Search Results for author:
Found 48 papers, 36 papers with code
Improved Crop and Weed Detection with Diverse Data Ensemble Learning in Agriculture
We observe that including base models trained on other target crops and weeds can help generalize the model to capture varied field conditions.
P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds
Point cloud completion aims to recover the complete shape based on a partial observation.
A Comprehensive Overview of Large Language Models
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond.
UnLoc: A Universal Localization Method for Autonomous Vehicles using LiDAR, Radar and/or Camera Input
The results ascertain the efficacy of our technique.
Investigation of UAV Detection in Images with Complex Backgrounds and Rainy Artifacts
Hence, for this purpose, we prepared two training datasets.
Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps
We present a self-supervised learning method that employs Transformers for the first time for the task of outdoor localization using LiDAR data.
PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning
Specifically, we use the Unknown-Point Simulator to simulate out-of-distribution data in the training stage by manipulating the geometric context of partial known data.
Energy-Based Residual Latent Transport for Unsupervised Point Cloud Completion
Unsupervised point cloud completion aims to infer the whole geometry of a partial object observation without requiring partial-complete correspondence.
Strengthening Skeletal Action Recognizers via Leveraging Temporal Patterns
Skeleton sequences are compact and lightweight.
ANUBIS: Skeleton Action Recognition Dataset, Review, and Benchmark
To this end, we present a review in the form of a taxonomy on existing works of skeleton-based action recognition.
DRT: A Lightweight Single Image Deraining Recursive Transformer
Over parameterization is a common technique in deep learning to help models learn and generalize sufficiently to the given task; nonetheless, this often leads to enormous network structures and consumes considerable computing resources during training.
Visual Attention Methods in Deep Learning: An In-Depth Survey
Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more on the essential aspects of data.
Pyramidal Attention for Saliency Detection
Consequently, we present a new SOD perspective of generating RGB-D SOD without acquiring depth data during training and testing and assist RGB methods with depth clues for improved performance.
Vehicle and License Plate Recognition with Novel Dataset for Toll Collection
The best Mean Average Precision (mAP@0. 5) of 98. 8% for vehicle type recognition, 98. 5% for license plate detection, and 98. 3% for license plate reading is achieved by YOLOv4, while its lighter version, i. e., Tiny YOLOv4 obtained a mAP of 97. 1%, 97. 4%, and 93. 7% on vehicle type recognition, license plate detection, and license plate reading, respectively.
Image Dehazing Transformer With Transmission-Aware 3D Position Embedding
Though Transformer has occupied various computer vision tasks, directly leveraging Transformer for image dehazing is challenging: 1) it tends to result in ambiguous and coarse details that are undesired for image reconstruction; 2) previous position embedding of Transformer is provided in logic or spatial position order that neglects the variational haze densities, which results in the sub-optimal dehazing performance.
PU-Transformer: Point Cloud Upsampling Transformer
Given the rapid development of 3D scanners, point clouds are becoming popular in AI-driven machines.
PnP-3D: A Plug-and-Play for 3D Point Clouds
With the help of the deep learning paradigm, many point cloud networks have been invented for visual analysis.
Investigating Attention Mechanism in 3D Point Cloud Object Detection
Object detection in three-dimensional (3D) space attracts much interest from academia and industry since it is an essential task in AI-driven applications such as robotics, autonomous driving, and augmented reality.
Underwater Image Restoration via Contrastive Learning and a Real-world Dataset
There are 2000 reference restored images and 6003 original underwater images in the unpaired training set.
Survey: Image Mixing and Deleting for Data Augmentation
One such method is augmentation which introduces different types of corruption in the data to prevent the model from overfitting and to memorize patterns present in the data.
Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions
Such GNNs are incapable of learning relative positions between graph nodes within a graph.
Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network
The prevalent convolutional neural network (CNN) based image denoising methods extract features of images to restore the clean ground truth, achieving high denoising accuracy.
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition
Recent skeleton-based action recognition methods extract features from 3D joint coordinates as spatial-temporal cues, using these representations in a graph neural network for feature fusion to boost recognition performance.
Ranked #20 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
As a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods.
Ranked #2 on
Underwater Image Restoration
on LSUI
(using extra training data)
Invertible Denoising Network: A Light Solution for Real Noise Removal
InvDN transforms the noisy input into a low-resolution clean image and a latent representation containing noise.
Single Underwater Image Restoration by Contrastive Learning
Underwater image restoration attracts significant attention due to its importance in unveiling the underwater world.
Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion
Given the prominence of current 3D sensors, a fine-grained analysis on the basic point cloud data is worthy of further investigation.
Ranked #6 on
Semantic Segmentation
on Semantic3D
Densely Deformable Efficient Salient Object Detection Network
In this paper, inspired by the best background/foreground separation abilities of deformable convolutions, we employ them in our Densely Deformable Network (DDNet) to achieve efficient SOD.
Ranked #4 on
RGB-D Salient Object Detection
on SIP
(Average MAE metric, using extra
training data)
Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable
Identifying the information lossless condition for deep neural architectures is important, because tasks such as image restoration require keep the detailed information of the input data as much as possible.
Uncertainty Inspired RGB-D Saliency Detection
Our framework includes two main models: 1) a generator model, which maps the input image and latent variable to stochastic saliency prediction, and 2) an inference model, which gradually updates the latent variable by sampling it from the true or approximate posterior distribution.
Ranked #1 on
RGB-D Salient Object Detection
on LFSD
RGB-D Salient Object Detection
RGB Salient Object Detection
+1
Image Colorization: A Survey and Dataset
Image colorization is the process of estimating RGB colors for grayscale images or video frames to improve their aesthetic and perceptual quality.
Dense-Resolution Network for Point Cloud Classification and Segmentation
Our DRNet is designed to learn local point features from the point cloud in different resolutions.
Ranked #22 on
3D Part Segmentation
on ShapeNet-Part
Identity Enhanced Residual Image Denoising
We propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules and residual on the residual architecture for image denoising.
Attention Based Real Image Restoration
Furthermore, the evaluation in terms of quantitative metrics and visual quality for four restoration tasks i. e. Denoising, Super-resolution, Raindrop Removal, and JPEG Compression on 11 real degraded datasets against more than 30 state-of-the-art algorithms demonstrate the superiority of our R$^2$Net.
Mosaic Super-resolution via Sequential Feature Pyramid Networks
Additionally, to the best of our knowledge, our method is the first specialized method to super-resolve mosaic images, whether it be multi-spectral or Bayer.
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process.
Ranked #4 on
RGB-D Salient Object Detection
on LFSD
A Systematic Evaluation: Fine-Grained CNN vs. Traditional CNN Classifiers
In this work, we investigate the performance of the landmark general CNN classifiers, which presented top-notch results on large scale classification datasets, on the fine-grained datasets, and compare it against state-of-the-art fine-grained classifiers.
Geometric Back-projection Network for Point Cloud Classification
As the basic task of point cloud analysis, classification is fundamental but always challenging.
Ranked #31 on
3D Point Cloud Classification
on ModelNet40
Deep localization of protein structures in fluorescence microscopy images
This study highlights the importance of deep learning for the analysis of fluorescence microscopy protein imagery.
Multi-FAN: Multi-Spectral Mosaic Super-Resolution Via Multi-Scale Feature Aggregation Network
This mosaic image is then merged with the mosaic image generated by the SR network to produce a quantitatively superior image.
Deep Ancient Roman Republican Coin Classification via Feature Fusion and Attention
We perform the classification of ancient Roman Republican coins via recognizing their reverse motifs where various objects, faces, scenes, animals, and buildings are minted along with legends.
Diving Deeper into Underwater Image Enhancement: A Survey
In this paper, our main aim is two-fold, 1): to provide a comprehensive and in-depth survey of the deep learning-based underwater image enhancement, which covers various perspectives ranging from algorithms to open issues, and 2): to conduct a qualitative and quantitative comparison of the deep algorithms on diverse datasets to serve as a benchmark, which has been barely explored before.
Densely Residual Laplacian Super-Resolution
Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images.
Ranked #1 on
Image Super-Resolution
on BSD100 - 8x upscaling
Real Image Denoising with Feature Attention
Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, their performance is limited on real-noisy photographs and requires multiple stage network modeling.
Ranked #1 on
Color Image Denoising
on BSD68 sigma15
A Deep Journey into Super-resolution: A survey
Deep convolutional networks based super-resolution is a fast-growing field with numerous practical applications.
Deep Underwater Image Enhancement
In an underwater scene, wavelength-dependent light absorption and scattering degrade the visibility of images, causing low contrast and distorted color casts.
Chaining Identity Mapping Modules for Image Denoising
We propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules (CIMM) for image denoising.
Class-Specific Image Deblurring
In image deblurring, a fundamental problem is that the blur kernel suppresses a number of spatial frequencies that are difficult to recover reliably.
