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Lightweight Maize Disease Detection through Post-Training Quantization with Similarity Preservation

Lightweight Maize Disease Detection through Post-Training Qu...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Carlos Victorino Padeiro Tse-Wei Chen Takahiro Komamizu Ichiro Ide Nagoya University Nagoya Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Traditional crop disease diagnosis, reliant on expert visual observation, is expensive, time-consuming, and prone to error. While Convolutional Neural Networks (CNNs) offer promising alternatives, their high resource demands limit their accessibility to farmers, particularly those in resource-constrained settings. Lightweight models that operate on resource-limited devices without network access are crucial to address this gap. This paper proposes a Similarity-Preserving Quantization (SPQ) method to convert high-precision CNNs into lower-precision models while maintaining similar feature representations. While quantization offers a promising approach for building lightweight CNNs for crop disease detection, the quality of quantized models often suffers. SPQ addresses this challenge by ensuring equivalent activation patterns for similar crop images in both the original and quantized models. Experimental evaluation using MobileNetV2 and ResNet-50 demonstrates that SPQ improves throughput, inference, and memory footprint more than 3 times while preserving the detection performance.

关键词： Visualization Quantization (signal) Crops Euclidean distance Throughput pattern recognition Medical diagnosis

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vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning

Vision-Language Pseudo-Labels for Single-Positive Multi-Labe...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xin Xing Zhexiao Xiong Abby Stylianou Srikumar Sastry Liyu Gong Nathan Jacobs University of Nebraska Omaha Washington University in St. Louis Saint Louis University Oracle Inc

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We study a limited label problem and present a novel approach to Single-Positive Multi-label Learning. In the multi-label learning setting, a model learns to predict multiple labels or categories for a single input image. This contrasts with standard multi-class image classification, where the task is to predict a single label from many possible labels for an image. Single-Positive Multi-label Learning specifically considers learning to predict multiple labels when there is only one annotation per image in the training data. Multi-label learning is a more natural task than single-label learning because real-world data often involves instances belonging to multiple categories simultaneously; however, most computer vision datasets contain single labels due to the inherent complexity and cost of collecting multiple high-quality annotations per image. We propose a novel approach called vision-Language Pseudo-Labeling, which uses a vision-language model, CLIP, to suggest strong positive and negative pseudo-labels. The experiment performance shows the effectiveness of the proposed model. Our code and data will be made publicly available at https://***/mvrl/VLPL.

关键词： computer vision Costs Annotations Computational modeling conferences Training data Predictive models

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ExerAIde: AI-assisted Multimodal Diagnosis for Enhanced Sports Performance and Personalised Rehabilitation

ExerAIde: AI-assisted Multimodal Diagnosis for Enhanced Spor...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ahmed Qazi Asim Iqbal Tibbling Technologies

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The quest for personalized sports therapy has long been a concern for practitioners and patients alike, aiming for recovery protocols that transcend the one-size-fits-all approach. In this study, we introduce a novel framework for personalized sports therapy through automated joint movement analysis. By synthesizing the analytical capabilities of a Random Forest Classifier (RFC) with a Vector Quantized Variational AutoEncoder (VQ-VAE), we systematically discern the nuanced kinematic differences between healthy and pathological exercise movements. The RFC prioritizes the joints by their discriminative influence on movement healthiness, which informs the VQ-VAE’s derivation of a distilled list of pivotal joints. This dual-model approach not only identifies a hierarchy of joint importance but also ascertains the minimal subset of joints critical for distinguishing between healthy and unhealthy movement patterns. The resultant data-driven insight into joint-specific dynamics underpins the development of targeted, individualized rehabilitation programs. Our results exhibit promising directions in sports therapy, showcasing the potential of machine learning in developing personalized therapeutic interventions.

关键词： Pathology computer vision Protocols conferences Medical treatment Kinematics Vectors

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Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large Multi-Modal Models

Audio-Visual Generalized Zero-Shot Learning using Pre-Traine...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： David Kurzendörfer Otniel-Bogdan Mercea A. Sophia Koepke Zeynep Akata University of Tübingen Localyzer GmbH Tübingen AI Center Helmholtz Munich Technical University of Munich

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Audio-visual zero-shot learning methods commonly build on features extracted from pre-trained models, e.g. video or audio classification models. However, existing benchmarks predate the popularization of large multi-modal models, such as CLIP and CLAP. In this work, we explore such large pre-trained models to obtain features, i.e. CLIP for visual features, and CLAP for audio features. Furthermore, the CLIP and CLAP text encoders provide class label embeddings which are combined to boost the performance of the system. We propose a simple yet effective model that only relies on feed-forward neural networks, exploiting the strong generalization capabilities of the new audio, visual and textual features. Our framework achieves state-of-the-art performance on VGGSound-GZSL cls , UCF-GZSL cls , and ActivityNet-GZSL cls with our new features. Code and data available at: https://***/dkurzend/ClipClap-GZSL.

关键词： Visualization computer vision Codes Zero-shot learning conferences Computational modeling Benchmark testing

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GenVideo: One-shot target-image and shape aware video editing using T2I diffusion models

GenVideo: One-shot target-image and shape aware video editin...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sai Sree Harsha Ambareesh Revanur Dhwanit Agarwal Shradha Agrawal Adobe Inc.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video editing methods based on diffusion models that rely solely on a text prompt for the edit are hindered by the limited expressive power of text prompts. Thus, incorporating a reference target image as a visual guide becomes desirable for precise control over edit. Also, most existing methods struggle to accurately edit a video when the shape and size of the object in the target image differ from the source object. To address these challenges, we propose "GenVideo" for editing videos leveraging target-image aware T2I models. Our approach handles edits with target objects of varying shapes and sizes while maintaining the temporal consistency of the edit using our novel target and shape aware InvEdit masks. Further, we propose a novel target-image aware latent noise correction strategy during inference to improve the temporal consistency of the edits. Experimental analyses indicate that GenVideo can effectively handle edits with objects of varying shapes, where existing approaches fail.

关键词： Visualization computer vision Shape conferences Pipelines Noise Wheels

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Red-Teaming Segment Anything Model

Red-Teaming Segment Anything Model

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Krzysztof Jankowski Bartlomiej Sobieski Mateusz Kwiatkowski Jakub Szulc Michał Janik Hubert Baniecki Przemysław Biecek University of Warsaw Warsaw University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Foundation models have emerged as pivotal tools, tackling many complex tasks through pre-training on vast datasets and subsequent fine-tuning for specific applications. The Segment Anything Model is one of the first and most well-known foundation models for computer vision segmentation tasks. This work presents a multi-faceted red-teaming analysis that tests the Segment Anything Model against challenging tasks: (1) We analyze the impact of style transfer on segmentation masks, demonstrating that applying adverse weather conditions and raindrops to dashboard images of city roads significantly distorts generated masks. (2) We focus on assessing whether the model can be used for attacks on privacy, such as recognizing celebrities’ faces, and show that the model possesses some undesired knowledge in this task. (3) Finally, we check how robust the model is to adversarial attacks on segmentation masks under text prompts. We not only show the effectiveness of popular white-box attacks and resistance to black-box attacks but also introduce a novel approach - Focused Iterative Gradient Attack (FIGA) that combines white-box approaches to construct an efficient attack resulting in a smaller number of modified pixels. All of our testing methods and analyses indicate a need for enhanced safety measures in foundation models for image segmentation.

关键词： Image segmentation Analytical models Privacy computer vision Text recognition Computational modeling Urban areas

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Unsupervised Microscopy Video Denoising

Unsupervised Microscopy Video Denoising

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Mary Aiyetigbo Alexander Korte Ethan Anderson Reda Chalhoub Peter Kalivas Feng Luo Nianyi Li School of Computing Clemson University Department of Neuroscience The Medical University of South Carolina

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we introduce a novel unsupervised network to denoise microscopy videos featured by image sequences captured by a fixed location microscopy camera. Specifically, we propose a DeepTemporal Interpolation method, leveraging a temporal signal filter integrated into the bottom CNN layers, to restore microscopy videos corrupted by unknown noise types. Our unsupervised denoising architecture is distinguished by its ability to adapt to multiple noise conditions without the need for pre-existing noise distribution knowledge, addressing a significant challenge in real-world medical applications. Furthermore, we evaluate our denoising framework using both real microscopy recordings and simulated data, validating our outperforming video denoising performance across a broad spectrum of noise scenarios. Extensive experiments demonstrate that our unsupervised model consistently outperforms state-of-the-art supervised and unsupervised video denoising techniques, proving especially effective for microscopy videos. The project page is available at https://***/UMVD/

关键词： Interpolation Microscopy Noise reduction Noise Medical services Recording pattern recognition

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Calibration of Continual Learning Models

Calibration of Continual Learning Models

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Lanpei Li Elia Piccoli Andrea Cossu Davide Bacciu Vincenzo Lomonaco ISTI-CNR University of Pisa University of Pisa

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data. Unfortunately, CL models tend to forget previous knowledge, thus often underperforming when compared with an offline model trained jointly on the entire data stream. Given that any CL model will eventually make mistakes, it is of crucial importance to build calibrated CL models: models that can reliably tell their confidence when making a prediction. Model calibration is an active research topic in machine learning, yet to be properly investigated in CL. We provide the first empirical study of the behavior of calibration approaches in CL, showing that CL strategies do not inherently learn calibrated models. To mitigate this issue, we design a continual calibration approach that improves the performance of post-processing calibration methods over a wide range of different benchmarks and CL strategies. CL does not necessarily need perfect predictive models, but rather it can benefit from reliable predictive models. We believe our study on continual calibration represents a first step towards this direction.

关键词： Continuing education computer vision conferences Computational modeling Machine learning Predictive models Data models

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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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A Machine Learning-based Approach for Automatic Grading and Quality Inspection of Indian Mangoes 2

A Machine Learning-based Approach for Automatic Grading and ...

引用

2nd ieee-Industrial-Electronics-society Annual On-Line conference (ONCON)

作者： Bagchi, Sourav Aditya, Janumpally Varun Kumari, Sneha Dhanraj, Malla Jenamani, Mamata Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur W Bengal India

ISBN: (纸本)9798350357974

Manual visual assessment of mangoes has been problematic for the agriculture sector because of its time-consuming nature and inconsistent evaluation and sorting methods. The advent of automated flaw identification using computer vision and machine learning offers a notable shift and improvement in the visual inspection process. A common issue with mangoes is the presence of dark patches, indicative of disease or rot, which negatively affect the appearance and quality of the fruit. This paper introduces a framework using computer vision which utilizes image analysis and machine learning methods to identify these dark spots, taking into account the mangoes' texture. The proposed framework has a simplified configuration and tuning process, enhancing its ease of deployment in real-world applications. This innovation aligns with the advancements in integrating cutting-edge technologies to optimize efficiency and consistency in agricultural practices, thereby contributing to the evolution of smart agriculture and addressing the challenges and opportunities presented by the next wave of industrial revolution.

关键词： computer vision System Dark Patches detection Image Processing Local Binary pattern (LBP) Machine Learning Random Forest SVM Classifier Grading

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