Search Results for author:
Found 49 papers, 29 papers with code
EfficientMFD: Towards More Efficient Multimodal Synchronous Fusion Detection
Multimodal image fusion and object detection play a vital role in autonomous driving.
Theoretically Achieving Continuous Representation of Oriented Bounding Boxes
Considerable efforts have been devoted to Oriented Object Detection (OOD).
Target Speaker Extraction by Directly Exploiting Contextual Information in the Time-Frequency Domain
These weighting matrices reflect the similarity among different frames of the T-F representations and are further employed to obtain the consistent T-F representations of the enrollment.
Auto MC-Reward: Automated Dense Reward Design with Large Language Models for Minecraft
Many reinforcement learning environments (e. g., Minecraft) provide only sparse rewards that indicate task completion or failure with binary values.
Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors
Existing scene text detection methods typically rely on extensive real data for training.
ADT: Agent-based Dynamic Thresholding for Anomaly Detection
In this paper, we model thresholding in anomaly detection as a Markov Decision Process and propose an agent-based dynamic thresholding (ADT) framework based on a deep Q-network.
PointOBB: Learning Oriented Object Detection via Single Point Supervision
Single point-supervised object detection is gaining attention due to its cost-effectiveness.
Point2RBox: Combine Knowledge from Synthetic Visual Patterns for End-to-end Oriented Object Detection with Single Point Supervision
To our best knowledge, Point2RBox is the first end-to-end solution for point-supervised OOD.
P2RBox: A Single Point is All You Need for Oriented Object Detection
In this study, we introduce the P2RBox network, which leverages point annotations and a mask generator to create mask proposals, followed by filtration through our Inspector Module and Constrainer Module.
Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning
In this paper, we aim to develop open-vocabulary object detection (OVD) technique in aerial images that scales up object vocabulary size beyond training data.
An Efficient Virtual Data Generation Method for Reducing Communication in Federated Learning
Then the server aggregates all the proxy datasets to form a central dummy dataset, which is used to finetune aggregated global model.
An Efficient and Multi-private Key Secure Aggregation for Federated Learning
With the emergence of privacy leaks in federated learning, secure aggregation protocols that mainly adopt either homomorphic encryption or threshold secret sharing have been widely developed for federated learning to protect the privacy of the local training data of each client.
InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language
Different from existing interactive systems that rely on pure language, by incorporating pointing instructions, the proposed iGPT significantly improves the efficiency of communication between users and chatbots, as well as the accuracy of chatbots in vision-centric tasks, especially in complicated visual scenarios where the number of objects is greater than 2.
ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection
Existing oriented object detection methods commonly use metric AP$_{50}$ to measure the performance of the model.
PatchDCT: Patch Refinement for High Quality Instance Segmentation
To further refine masks obtained by compressed vectors, we propose for the first time a compressed vector based multi-stage refinement framework.
Self-supervised Character-to-Character Distillation for Text Recognition
Therefore, exploring the robust text feature representations on unlabeled real images by self-supervised learning is a good solution.
H2RBox: Horizontal Box Annotation is All You Need for Oriented Object Detection
Oriented object detection emerges in many applications from aerial images to autonomous driving, while many existing detection benchmarks are annotated with horizontal bounding box only which is also less costive than fine-grained rotated box, leading to a gap between the readily available training corpus and the rising demand for oriented object detection.
Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization
Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects and an additional rotation angle parameter is used for rotated objects.
Black-box Dataset Ownership Verification via Backdoor Watermarking
The rapid development of DNNs has benefited from the existence of some high-quality datasets ($e. g.$, ImageNet), which allow researchers and developers to easily verify the performance of their methods.
G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection
To go further, in this paper, we propose a unified Gaussian representation called G-Rep to construct Gaussian distributions for OBB, QBB, and PointSet, which achieves a unified solution to various representations and problems.
MMRotate: A Rotated Object Detection Benchmark using PyTorch
We present an open-source toolbox, named MMRotate, which provides a coherent algorithm framework of training, inferring, and evaluation for the popular rotated object detection algorithm based on deep learning.
Embedding Recurrent Layers with Dual-Path Strategy in a Variant of Convolutional Network for Speaker-Independent Speech Separation
Various network architectures, from traditional convolutional neural network (CNN) and recurrent neural network (RNN) to advanced transformer, have been designed sophistically to improve separation performance.
Self-supervised Implicit Glyph Attention for Text Recognition
Supervised attention can alleviate the above issue, but it is character category-specific, which requires extra laborious character-level bounding box annotations and would be memory-intensive when handling languages with larger character categories.
Ranked #2 on
Scene Text Recognition
on ICDAR 2003
The KFIoU Loss for Rotated Object Detection
This is in contrast to recent Gaussian modeling based rotation detectors e. g. GWD loss and KLD loss that involve a human-specified distribution distance metric which require additional hyperparameter tuning that vary across datasets and detectors.
Dual-Path Image Inpainting With Auxiliary GAN Inversion
Different from feed-forward methods, they seek for a closest latent code to the corrupted image and feed it to a pretrained generator.
AlphaRotate: A Rotation Detection Benchmark using TensorFlow
AlphaRotate is an open-source Tensorflow benchmark for performing scalable rotation detection on various datasets.
RSDet++: Point-based Modulated Loss for More Accurate Rotated Object Detection
We classify the discontinuity of loss in both five-param and eight-param rotated object detection methods as rotation sensitivity error (RSE) which will result in performance degeneration.
An adaptive Origin-Destination flows cluster-detecting method to identify urban mobility trends
As an important spatial analysis approach, the clustering methods of point events have been extended to OD flows to identify the dominant trends and spatial structures of urban mobility.
Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence
Taking the perspective that horizontal detection is a special case for rotated object detection, in this paper, we are motivated to change the design of rotation regression loss from induction paradigm to deduction methodology, in terms of the relation between rotation and horizontal detection.
Ranked #14 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss
In this paper, we propose a Representation Invariance Loss (RIL) to optimize the bounding box regression for the rotating objects.
Ranked #27 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design.
Ranked #16 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Parallel Multi-Resolution Fusion Network for Image Inpainting
Conventional deep image inpainting methods are based on auto-encoder architecture, in which the spatial details of images will be lost in the down-sampling process, leading to the degradation of generated results.
Dense Label Encoding for Boundary Discontinuity Free Rotation Detection
Rotation detection serves as a fundamental building block in many visual applications involving aerial image, scene text, and face etc.
Ranked #29 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning
Interpretability and effectiveness are two essential and indispensable requirements for adopting machine learning methods in reality.
SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing
Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects.
Ranked #33 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited
For the resulting circularly distributed angle classification problem, we first devise a Circular Smooth Label technique to handle the periodicity of angle and increase the error tolerance to adjacent angles.
Ranked #37 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning
However, the strong defence ability and high learning accuracy of these schemes cannot be ensured at the same time, which will impede the wide application of FL in practice (especially for medical or financial institutions that require both high accuracy and strong privacy guarantee).
Learning Modulated Loss for Rotated Object Detection
Popular rotated detection methods usually use five parameters (coordinates of the central point, width, height, and rotation angle) to describe the rotated bounding box and l1-loss as the loss function.
Ranked #43 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model
In recent years, building change detection methods have made great progress by introducing deep learning, but they still suffer from the problem of the extracted features not being discriminative enough, resulting in incomplete regions and irregular boundaries.
Building change detection for remote sensing images
Change Detection
+3
R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
Considering the shortcoming of feature misalignment in existing refined single-stage detector, we design a feature refinement module to improve detection performance by getting more accurate features.
Rethinking Classification and Localization for Cascade R-CNN
We extend the state-of-the-art Cascade R-CNN with a simple feature sharing mechanism.
Comparison Network for One-Shot Conditional Object Detection
A two-stage detector for OSCD is introduced to compare the extracted query and target features with the learnable metric to approach the optimized non-linear conditional probability.
Multinomial Random Forest: Toward Consistency and Privacy-Preservation
Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood.
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
Specifically, a sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects.
Ranked #47 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network
The complexity of application scenarios, the redundancy of detection region, and the difficulty of dense ship detection are all the main obstacles that limit the successful operation of traditional methods in ship detection.
Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall.
Sequence-based Multimodal Apprenticeship Learning For Robot Perception and Decision Making
Apprenticeship learning has recently attracted a wide attention due to its capability of allowing robots to learn physical tasks directly from demonstrations provided by human experts.
Simultaneous Feature and Body-Part Learning for Real-Time Robot Awareness of Human Behaviors
We formulate FABL as a regression-like optimization problem with structured sparsity-inducing norms to model interrelationships of body parts and features.
Enforcing Template Representability and Temporal Consistency for Adaptive Sparse Tracking
Sparse representation has been widely studied in visual tracking, which has shown promising tracking performance.
