxes by firstly converting an image into a Stochastic Flow Graph (SFG) and then performing Markov Clustering on this graph.
Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences
Performing arts organizations aim to enrich their communities through the arts.
Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing
In this analysis, we find that lead service lines are not the only factor that is predictive of the risk of lead contamination of water.
