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Guided Generative Models using Weak Supervision for Detecting Object Spatial Arrangement in Overhead Images
[article]
2021
arXiv
pre-print
Preserved Fulltext
Spatial arrangement estimation is the process of identifying the areas which contain the desired objects in overhead images. ...
The increasing availability and accessibility of numerous overhead images allows us to estimate and assess the spatial arrangement of groups of geospatial target objects, which can benefit many applications ...
This paper proposes a target-guided generative model (TGGM), which exploits a few labeled target windows to detect the spatial arrangement of the target objects within an ROI in overhead images. ...
arXiv:2112.05786v1
fatcat:mlkx3jz7c5cnzjya2jb2g2cs2q
Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields
2017
2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
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2017 pre-print arXiv:1706.02850v1 fatcat:5wcqjinvdrcrvmp46uf7zl7hb4
In this work we present an approach for developing pedestrian localization models using DL algorithms with efficient weak supervision from an expert. ...
This approach of weak supervision through expert selection of representative patches, suitable transformations and synthetic data augmentations enables us to successfully develop DL models for pedestrian ...
The model processes the whole image in a single pass and produces a set of bounding boxes for each object that it has detected in the image. The model can also associate a class to each object. ...
doi:10.1109/avss.2017.8078490
dblp:conf/avss/CorbettaMT17
fatcat:mhrksi66jvenxpmrrxa6cqzoqu
Learning Saliency Propagation for Semi-Supervised Instance Segmentation
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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We aim to improve the accuracy of the existing instance segmentation models by utilizing a large amount of detection supervision. ...
Instance segmentation is a challenging task for both modeling and annotation. Due to the high annotation cost, modeling becomes more difficult because of the limited amount of supervision. ...
N is the number of detected objects in the image. ...
doi:10.1109/cvpr42600.2020.01032
dblp:conf/cvpr/ZhouWJDY20
fatcat:qcak6ujxt5dolca4dtudmuezkm
Semantic-Edge-Supervised Single-Stage Detector for Oriented Object Detection in Remote Sensing Imagery
2022
Remote Sensing
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In recent years, significant progress has been made in arbitrary-oriented object detection. Different from natural images, object detection in aerial images remains its problems and challenges. ...
Furthermore, to solve the problem of dense objects with different directions in remote sensing images, we propose a rotation-invariant spatial pooling pyramid (RISPP) to extract the features of objects ...
Datasets The DOTA [28] is a large-scale public dataset for object detection in the aerial image. There are 2806 images in total. ...
doi:10.3390/rs14153637
fatcat:c2u4g2zsljcxzoo47q6s6t55sy
Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey
2022
Remote Sensing
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Object detection in remote sensing images (RSIs) requires the locating and classifying of objects of interest, which is a hot topic in RSI analysis research. ...
Then, we systematically summarize the procedures used in DL-based detection algorithms. ...
[149] proposed weak labels of object centroids and trained the network by using the generated pseudo-label. ...
doi:10.3390/rs14102385
fatcat:sgoqy33cdbe2xopqqfsa2hyowq
Adaptive headlamps in automobiles: A review on detection techniques and mathematical models
2021
IEEE Access
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Also, the paper reviews vehicle detection algorithms as well as various vehicle mathematical models for headlamp control based on steering angles ...
This paper explores various methodologies used in implementing adaptive headlamps and explore the scope for future work in this area of research. ...
• Apt for real time object
detection from videos
• Faster response but accuracy not
on par with region proposal
based models especially for
detection of small objects and
images of varied aspect ...
doi:10.1109/access.2021.3088036
fatcat:nhzlvzlg2fhrfghhu3q2d2ahxy
Salient Object Detection Techniques in Computer Vision—A Survey
2020
Entropy
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Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. ...
Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end. ...
In contrast to weak-supervision methods which work with accurate but limited supervision, pseudo-supervised SOD models usually have access to more information which is generally not accurate for SOD. ...
doi:10.3390/e22101174
pmid:33286942
pmcid:PMC7597345
fatcat:3p5d2nal4vhxbi2via3g7oicga
Sparse Label Assignment for Oriented Object Detection in Aerial Images
2021
Remote Sensing
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Object detection in aerial images has received extensive attention in recent years. ...
Next, to accurately detect small and densely arranged objects, we use a position-sensitive feature pyramid network (PS-FPN) with a coordinate attention module to extract position-sensitive features for ...
IoU loss has achieved great success in generic object detection [46, 47] . It is feasible to directly use the rotated IoU to guide the regression in oriented object detection, but it is not optimal. ...
doi:10.3390/rs13142664
fatcat:h33uht3mevb6ni7pijrowvlg6i
Destruction and Construction Learning for Fine-Grained Image Recognition
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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For "construction", a region alignment network, which tries to restore the original spatial layout of local regions, is followed to model the semantic correlation among local regions. ...
Delicate feature representation about object parts plays a critical role in fine-grained recognition. ...
Object Localization: We also tested DCL on weakly supervised object localization task on VOC2007 dataset using SPN [43] . ...
doi:10.1109/cvpr.2019.00530
dblp:conf/cvpr/ChenBZM19
fatcat:eohyeox7ovbctpotypaf6cmzt4
Weakly Supervised Attention-based Models Using Activation Maps for Citrus Mite and Insect Pest Classification
[article]
2021
arXiv
pre-print
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In addition, we evaluate and compare our models with weakly supervised methods, such as Attention-based Deep MIL and WILDCAT. ...
We analyze the proposed approach in two challenging datasets, the Citrus Pest Benchmark, which was captured directly in the field using magnifying glasses, and the Insect Pest, a large pest image benchmark ...
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ...
arXiv:2110.00881v1
fatcat:jonogwvdgja53nfprsmo3yxhoi
CO2: Consistent Contrast for Unsupervised Visual Representation Learning
[article]
2020
arXiv
pre-print
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It also transfers to image classification, object detection, and semantic segmentation on PASCAL VOC. This shows that CO2 learns better visual representations for these downstream tasks. ...
To address this issue, inspired by consistency regularization in semi-supervised learning on unlabeled data, we propose Consistent Contrast (CO2), which introduces a consistency regularization term into ...
For example, by identifying spatial arrangement (Doersch et al., 2015) , orientation (Gidaris et al., 2018) , or chromatic channels , models learn useful representations for downstream tasks. ...
arXiv:2010.02217v1
fatcat:o7jllik7ezerzdkfe4dosreaa4
Research on efficient feature extraction: Improving YOLOv5 backbone for facial expression detection in live streaming scenes
2022
Frontiers in Computational Neuroscience
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This paper proposes an efficient feature extraction network based on the YOLOv5 model for detecting anchors' facial expressions. ...
First, a two-step cascade classifier and recycler is established to filter invalid video frames to generate a facial expression dataset of anchors. ...
By controlling the width and depth of the extracted features, the network can meet different object detection arrangement needs. ...
doi:10.3389/fncom.2022.980063
pmid:36034936
pmcid:PMC9399731
fatcat:x2tmyz54xnafhkhaqmolhphd4y
Unsupervised Domain Adaptation in Semantic Segmentation: a Review
[article]
2020
arXiv
pre-print
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The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. ...
Finally, a comparison of the performance of the various methods in the widely used autonomous driving scenario is presented. ...
like image classification or object detection. ...
arXiv:2005.10876v1
fatcat:7t5v6qibxnfcxhwtohqqunhd2u
DocParser: Hierarchical Document Structure Parsing from Renderings
2021
AAAI Conference on Artificial Intelligence
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Our experiments confirm the effectiveness of our proposed weak supervision: Compared to the baseline without weak supervision, it improves the mean average precision for detecting document entities by ...
Our third contribution is to propose a scalable learning framework for settings where domain-specific data are scarce, which we address by a novel approach to weak supervision that significantly improves ...
Weak Supervision for Document Layout: (Zhong, Tang, and Yepes 2019) use weak supervision for detection of page layout entities. ...
dblp:conf/aaai/RauschMB0F21
fatcat:scjwme4nwzajxet7wqvpgoal2u
Unsupervised Domain Adaptation in Semantic Segmentation: A Review
2020
Technologies
Preserved Fulltext
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The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. ...
Finally, a comparison of the performance of the various methods in the widely used autonomous driving scenario is presented. ...
like image classification or object detection. ...
doi:10.3390/technologies8020035
fatcat:qzgjjiw5p5bldk76mh3s3pwlfq
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