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TherISuRNet - A Computationally Efficient Thermal Image Super-Resolution Network

TherISuRNet - A Computationally Efficient Thermal Image Supe...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Vishal Chudasama Heena Patel Kalpesh Prajapati Kishor Upla Raghavendra Ramachandra Kiran Raja Christoph Busch Sardar Vallabhbhai National Institute of Technology (SVNIT) Surat India Norwegian University of Science and Technology (NTNU) Gjøvik Norway

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Human perception is limited to perceive the objects beyond the range of visible wavelengths in the Electromagnetic (EM) spectrum. This prevents them to recognize objects in different conditions such as poor illumination or severe weather (e.g., under fog or smoke). The technological advancement in thermographic imaging enables the visualization of objects beyond visible range which enables it's use in many applications such as military, medical, agriculture, etc. However, due to the hardware constraints, the thermal cameras are limited with poor spatial resolution when compared to similar visible range RGB cameras. In this paper, we propose a Super-Resolution (SR) of thermal images using a deep neural network architecture which we refer to as TherISuRNet. We use progressive upscaling strategy with asymmetrical residual learning in the network which is computationally efficient for different upscaling factors such as ×2, ×3 and ×4. The proposed architecture consists of different modules for low and high-frequency feature extraction along with upsampling blocks. The effectiveness of the proposed architecture in TherISuRNet is verified by evaluating it with different datasets. The obtained results indicate superior results as compared to other state-of-the-art SR methods.

关键词： Cameras Spatial resolution Feature extraction Training computer architecture

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Multi-Stream CNN for Spatial Resource Allocation: a Crop Management Application

Multi-Stream CNN for Spatial Resource Allocation: a Crop Man...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Alexandre Barbosa Thiago Marinho Nicolas Martin Naira Hovakimyan University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Modeling the spatial structure of crop inputs is of great importance for accurate yield prediction. It is a fundamental step towards optimizing the spatial allocation of resources such as seed and fertilizer. We propose two distinct architectures of Multi-Stream Convolutional Neural Network (MSCNN) - Late Fusion (LF) and Early Fusion (EF) - to model yield response to seed and nutrient management. A study presents a comparison between proposed models with conventional 2D and 3D CNN architectures, and existing agronomy methods. The dataset used to train and test the models is constructed using on-farm experiment data from nine cornfields across the US together with multispectral satellite images. Results show that the MSCNN-LF achieved a 20% reduction of the prediction's mean squared error value when compared to a 3D CNN, and a 26% reduction when compared to a 2D CNN. An optimization algorithm uses the MSCNN-LF model's gradient to change the manageable inputs variables in a way the expected profit is maximized subject to resource constraints. It is shown that an increase of up to 5.2% on expected crop yield return is obtained when compared to usual management practices.

关键词： Agriculture Nitrogen computer architecture Data models Three-dimensional displays Soil Analytical models

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When Person Re-identification Meets Changing Clothes

When Person Re-identification Meets Changing Clothes

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Fangbin Wan Yang Wu Xuelin Qian Yixiong Chen Yanwei Fu School of Data Science Fudan University Kyoto University School of Computer Science Fudan University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Person re-identification (ReID) is now an active research topic for AI-based video surveillance applications such as specific person search, but the practical issue that the target person(s) may change clothes (clothes inconsistency problem) has been overlooked for long. For the first time, this paper systematically studies this problem. We first overcome the difficulty of lack of suitable dataset, by collecting a small yet representative real dataset for testing whilst building a large realistic synthetic dataset for training and deeper studies. Facilitated by our new datasets, we are able to conduct various interesting new experiments for studying the influence of clothes inconsistency. We find that changing clothes makes ReID a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous ReID models to identify persons with unseen (new) clothes. Representative existing ReID models are adopted to show informative results on such a challenging setting, and we also provide some preliminary efforts on improving the robustness of existing models on handling the clothes inconsistency issue in the data. We believe that this study can be inspiring and helpful for encouraging more researches in this direction. The dataset is available on the project website: https://***/***.

关键词： Cameras Benchmark testing Lighting Buildings Games Data models Biological system modeling

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The revenge of BiSeNet: Efficient Multi-Task Image Segmentation

The revenge of BiSeNet: Efficient Multi-Task Image Segmentat...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Gabriele Rosi Claudia Cuttano Niccolò Cavagnero Giuseppe Averta Fabio Cermelli Politecnico di Torino Focoos AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent advancements in image segmentation have focused on enhancing the efficiency of the models to meet the demands of real-time applications, especially on edge devices. However, existing research has primarily concentrated on single-task settings, especially on semantic segmentation, leading to redundant efforts and specialized architectures for different tasks. To address this limitation, we propose a novel architecture for efficient multi-task image segmentation, capable of handling various segmentation tasks without sacrificing efficiency or accuracy. We introduce BiSeNetFormer, that leverages the efficiency of two-stream semantic segmentation architectures and it extends them into a mask classification framework. Our approach maintains the efficient spatial and context paths to capture detailed and semantic information, respectively, while leveraging an efficient transformed-based segmentation head that computes the binary masks and class probabilities. By seamlessly supporting multiple tasks, namely semantic and panoptic segmentation, BiSeNetFormer offers a versatile solution for multi-task segmentation. We evaluate our approach on popular datasets, Cityscapes and ADE20K, demonstrating impressive inference speeds while maintaining competitive accuracy compared to state-of-the-art architectures. Our results indicate that BiSeNetFormer represents a significant advancement towards fast, efficient, and multi-task segmentation networks, bridging the gap between model efficiency and task adaptability.

关键词： computer vision Accuracy Semantic segmentation Semantics Refining Redundancy computer architecture

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Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis

Enhancing Traffic Safety with Parallel Dense Video Captionin...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Maged Shoman Dongdong Wang Armstrong Aboah Mohamed Abdel-Aty Department of Civil Environmental and Construction Engineering Smart and Safe Transportation (SST) Lab University of Central Florida United States Department of Civil Construction and Environmental Engineering North Dakota State University United States Department of Civil Environmental and Construction Engineering Joint Appointment Department of Computer Science Smart and Safe Transportation (SST) Lab University of Central Florida United States

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces our solution for Track 2 in AI City Challenge 2024. The task aims to solve traffic safety description and analysis with the dataset of Woven Traffic Safety (WTS), a real-world Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding. Our solution mainly focuses on the following points: 1) To solve dense video captioning, we leverage the framework of dense video captioning with parallel decoding (PDVC) to model visual-language sequences and generate dense caption by chapters for video. 2) Our work leverages CLIP to extract visual features to more efficiently perform cross-modality training between visual and textual representations. 3) We conduct domain-specific model adaptation to mitigate domain shift problem that poses recognition challenge in video understanding. 4) Moreover, we leverage BDD-5K captioned videos to conduct knowledge transfer for better understanding WTS videos and more accurate captioning. Our solution has yielded on the test set, achieving 6th place in the competition. The open-source code will be available at https://***/UCF-SST-Lab/AICity2024cvprw

关键词： Training Visualization Adaptation models Urban areas Feature extraction Weaving Tokenization

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Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways

Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Weikai Tan Nannan Qin Lingfei Ma Ying Li Jing Du Guorong Cai Ke Yang Jonathan Li Department of Geography and Environmental Management University of Waterloo Waterloo ON Canada College of Computer Engineering Jimei University Xiamen China Department of Systems Design Engineering University of Waterloo Waterloo ON Canada

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping. With rapid developments of mobile laser scanning (MLS) systems, massive point clouds are available for scene understanding, but publicly accessible large-scale labeled datasets, which are essential for developing learning-based methods, are still limited. This paper introduces Toronto-3D, a large-scale urban outdoor point cloud dataset acquired by a MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of point clouds and consists of about 78.3 million points with 8 labeled object classes. Baseline experiments for semantic segmentation were conducted and the results confirmed the capability of this dataset to train deep learning models effectively. Toronto-3D is released 1 to encourage new research, and the labels will be improved and updated with feedback from the research community.

关键词： Three-dimensional displays Semantics Roads Laser radar Sensors Machine learning Automobiles

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A Multimodal Approach Integrating Convolutional and Recurrent Neural Networks for Alzheimer’s Disease Temporal Progression Prediction

A Multimodal Approach Integrating Convolutional and Recurren...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Durga Supriya HL Swetha Mary Thomas Sowmya Kamath S National Institute of Technology Karnataka India Mangalore Karnataka India Dept. of Information Technology Healthcare Analytics and Language Engineering (HALE) Lab National Institute of Technology Karnataka Mangaluru India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Alzheimer’s Disease (AD) poses a substantial healthcare challenge marked by cognitive decline and a lack of definitive treatments. As the global population ages, the prevalence of AD escalates, underscoring the need for more advanced diagnostic techniques. Current single-modality methods have limitations, emphasizing the critical need for early detection and precise diagnosis to facilitate timely interventions and the development of effective therapies. We propose a novel multimodal medical diagnostic framework for AD employing a hybrid deep learning model. This framework integrates a 3D Convolutional Neural Network (CNN) to extract detailed intra-slice features from MRI volumes and a Long Short-Term Memory (LSTM) network to capture intricate inter-sequence patterns indicative of AD progression. By leveraging longitudinal MRI data alongside spatial, temporal, biomarkers, and cognitive scores, our framework significantly enhances diagnostic accuracy, particularly in the early stages of the disease. We incorporate Grad-CAM to enhance the interpretability of MRI-based diagnoses by highlighting relevant brain regions. This multimodal approach achieves a promising accuracy of 92.65%, outperforming state-of-the-art works by a margin of 6%.

关键词： Accuracy Three-dimensional displays Recurrent neural networks Magnetic resonance imaging Feature extraction Spatial databases pattern recognition

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A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning

A Method of Moments Embedding Constraint and its Application...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Michael Majurski Sumeet Menon Parniyan Farvardin David Chapman Information Technology Lab NIST University of Maryland University of Maryland University of Miami

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Discriminative deep learning models with a linear+softmax final layer have a problem: the latent space only predicts the conditional probabilities p(Y |X) but not the full joint distribution p(Y,X), which necessitates a generative approach. The conditional probability cannot detect outliers, causing outlier sensitivity in softmax networks. This exacerbates model over-confidence impacting many problems, such as hallucinations, confounding biases, and dependence on large datasets. To address this we introduce a novel embedding constraint based on the Method of Moments (MoM). We investigate the use of polynomial moments ranging from 1st through 4th order hyper-covariance matrices. Furthermore, we use this embedding constraint to train an Axis-Aligned Gaussian Mixture Model (AAGMM) final layer, which learns not only the conditional, but also the joint distribution of the latent space. We apply this method to the domain of semi-supervised image classification by extending FlexMatch with our technique. We find our MoM constraint with the AAGMM layer is able to match the reported FlexMatch accuracy, while also modeling the joint distribution, thereby reducing outlier sensitivity. We also present a preliminary outlier detection strategy based on Mahalanobis distance and discuss future improvements to this strategy. Code is available at: https://***/mmajurski/ssl-gmm

关键词： Deep learning Sensitivity Semisupervised learning Predictive models Polynomials Distance measurement pattern recognition

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RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection

RRU-Net: The Ringed Residual U-Net for Image Splicing Forger...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xiuli Bi Yang Wei Bin Xiao Weisheng Li School of Computer Science & Technology Chongqing University of Posts and Telecommunication

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Detecting a splicing forgery image and then locating the forgery regions is a challenging task. Some traditional feature extraction methods and convolutional neural network (CNN)-based detection methods have been proposed to finish this task by exploring the differences of image attributes between the un-tampered and tampered regions in a image. However, the performance of the existing detection methods is unsatisfactory. In this paper, we propose a ringed residual U-Net (RRU-Net) for image splicing forgery detection. The proposed RRU-Net is an end-to-end image essence attribute segmentation network, which is independent of human visual system, it can accomplish the forgery detection without any preprocessing and post-processing. The core idea of the proposed RRU-Net is to strengthen the learning way of CNN, which is inspired by the recall and the consolidation mechanism of the human brain and implemented by the propagation and the feedback process of the residual in CNN. The residual propagation recalls the input feature information to solve the gradient degradation problem in the deeper network; the residual feedback consolidates the input feature information to make the differences of image attributes between the un-tampered and tampered regions be more obvious. Experimental results show that the proposed detection method can achieve a promising result compared with the state-of-the-art splicing forgery detection methods.

关键词： Forgery Splicing Feature extraction Degradation Task analysis Image segmentation Network architecture

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Dataset condensation with latent quantile matching

Dataset condensation with latent quantile matching

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Wei Wei Tom De Schepper Kevin Mets Department of Computer Science IDLab University of Antwerp - imec Antwerp Belgium IDLab Faculty of Applied Engineering University of Antwerp - imec Antwerp Belgium

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Dataset condensation (DC) methods aim to learn a smaller, synthesized dataset with informative data records to accelerate the training of machine learning models. Current distribution matching (DM) based DC methods learn a synthesized dataset by matching the mean of the latent embeddings between the synthetic and the real dataset. However, two distributions with the same mean can still be vastly different. In this work, we demonstrate the shortcomings of using Maximum Mean Discrepancy to match latent distributions, i.e., the weak matching power and lack of outlier regularization. To alleviate these shortcomings, we propose our new method: Latent Quantile Matching (LQM), which matches the quantiles of the latent embeddings to minimize the goodness of fit test statistic between two distributions. Empirical experiments on both image and graph-structured datasets show that LQM matches or outperforms previous state of the art in distribution matching based DC. Moreover, we show that LQM improves the performance in continual graph learning (CGL) setting, where memory efficiency and privacy can be important. Our work sheds light on the application of DM based DC for CGL.

关键词： Training Measurement Data privacy conferences Memory management Machine learning Current distribution

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