Search Results for author:
Found 67 papers, 33 papers with code
DF-Calib: Targetless LiDAR-Camera Calibration via Depth Flow
DF-Calib estimates a dense depth map from the camera image and completes the sparse LiDAR projected depth map, using a shared feature encoder to extract consistent depth-to-depth features, effectively bridging the 2D-3D cross-modal gap.
Mitigating the Impact of Prominent Position Shift in Drone-based RGBT Object Detection
For instance, a slight deviation of a tiny object in the thermal modality will induce it to drift from the main body of itself in the RGB modality.
Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning
Detecting oriented tiny objects, which are limited in appearance information yet prevalent in real-world applications, remains an intricate and under-explored problem.
Tiny Object Detection with Single Point Supervision
To handle label noise from scale ambiguity and location shifts in point annotations, Point Teacher employs the teacher-student architecture and decouples the learning into a two-phase denoising process.
Language hooks: a modular framework for augmenting LLM reasoning that decouples tool usage from the model and its prompt
Fine-tuning removes the need for task-specific demonstrations of tool usage at runtime; however, this ties new capabilities to a single model, thus making already-heavier setup costs a recurring expense.
Frequency-Adaptive Low-Latency Object Detection Using Events and Frames
We further propose a training strategy, Time Shift, which enforces the module to align the prediction from temporally shifted Event-RGB pairs and their original representation, that is, consistent with Event-aligned annotations.
QuadricsReg: Large-Scale Point Cloud Registration using Quadric Primitives
In the realm of large-scale point cloud registration, designing a compact symbolic representation is crucial for efficiently processing vast amounts of data, ensuring registration robustness against significant viewpoint variations and occlusions.
ChineseWebText 2.0: Large-Scale High-quality Chinese Web Text with Multi-dimensional and fine-grained information
With this approach, we release the largest, high-quality and fine-grained Chinese text ChineseWebText2. 0, which consists of 3. 8TB and each text is associated with a quality score, domain labels, a toxicity label and a toxicity score, facilitating the LLM researchers to select data based on various types of fine-grained information.
Unsupervised Multi-view UAV Image Geo-localization via Iterative Rendering
As such, our unsupervised paradigm naturally avoids the problem of region-specific overfitting, enabling generic CVGL for UAV images without feature fine-tuning or data-driven training.
Markov Chain of Thought for Efficient Mathematical Reasoning
Chain of Thought (CoT) of multi-step benefits from the logical structure of the reasoning steps and task-specific actions, significantly enhancing the mathematical reasoning capabilities of large language models.
Language Imbalance Driven Rewarding for Multilingual Self-improving
Large Language Models (LLMs) have achieved state-of-the-art performance across numerous tasks.
PersonaMath: Enhancing Math Reasoning through Persona-Driven Data Augmentation
To bridge this gap, we propose a data augmentation approach and introduce PersonaMathQA, a dataset derived from MATH and GSM8K, on which we train the PersonaMath models.
Enhancing Fine-grained Object Detection in Aerial Images via Orthogonal Mapping
This letter introduces Orthogonal Mapping (OM), a simple yet effective method aimed at addressing the challenge of semantic confusion inherent in FGOD.
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions
In addition, we find that models tuned on cross-lingual instruction following samples can follow the instruction in the output language without further tuning.
Alterations of electrocortical activity during hand movements induced by motor cortex glioma
Glioma cells can reshape functional neuronal networks by hijacking neuronal synapses, leading to partial or complete neurological dysfunction.
Detecting Every Object from Events
Object detection is critical in autonomous driving, and it is more practical yet challenging to localize objects of unknown categories: an endeavour known as Class-Agnostic Object Detection (CAOD).
Privacy Preservation by Intermittent Transmission in Cooperative LQG Control Systems
Under the given threshold of the control performance loss, a trade-off optimization problem is proposed.
ConGeo: Robust Cross-view Geo-localization across Ground View Variations
Cross-view geo-localization aims at localizing a ground-level query image by matching it to its corresponding geo-referenced aerial view.
Learning Cross-view Visual Geo-localization without Ground Truth
We observe that training on unlabeled cross-view images presents significant challenges, including the need to establish relationships within unlabeled data and reconcile view discrepancies between uncertain queries and references.
Rethinking Scale Imbalance in Semi-supervised Object Detection for Aerial Images
This paper focuses on the scale imbalance problem of semi-supervised object detection(SSOD) in aerial images.
CrossZoom: Simultaneously Motion Deblurring and Event Super-Resolving
Even though the collaboration between traditional and neuromorphic event cameras brings prosperity to frame-event based vision applications, the performance is still confined by the resolution gap crossing two modalities in both spatial and temporal domains.
QuadricsNet: Learning Concise Representation for Geometric Primitives in Point Clouds
This paper presents a novel framework to learn a concise geometric primitive representation for 3D point clouds.
Generalizing Event-Based Motion Deblurring in Real-World Scenarios
Event-based motion deblurring has shown promising results by exploiting low-latency events.
BigTranslate: Augmenting Large Language Models with Multilingual Translation Capability over 100 Languages
Second, we continue training the model with a large-scale parallel dataset that covers 102 natural languages.
Ranked #4 on
Machine Translation
on FLoRes-200
Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection
Despite the exploration of adaptive label assignment in recent oriented object detectors, the extreme geometry shape and limited feature of oriented tiny objects still induce severe mismatch and imbalance issues.
Ranked #4 on
Oriented Object Detection
on DOTA 2.0
Self-Supervised Scene Dynamic Recovery from Rolling Shutter Images and Events
Scene Dynamic Recovery (SDR) by inverting distorted Rolling Shutter (RS) images to an undistorted high frame-rate Global Shutter (GS) video is a severely ill-posed problem due to the missing temporal dynamic information in both RS intra-frame scanlines and inter-frame exposures, particularly when prior knowledge about camera/object motions is unavailable.
Recovering Continuous Scene Dynamics from A Single Blurry Image with Events
This paper aims at demystifying a single motion-blurred image with events and revealing temporally continuous scene dynamics encrypted behind motion blurs.
Learning to Super-Resolve Blurry Images with Events
Super-Resolution from a single motion Blurred image (SRB) is a severely ill-posed problem due to the joint degradation of motion blurs and low spatial resolution.
Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark
With this dataset, we consider the problem of cloud removal in high-resolution optical remote sensing imagery by integrating multi-modal and multi-resolution information.
Attention-Enhanced Cross-modal Localization Between 360 Images and Point Clouds
To overcome this limitation, we focus on correlating the information of 360 equirectangular images to point clouds, proposing an end-to-end learnable network to conduct cross-modal visual localization by establishing similarity in high-dimensional feature space.
Learning to See Through with Events
Although synthetic aperture imaging (SAI) can achieve the seeing-through effect by blurring out off-focus foreground occlusions while recovering in-focus occluded scenes from multi-view images, its performance is often deteriorated by dense occlusions and extreme lighting conditions.
Detecting Line Segments in Motion-blurred Images with Events
To robustly detect line segments over motion blurs, we propose to leverage the complementary information of images and events.
A novel method for data augmentation: Nine Dot Moving Least Square (ND-MLS)
However, the ND-MLS method has stable performance and obtains 96. 5 top-1 acc in Res-Net on 100 different handwritten character classification tasks; 2) in segmentation, under the premise of only ten original images, DeepLab obtains 93. 5%, 85%, and 73. 3% m_IOU(10) on the bottle, horse, and grass test datasets, respectively, while the cat test dataset obtains 86. 7% m_IOU(10) with the SegNet model; 3) with only 10 original images from each category in object detection, YOLO v4 obtains 100% and 97. 2% bottle and horse detection, respectively, while the cat dataset obtains 93. 6% with YOLO v3.
Motion Robust High-Speed Light-Weighted Object Detection With Event Camera
In this work, we propose a motion robust and high-speed detection pipeline which better leverages the event data.
RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection
Then, instead of assigning samples with IoU or center sampling strategy, a new Receptive Field Distance (RFD) is proposed to directly measure the similarity between the Gaussian receptive field and ground truth.
Ranked #2 on
Object Detection
on AI-TOD
Privacy Preservation by Local Design in Cooperative Networked Control Systems
In this paper, we study the privacy preservation problem in a cooperative networked control system working for the task of LQG control.
Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark
Tiny object detection (TOD) in aerial images is challenging since a tiny object only contains a few pixels.
GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion
The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover.
Ranked #3 on
Cloud Removal
on SEN12MS-CR
Learning to Extract Building Footprints from Off-Nadir Aerial Images
In this paper, we propose an offset vector learning scheme, which turns the building footprint extraction problem in off-nadir images into an instance-level joint prediction problem of the building roof and its corresponding "roof to footprint" offset vector.
Synthetic Aperture Imaging With Events and Frames
This paper addresses this problem by leveraging the merits of both events and frames, leading to a fusion-based SAI (EF-SAI) that performs consistently under the different densities of occlusions.
Understanding the Dynamics of DNNs Using Graph Modularity
Specifically, we model the process of class separation of intermediate representations in pre-trained DNNs as the evolution of communities in dynamic graphs.
Graph-Based Similarity of Neural Network Representations
Understanding the black-box representations in Deep Neural Networks (DNN) is an essential problem in deep learning.
Lidar with Velocity: Correcting Moving Objects Point Cloud Distortion from Oscillating Scanning Lidars by Fusion with Camera
Lidar point cloud distortion from moving object is an important problem in autonomous driving, and recently becomes even more demanding with the emerging of newer lidars, which feature back-and-forth scanning patterns.
A Normalized Gaussian Wasserstein Distance for Tiny Object Detection
Our key observation is that Intersection over Union (IoU) based metrics such as IoU itself and its extensions are very sensitive to the location deviation of the tiny objects, and drastically deteriorate the detection performance when used in anchor-based detectors.
Ranked #3 on
Object Detection
on AI-TOD
Motion Deblurring with Real Events
In this paper, we propose an end-to-end learning framework for event-based motion deblurring in a self-supervised manner, where real-world events are exploited to alleviate the performance degradation caused by data inconsistency.
A Survey of Uncertainty in Deep Neural Networks
Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications.
Robust wavefront dislocations of Friedel oscillations in gapped graphene
Friedel oscillation is a well-known wave phenomenon, which represents the oscillatory response of electron waves to imperfection.
Mesoscale and Nanoscale Physics
Event-based Synthetic Aperture Imaging with a Hybrid Network
Synthetic aperture imaging (SAI) is able to achieve the see through effect by blurring out the off-focus foreground occlusions and reconstructing the in-focus occluded targets from multi-view images.
Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges
In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI.
Tiny Object Detection in Aerial Images
To build a benchmark for tiny object detection in aerial images, we evaluate the state-of-the-art object detectors on our AI-TOD dataset.
Ranked #5 on
Object Detection
on AI-TOD
ULSD: Unified Line Segment Detection across Pinhole, Fisheye, and Spherical Cameras
Targeting at the unified line segment detection (ULSD) for both distorted and undistorted images, we propose to represent line segments with the Bezier curve model.
Ranked #5 on
Line Segment Detection
on wireframe dataset
(sAP10 metric)
Semantic Change Detection with Asymmetric Siamese Networks
Given two multi-temporal aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries.
Liouville type theorems and periodic solutions for the nonhomogeneous parabolic systems
In the present paper we derive Liouville type results and existence of periodic solutions for $\chi^{(2)}$ type systems with non-homogeneous nonlinearities.
Analysis of PDEs 35K9, 35J61, 35B45
Structure-Aware Network for Lane Marker Extraction with Dynamic Vision Sensor
We then propose a structure-aware network for lane marker extraction in DVS images.
Event Enhanced High-Quality Image Recovery
To recover high-quality intensity images, one should address both denoising and super-resolution problems for event cameras.
Implicit Euler ODE Networks for Single-Image Dehazing
Deep convolutional neural networks (CNN) have been applied for image dehazing tasks, where the residual network (ResNet) is often adopted as the basic component to avoid the vanishing gradient problem.
SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy
Many different schemes have been proposed for the restoration of intensity SAR images.
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID
After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation.
A Point Cloud-Based Method for Automatic Groove Detection and Trajectory Generation of Robotic Arc Welding Tasks
In this paper, in order to pursue high-efficiency robotic arc welding tasks, we propose a method based on point cloud acquired by an RGB-D sensor.
Robotics
Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences
With the pose prediction from VIO, we can efficiently obtain coarse 2D-3D line correspondences.
Semantic Change Pattern Analysis
Change detection is an important problem in vision field, especially for aerial images.
Matching Neuromorphic Events and Color Images via Adversarial Learning
The event camera has appealing properties: high dynamic range, low latency, low power consumption and low memory usage, and thus provides complementariness to conventional frame-based cameras.
Line-based Camera Pose Estimation in Point Cloud of Structured Environments
In this paper, we propose a new 2D-3D registration method to estimate 2D-3D line feature correspondences and the camera pose in untextured point clouds of structured environments.
Toward Autonomous Rotation-Aware Unmanned Aerial Grasping
Autonomous Unmanned Aerial Manipulators (UAMs) have shown promising potentials to transform passive sensing missions into active 3-dimension interactive missions, but they still suffer from some difficulties impeding their wide applications, such as target detection and stabilization.
Recent advances and opportunities in scene classification of aerial images with deep models
Scene classification is a fundamental task in interpretation of remote sensing images, and has become an active research topic in remote sensing community due to its important role in a wide range of applications.
Meaningful Objects Segmentation from SAR Images via A Multi-Scale Non-Local Active Contour Model
The segmentation of synthetic aperture radar (SAR) images is a longstanding yet challenging task, not only because of the presence of speckle, but also due to the variations of surface backscattering properties in the images.
