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Found 81 papers, 23 papers with code
RoadFormer+: Delivering RGB-X Scene Parsing through Scale-Aware Information Decoupling and Advanced Heterogeneous Feature Fusion
These decoupled features are then fused through a dual-branch multi-scale heterogeneous feature fusion block, which employs parallel Transformer attentions and convolutional neural network modules to merge multi-scale features across different scales and receptive fields.
Ranked #1 on
Semantic Segmentation
on ZJU-RGB-P
TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework
The major contributions of this study lie in comprehensively tightening the coupling between semantic segmentation and stereo matching.
SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint Learning
Specifically, a confidence-aware feature flow estimator is proposed to acquire 2D feature positional translations and their associated confidence levels.
Ranked #2 on
Camera Pose Estimation
on KITTI Odometry Benchmark
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation
The second novelty arises from the observation that there exists an explicit, deducible relationship between optical flow divergence and depth gradient.
OverlapMamba: Novel Shift State Space Model for LiDAR-based Place Recognition
In a novel way, we employ a stochastic reconstruction approach to build shift state space models, compressing the visual representation.
Online,Target-Free LiDAR-Camera Extrinsic Calibration via Cross-Modal Mask Matching
The cornerstone of our framework and toolbox is the cross-modal mask matching (C3M) algorithm, developed based on a state-of-the-art (SoTA) LVM and capable of generating sufficient and reliable matches.
ODFormer: Semantic Fundus Image Segmentation Using Transformer for Optic Nerve Head Detection
However, the significant discrepancy between fundus image datasets, each generated using a single type of fundus camera, poses challenges to the generalizability of ONH detection approaches developed based on semantic segmentation networks.
Playing to Vision Foundation Model's Strengths in Stereo Matching
We believe this new paradigm will pave the way for the next generation of stereo matching networks.
HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature Fusion
In this study, we take one step toward this new research area by exploring a feasible strategy to fully exploit VFM features for RGB-thermal scene parsing.
Ranked #1 on
Thermal Image Segmentation
on KP day-night
LIX: Implicitly Infusing Spatial Geometric Prior Knowledge into Visual Semantic Segmentation for Autonomous Driving
We introduce the Learning to Infuse "X" (LIX) framework, with novel contributions in both logit distillation and feature distillation aspects.
Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation
Taking into account the practical applicability of our method in real-world robotics applications, we also propose a novel patch descriptor distillation strategy to further reduce the computational complexity of correspondence matching.
SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection
Feature-fusion networks with duplex encoders have proven to be an effective technique to solve the freespace detection problem.
YOLO-Ant: A Lightweight Detector via Depthwise Separable Convolutional and Large Kernel Design for Antenna Interference Source Detection
In this article, an antenna dataset is created to address important antenna interference source detection issues and serves as the basis for subsequent research.
MF-MOS: A Motion-Focused Model for Moving Object Segmentation
Moving object segmentation (MOS) provides a reliable solution for detecting traffic participants and thus is of great interest in the autonomous driving field.
UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image Restoration
Subsequently, a generative adversarial network is trained to restore the corrupted regions by leveraging the semantic context learned from surrounding uncorrupted regions.
S$^3$M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving
Hence, in this article, we introduce S$^3$M-Net, a novel joint learning framework developed to perform semantic segmentation and stereo matching simultaneously.
Towards Effective Multi-Moving-Camera Tracking: A New Dataset and Lightweight Link Model
Moreover, to alleviate the impact of the image style variations caused by different cameras, a color transfer module is effectively incorporated to extract cross-camera consistent appearance features for pedestrian association across moving cameras for ICT, resulting in a much improved MTMMC tracking system, which can constitute a step further towards coordinated mining of multiple moving cameras.
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity
Recent research demonstrates that GNNs are vulnerable to the model stealing attack, a nefarious endeavor geared towards duplicating the target model via query permissions.
Model Stealing Attack against Recommender System
Some adversarial attacks have achieved model stealing attacks against recommender systems, to some extent, by collecting abundant training data of the target model (target data) or making a mass of queries.
Three-Filters-to-Normal+: Revisiting Discontinuity Discrimination in Depth-to-Normal Translation
This article introduces three-filters-to-normal+ (3F2N+), an extension of our previous work three-filters-to-normal (3F2N), with a specific focus on incorporating discontinuity discrimination capability into surface normal estimators (SNEs).
Dive Deeper into Rectifying Homography for Stereo Camera Online Self-Calibration
Instead, we introduce four new evaluation metrics to quantify the robustness and accuracy of extrinsic parameter estimation, applicable to both single-pair and multi-pair cases.
RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing
Additionally, we release SYN-UDTIRI, the first large-scale road scene parsing dataset that contains over 10, 407 RGB images, dense depth images, and the corresponding pixel-level annotations for both freespace and road defects of different shapes and sizes.
Ranked #2 on
Semantic Segmentation
on SYN-UDTIRI
E3CM: Epipolar-Constrained Cascade Correspondence Matching
Accurate and robust correspondence matching is of utmost importance for various 3D computer vision tasks.
Freespace Optical Flow Modeling for Automated Driving
Additionally, our proposed freespace optical flow model boasts a diverse array of applications within the realm of automated driving, providing a geometric constraint in freespace detection, vehicle localization, and more.
Self-attention Dual Embedding for Graphs with Heterophily
In this work, we design a novel GNN which is effective for both heterophilic and homophilic graphs.
Ranked #8 on
Node Classification
on Actor
D2NT: A High-Performing Depth-to-Normal Translator
Surface normal holds significant importance in visual environmental perception, serving as a source of rich geometric information.
UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite
To foster advancements in this burgeoning field, we have launched an online open-source benchmark suite, referred to as UDTIRI.
Temporal Segment Transformer for Action Segmentation
To deal with these issues, we propose an attention based approach which we call \textit{temporal segment transformer}, for joint segment relation modeling and denoising.
Risk-Aware Bid Optimization for Online Display Advertisement
This research focuses on the bid optimization problem in the real-time bidding setting for online display advertisements, where an advertiser, or the advertiser's agent, has access to the features of the website visitor and the type of ad slots, to decide the optimal bid prices given a predetermined total advertisement budget.
SIM2E: Benchmarking the Group Equivariant Capability of Correspondence Matching Algorithms
The experimental results demonstrate the importance of group equivariant algorithms for correspondence matching on various sim(2) transformation conditions.
SDA-SNE: Spatial Discontinuity-Aware Surface Normal Estimation via Multi-Directional Dynamic Programming
The state-of-the-art (SoTA) surface normal estimators (SNEs) generally translate depth images into surface normal maps in an end-to-end fashion.
Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms
Computer vision algorithms have been prevalently utilized for 3-D road imaging and pothole detection for over two decades.
Computer-Aided Road Inspection: Systems and Algorithms
Road damage is an inconvenience and a safety hazard, severely affecting vehicle condition, driving comfort, and traffic safety.
DeepDrug: A General Graph-Based Deep Learning Framework for Drug Relation Prediction
Computational approaches for accurate predictions of drug-related interactions such as drug-drug interactions (DDIs) and drug-target interactions (DTIs) are highly demanding for biochemical researchers due to their efficiency and cost-effectiveness.
Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection
This further reduces the semantic gap between different feature channel layers.
Bone Marrow Cell Recognition: Training Deep Object Detection with A New Loss Function
Therefore, it is crucial to study a robust bone marrow cell detection algorithm for a quantitative automatic analysis system.
Robust Edge-Direct Visual Odometry based on CNN edge detection and Shi-Tomasi corner optimization
This solution used CNN edge detection and Shi-Tomasi corner optimization to extract information from the image.
Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms
Existing road pothole detection approaches can be classified as computer vision-based or machine learning-based.
MFuseNet: Robust Depth Estimation with Learned Multiscopic Fusion
We design a multiscopic vision system that utilizes a low-cost monocular RGB camera to acquire accurate depth estimation.
SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection
In particular, SNE-RoadSeg, our previously proposed method based on a surface normal estimator (SNE) and a data-fusion DCNN (RoadSeg), has achieved impressive performance in freespace detection.
SCV-Stereo: Learning Stereo Matching from a Sparse Cost Volume
Convolutional neural network (CNN)-based stereo matching approaches generally require a dense cost volume (DCV) for disparity estimation.
Co-Teaching: An Ark to Unsupervised Stereo Matching
Stereo matching is a key component of autonomous driving perception.
SAS: A Simple, Accurate and Scalable Node Classification Algorithm
Recent works have sought to address this problem using a two-stage approach, which first aggregates data along graph edges, then trains a classifier without using additional graph information.
End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving
With the recent advancement of deep learning technology, data-driven approaches for autonomous car prediction and planning have achieved extraordinary performance.
PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching
Supervised learning with deep convolutional neural networks (DCNNs) has seen huge adoption in stereo matching.
Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms
Therefore, in this paper, we first build a drivable area and road anomaly detection benchmark for ground mobile robots, evaluating the existing state-of-the-art single-modal and data-fusion semantic segmentation CNNs using six modalities of visual features.
Predictive Quantile Regression with Mixed Roots and Increasing Dimensions: The ALQR Approach
In this paper we propose the adaptive lasso for predictive quantile regression (ALQR).
Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation
This paper presents an efficient pothole detection algorithm based on road disparity map estimation and segmentation.
Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation
Therefore, this paper mainly explores an effective training data augmentation approach that can be employed to improve the overall DCNN performance, when additional images captured from different views are available.
One-Vote Veto: Semi-Supervised Learning for Low-Shot Glaucoma Diagnosis
Our proposed multi-task Siamese network (MTSN) can employ any backbone CNN, and we demonstrate with four backbone CNNs that its accuracy with limited training data approaches the accuracy of backbone CNNs trained with a dataset that is 50 times larger.
Computer Stereo Vision for Autonomous Driving
As an important component of autonomous systems, autonomous car perception has had a big leap with recent advances in parallel computing architectures.
Conditional Link Prediction of Category-Implicit Keypoint Detection
Keypoints of objects reflect their concise abstractions, while the corresponding connection links (CL) build the skeleton by detecting the intrinsic relations between keypoints.
CoT-AMFlow: Adaptive Modulation Network with Co-Teaching Strategy for Unsupervised Optical Flow Estimation
The interpretation of ego motion and scene change is a fundamental task for mobile robots.
Loop-box: Multi-Agent Direct SLAM Triggered by Single Loop Closure for Large-Scale Mapping
We develop a generic method for the keychallenging scenarios in multi-agent 3D mapping based on different camera systems.
Agile Reactive Navigation for A Non-Holonomic Mobile Robot Using A Pixel Processor Array
Experimental results demonstrate that the algorithm's ability to enable a ground vehicle to navigate at an average speed of 2. 20 m/s for passing through multiple gates and 3. 88 m/s for a 'slalom' task in an environment featuring significant visual clutter.
Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots
Our NIM can be deployed in existing convolutional neural networks (CNNs) to refine the segmentation performance.
SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection
Freespace detection is an essential component of visual perception for self-driving cars.
Ranked #5 on
Semantic Segmentation
on SYN-UDTIRI
ATG-PVD: Ticketing Parking Violations on A Drone
In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD).
We Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation
The experimental results demonstrate that, firstly, the transformed disparity (or inverse depth) images become more informative; secondly, AA-UNet and AA-RTFNet, our best performing implementations, respectively outperform all other state-of-the-art single-modal and data-fusion networks for road pothole detection; and finally, the training set augmentation technique based on adversarial domain adaptation not only improves the accuracy of the state-of-the-art semantic segmentation networks, but also accelerates their convergence.
Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator
To evaluate the performance of our proposed SNE, we created three large-scale synthetic datasets (easy, medium and hard) using 24 3D mesh models, each of which is used to generate 1800--2500 pairs of depth images (resolution: 480X640 pixels) and the corresponding ground-truth surface normal maps from different views.
Active Perception with A Monocular Camera for Multiscopic Vision
We design a multiscopic vision system that utilizes a low-cost monocular RGB camera to acquire accurate depth estimation for robotic applications.
Robust Lane Marking Detection Algorithm Using Drivable Area Segmentation and Extended SLT
In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road area is segmented.
PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation
This linear model is then utilized to reduce the redundant information in the left and right road images.
A Robust Pavement Mapping System Based on Normal-Constrained Stereo Visual Odometry
Pavement condition is crucial for civil infrastructure maintenance.
Road Damage Detection Based on Unsupervised Disparity Map Segmentation
This paper presents a novel road damage detection algorithm based on unsupervised disparity map segmentation.
GQ-Net: Training Quantization-Friendly Deep Networks
Network quantization is a model compression and acceleration technique that has become essential to neural network deployment.
Pothole Detection Based on Disparity Transformation and Road Surface Modeling
Furthermore, the pothole detection accuracy is still far from satisfactory.
Key Ingredients of Self-Driving Cars
Over the past decade, many research articles have been published in the area of autonomous driving.
A Robust Roll Angle Estimation Algorithm Based on Gradient Descent
This paper presents a robust roll angle estimation algorithm, which is developed from our previously published work, where the roll angle was estimated from a dense disparity map by minimizing a global energy using golden section search algorithm.
A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces
Multiple lidars are prevalently used on mobile vehicles for rendering a broad view to enhance the performance of localization and perception systems.
Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding
In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation.
Real-Time Dense Stereo Embedded in A UAV for Road Inspection
This makes the damaged road areas more distinguishable from the road surface.
Adaptive Power System Emergency Control using Deep Reinforcement Learning
Power system emergency control is generally regarded as the last safety net for grid security and resiliency.
Using DP Towards A Shortest Path Problem-Related Application
We apply the designed model and proposed an algorithm for detecting lanes by formulating it as the shortest path problem.
Multiple Lane Detection Algorithm Based on Optimised Dense Disparity Map Estimation
Lane detection is very important for self-driving vehicles.
A Novel Disparity Transformation Algorithm for Road Segmentation
The transformation is achieved using two parameters: roll angle and fitted disparity value with respect to each row.
Real-Time Stereo Vision for Road Surface 3-D Reconstruction
In this paper, we present a real-time stereo vision system used for road surface 3-D reconstruction.
Real-time stereo vision-based lane detection system
The proposed system in this paper is developed from our previous work where the dense vanishing point Vp is estimated globally to assist the detection of multiple curved lane markings.
Real-Time Subpixel Fast Bilateral Stereo
Stereo vision technique has been widely used in robotic systems to acquire 3-D information.
Road surface 3d reconstruction based on dense subpixel disparity map estimation
To achieve the millimetre accuracy required for road condition assessment, a disparity map with subpixel resolution needs to be used.
Sentiment Analysis based on User Tag for Traditional Chinese Medicine in Weibo
To the best of our knowledge, the present work is the first attempt that applies sentiment analysis to the domain of TCM on Sina Weibo (a twitter-like microblogging service in China).
Cultural Vocal Bursts Intensity Prediction
Sentiment Analysis
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