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Unsupervised Domain Adaptation for Weed Segmentation Using Greedy Pseudo-labelling

Unsupervised Domain Adaptation for Weed Segmentation Using G...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yingchao Huang Abdul Bais University of Regina Regina Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Automatic weed identification based on RGB images with convolutional neural networks (CNN) is a new frontier of precision agriculture. However, the CNN models expect a large volume of labelled data. Their performance deteriorates across different fields due to varied agricultural contexts. To address this, we propose an unsupervised domain adaptation (DA) framework leveraging pseudo-labelling. Our method involves co-training labelled source data with pseudo-labelled target data. We introduce a novel greedy pseudo-labelling strategy to optimize pseudo-label selection, maximizing gains while minimizing overfitting risks. Monitoring overfitting with covariance helps detect fluctuations in class pixel counts during co-training, enhancing target performance. The proposed framework has demonstrated superior performance by evaluation against literature approaches, including the input-level DA methods with Fourier Transform, feature-level with CycleGAN methods and AdaptSegNet, and output-level with self-training. It is tested with the ROSE challenge dataset from different cameras and years with diverse plant stages. Particularly in challenging conditions for plants across different years with varied plant stages, the proposed method outperforms existing literature that struggles to surpass the baseline.

关键词： Precision agriculture Solid modeling computer vision Fourier transforms Fluctuations conferences Data models

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Multi-User Natural Interaction System based on Real-Time Hand Tracking and Gesture recognition

Multi-User Natural Interaction System based on Real-Time Han...

引用

International conference on pattern recognition

作者： A. Del Bimbo L. Landucci A. Valli Universita degli Studi di Firenze Firenze Toscana IT Dipartimento di Sistemi e Informatica University of Florence Italy Firenze Italy

We present a computer vision based system that enables multiple people to interact naturally with a large display table using their own bare-hand gestures. The display presents and supports a particular multimedia application that can be used at the same time even by remote users. Finally we describe two different applications designed for didactic and entertainment scenarios

关键词： Real time systems Surface morphology computer displays Application software Multimedia systems pattern recognition Hidden Markov models Visualization Videoconference computer vision

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Non-Rigid Metric Shape and Motion Recovery from Uncalibrated Images Using Priors

Non-Rigid Metric Shape and Motion Recovery from Uncalibrated...

引用

conference on computer vision and pattern recognition (CVPR)

作者： A. Del Bue X. Llad L. Agapito Department of Computer Science University of London London UK

In this paper we focus on the estimation of the 3D Euclidean shape and motion of a non-rigid object which is moving rigidly while deforming and is observed by a perspective camera. Our method exploits the fact that it is often a reasonable assumption that some of the points are deforming throughout the sequence while others remain rigid. First we use an automatic segmentation algorithm to identify the set of rigid points which in turn is used to estimate the internal camera calibration parameters and the overall rigid motion. Finally we formalise the problem of non-rigid shape estimation as a constrained non-linear minimization adding priors on the degree of deformability of each point. We perform experiments on synthetic and real data which show firstly that even when using a minimal set of rigid points it is possible to obtain reliable metric information and secondly that the shape priors help to disambiguate the contribution to the image motion caused by the deformation and the perspective distortion.

关键词： Cameras Motion estimation Nonlinear distortion Shape measurement Noise shaping computer vision computer science Calibration Motion measurement Rotation measurement

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Implicit Euler ODE Networks for Single-Image Dehazing

Implicit Euler ODE Networks for Single-Image Dehazing

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jiawei Shen Zhuoyan Li Lei Yu Gui-Song Xia Wen Yang School of Electronic and Information Wuhan University School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep convolutional neural networks (CNN) have been applied for image dehazing tasks, where the residual network (ResNet) is often adopted as the basic component to avoid the vanishing gradient problem. Recently, many works indicate that the ResNet can be considered as the explicit Euler forward approximation of an ordinary differential equation (ODE). In this paper, we extend the explicit forward approximation to the implicit backward counterpart, which can be realized via a recursive neural network, named IM-block. Given that, we propose an efficient end-to-end multi-level implicit network (MI-Net) for the single image dehazing problem. Moreover, multi-level fusing (MLF) mechanism and residual channel attention block (RCA-block) are adopted to boost performance of our network. Experiments on several dehazing benchmark datasets demonstrate that our method outperforms existing methods and achieves the state-of-the-art performance.

关键词： Atmospheric modeling Task analysis Convolution Neural networks Image color analysis Visualization Scattering

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Wire Structure pattern Extraction and Tracking From X-Ray Images of Composite Mechanisms

Wire Structure Pattern Extraction and Tracking From X-Ray Im...

引用

conference on computer vision and pattern recognition (CVPR)

作者： D. Tschumperle J. Fadili GREYC IMAGE (CNRS UMR 6072) Juin Caen Cedex France

This paper introduces a complete pipeline of image processing methods in order to analyze and track the internal structures of a composite material. As a first step, input Xray images are denoised, enhanced, separated into different morphological components, and geometrically filtered in order to isolate the interesting fiber inside the composite material. This requires the design of specific algorithms preserving very thin image details while being able to remove undesired image regions that may be sometimes large. For this purpose, we use state-of the-art techniques based on recent achievements in diffusion PDE’s and modern harmonic analysis tools. Then, the shapes of the remaining fibered composite are individually analyzed by the use of a tensor-based tracking algorithm. We illustrate how this set of techniques allows the dynamic analysis of the composite structure when submitted to external mechanical loads.

关键词： Wire X-ray imaging Optical fiber devices Image processing Composite materials Image analysis Pipelines Algorithm design and analysis Shape Optical fiber testing

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Discriminant Distribution-Agnostic Loss for Facial Expression recognition in the Wild

Discriminant Distribution-Agnostic Loss for Facial Expressio...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Amir Hossein Farzaneh Xiaojun Qi Department of Computer Science Utah State University Logan UT USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Facial Expression recognition (FER) has demonstrated remarkable progress due to the advancement of deep Convolutional Neural Networks (CNNs). FER's goal as a visual recognition problem is to learn a mapping from the facial embedding space to a set of fixed expression categories using a supervised learning algorithm. Softmax loss as the de facto standard in practice fails to learn discriminative features for efficient learning. Center loss and its variants as promising solutions increase deep feature discriminability in the embedding space and enable efficient learning. They fundamentally aim to maximize intra-class similarity and inter-class separation in the embedding space. However, center loss and its variants ignore the underlying extreme class imbalance in challenging wild FER datasets. As a result, they lead to a separation bias toward majority classes and leave minority classes overlapped in the embedding space. In this paper, we propose a novel Discriminant Distribution-Agnostic loss (DDA loss) to optimize the embedding space for extreme class imbalance scenarios. Specifically, DDA loss enforces inter-class separation of deep features for both majority and minority classes. Any CNN model can be trained with the DDA loss to yield well separated deep feature clusters in the embedding space. We conduct experiments on two popular large-scale wild FER datasets (RAF-DB and AffectNet) to show the discriminative power of the proposed loss function.

关键词： Face recognition Feature extraction Visualization Emotion recognition Clustering algorithms Linear programming

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PEA: Improving the Performance of ReLU Networks for Free by Using Progressive Ensemble Activations

PEA: Improving the Performance of ReLU Networks for Free by ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Á kos Utasi Continental AI Development Center Budapest

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In recent years novel activation functions have been proposed to improve the performance of neural networks, and they show superior performance compared to the ReLU counterpart. However, there are environments, where the availability of complex activations is limited, and usually only the ReLU is supported. In this paper we propose methods that can be used to improve the performance of ReLU networks by using these efficient novel activations during model training. More specifically, we propose ensemble activations that are composed of the ReLU and one of these novel activations. Furthermore, the coefficients of the ensemble are neither fixed nor learned, but are progressively updated during the training process in a way that by the end of the training only the ReLU activations remain active in the network and the other activations can be removed. This means that in inference time the network contains ReLU activations only. We perform extensive evaluations on the ImageNet classification task using various compact network architectures and various novel activation functions. Results show 0.2–0.8% top-1 accuracy gain, which confirms the applicability of the proposed methods. Furthermore, we demonstrate the proposed methods on semantic segmentation and we boost the performance of a compact segmentation network by 0.34% mIOU on the Cityscapes dataset.

关键词： Training Image segmentation computer vision conferences Semantics Neural networks Network architecture

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Is Our Continual Learner Reliable? Investigating Its Decision Attribution Stability through SHAP Value Consistency

Is Our Continual Learner Reliable? Investigating Its Decisio...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yusong Cai Shimou Ling Liang Zhang Lili Pan Hongliang Li University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this work, we identify continual learning (CL) methods’ inherent differences in sequential decision attribution. In the sequential learning process, inconsistent decision attribution may undermine the interpretability of a continual learner. However, existing CL evaluation metrics, as well as current interpretability methods, cannot measure the decision attribution stability of a continual learner. To bridge the gap, we introduce Shapley value, a well-known decision attribution theory, and define SHAP value consistency (SHAPC) to measure the consistency of a continual learner’s decision attribution. Furthermore, we define the mean and the variance of SHAPC values, namely SHAPC-Mean and SHAPC-Var, to jointly evaluate the decision attribution stability of continual learners over sequential tasks. On Split CIFAR-10, Split CIFAR-100, and Split TinyImageNet, we compare the decision attribution stability of different CL methods using the proposed metrics, providing a new perspective for evaluating their reliability.

关键词： Measurement Continuing education computer vision Current measurement conferences Decision making Reliability theory

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Open-world Instance Segmentation: Top-down Learning with Bottom-up Supervision

Open-world Instance Segmentation: Top-down Learning with Bot...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tarun Kalluri Weiyao Wang Heng Wang Manmohan Chandraker Lorenzo Torresani Du Tran UC San Diego Meta AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Top-down instance segmentation architectures excel with predefined closed-world taxonomies but exhibit biases and performance degradation in open-world scenarios. In this work, we introduce bottom-Up and top-Down Open-world Segmentation (UDOS), a novel approach that combines classical bottom-up segmentation methods within a top-down learning framework. UDOS leverages a top-down network trained with weak supervision derived from class-agnostic bottom-up segmentation to predict object parts. These part-masks undergo affinity-based grouping and refinement to generate precise instance-level segmentations. UDOS balances the efficiency of top-down architectures with the capacity to handle unseen categories through bottom-up supervision. We validate UDOS on challenging datasets (MS-COCO, LVIS, ADE20k, UVO, and OpenImages), achieving superior performance over state-of-the-art methods in cross-category and cross-dataset transfer tasks. Our code and models will be publicly available.

关键词： Instance segmentation Degradation computer vision Codes conferences Computational modeling Taxonomy

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uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories

uTRAND: Unsupervised Anomaly Detection in Traffic Trajectori...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Giacomo D’Amicantonio Egor Bondarau Peter H.N De With Eindhoven University of Technology Eindhoven Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep learning-based approaches have achieved significant improvements on public video anomaly datasets, but often do not perform well in real-world applications. This paper addresses two issues: the lack of labeled data and the difficulty of explaining the predictions of a neural network. To this end, we present a framework called uTRAND, that shifts the problem of anomalous trajectory prediction from the pixel space to a semantic-topological domain. The framework detects and tracks all types of traffic agents in bird’s-eye-view videos of traffic cameras mounted at an intersection. By conceptualizing the intersection as a patch-based graph, it is shown that the framework learns and models the normal behaviour of traffic agents without costly manual labeling. Furthermore, uTRAND allows to formulate simple rules to classify anomalous trajectories in a way suited for human interpretation. We show that uTRAND outperforms other state-of-the-art approaches on a dataset of anomalous trajectories collected in a real-world setting, while producing explainable detection results.

关键词： Accuracy Semantics Neural networks Layout Object detection Manuals Rendering (computer graphics)

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