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Found 25 papers, 8 papers with code
Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Network
Moreover, we introduce the Dynamic Object Mask-aware Event Labeling (DOMEL) approach for generating approximate ground-truth labels for event-based motion segmentation datasets.
Accurate and Efficient Urban Street Tree Inventory with Deep Learning on Mobile Phone Imagery
By enhancing the accuracy and efficiency of tree inventory, our model empowers urban management to mitigate the adverse effects of deforestation and climate change.
3D-TexSeg: Unsupervised Segmentation of 3D Texture using Mutual Transformer Learning
Analysis of the 3D Texture is indispensable for various tasks, such as retrieval, segmentation, classification, and inspection of sculptures, knitted fabrics, and biological tissues.
Learning Spatial-Temporal Regularized Tensor Sparse RPCA for Background Subtraction
Robust principal component analysis has been identified as a promising unsupervised paradigm for background subtraction tasks in the last decade thanks to its competitive performance in a number of benchmark datasets.
Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement
The method has resulted in a significant performance improvement, of up to 5. 0% AUC, of state-of-the-art (SOTA) visual trackers.
EFaR 2023: Efficient Face Recognition Competition
To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size.
Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification
There has been a surge of research in deep learning models for WSI classification with clinical applications such as cancer detection or prediction of molecular mutations from WSIs.
GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations
The development of deep learning-based biometric models that can be deployed on devices with constrained memory and computational resources has proven to be a significant challenge.
Ranked #1 on
Face Recognition
on CFP-FF
DFR-FastMOT: Detection Failure Resistant Tracker for Fast Multi-Object Tracking Based on Sensor Fusion
The proposed solution enables superior performance under various distortion levels in detection over current state-of-art methods.
Higher-order Sparse Convolutions in Graph Neural Networks
Graph Neural Networks (GNNs) have been applied to many problems in computer sciences.
Person Monitoring by Full Body Tracking in Uniform Crowd Environment
The dataset was generated in four different scenarios where mainly the target was moving alongside the crowd, sometimes occluding with them, and other times the camera's view of the target is blocked by the crowd for a short period.
Learning Branched Fusion and Orthogonal Projection for Face-Voice Association
In addition, we leverage cross-modal verification and matching tasks to analyze the impact of multiple languages on face-voice association.
Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos
Moving Object Detection (MOD) is a fundamental step for many computer vision applications.
Robot Person Following in Uniform Crowd Environment
We present a new robot person tracking system with a new RGB-D tracker, Deep Tracking with RGB-D (DTRD) that is resilient to tricky challenges introduced by the uniform crowd environment.
Real-Time Face Recognition System
Over the past few decades, interest in algorithms for face recognition has been growing rapidly and has even surpassed human-level performance.
Underwater Image Enhancement Using Pre-trained Transformer
The goal of this work is to apply a denoising image transformer to remove the distortion from underwater images and compare it with other similar approaches.
Neuromorphic Camera Denoising using Graph Neural Network-driven Transformers
Compared to existing solutions, qualitative results verified the superior capability of the proposed algorithm to eliminate noise while preserving meaningful scene events.
Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook
Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems.
Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends
In order to overcome the drawbacks of the existing benchmarks, a new benchmark Object Tracking and Temple Color (OTTC) has also been proposed and used in the evaluation of different algorithms.
Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation
Currently, the top current background subtraction methods in CDnet 2014 are based on deep neural networks with a large gap of performance in comparison on the conventional unsupervised approaches based on multi-features or multi-cues strategies.
Unsupervised RGBD Video Object Segmentation Using GANs
To handle these challenges we propose a fusion based moving object segmentation algorithm which exploits color as well as depth information using GAN to achieve more accuracy.
Unsupervised Deep Context Prediction for Background Foreground Separation
Furthermore we also evaluated foreground object detection with the fusion of our proposed method and morphological operations.
Comparative Study of ECO and CFNet Trackers in Noisy Environment
Object tracking is one of the most challenging task and has secured significant attention of computer vision researchers in the past two decades.
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery
The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning or robust PCA (RPCA).
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset
In this context, this work aims to initiate a rigorous and comprehensive review of the similar problem formulations in robust subspace learning and tracking based on decomposition into low-rank plus additive matrices for testing and ranking existing algorithms for background/foreground separation.
