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Making use of unlabeled data: Comparing strategies for marine animal detection in long-tailed datasets using self-supervised and semi-supervised pre-training

Making use of unlabeled data: Comparing strategies for marin...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Sharma, Tarun Cline, Danelle E. Edgington, Duane CALTECH Pasadena CA 91125 USA MBARI Moss Landing CA USA

ISBN: (纸本)9798350365474

This paper discusses strategies for object detection in marine images from a practitioner's perspective working with real-world long-tail distributed datasets with a large amount of additional unlabeled data on hand. The paper discusses the benefits of separating the localization and classification stages, making the case for robustness in localization through the amalgamation of additional datasets inspired by a widely used approach by practitioners in the camera-trap literature. For the classification stage, the paper compares strategies to use additional unlabeled data, comparing supervised, supervised iteratively, self-supervised, and semi-supervised pre-training approaches. Our findings reveal that semi-supervised pre-training, followed by supervised fine-tuning, yields a significantly improved balanced performance across the long-tail distribution, albeit occasionally with a trade-off in overall accuracy. These insights are validated through experiments on two real-world long-tailed underwater datasets collected by the Monterey Bay Aquarium Research Institute (MBARI).

关键词： computer vision conservation technology long-tail long-tailed distribution marine imaging real-world datasets Self-supervised learning semi-supervised learning underwater animal detection unlabeled data

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Unveiling the Anomalies in an Ever-ChangingWorld: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning

Unveiling the Anomalies in an Ever-ChangingWorld: A Benchmar...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Bugarin, Nikola Bugaric, Jovana Barusco, Manuel Pezze, Davide Dalle Susto, Gian Antonio Univ Padua Padua Italy

ISBN: (纸本)9798350365474

Anomaly Detection is a relevant problem in numerous real-world applications, especially when dealing with images. However, little attention has been paid to the issue of changes over time in the input data distribution, which may cause a significant decrease in performance. In this study, we investigate the problem of Pixel-Level Anomaly Detection in the Continual Learning setting, where new data arrives over time and the goal is to perform well on new and old data. We implement several state-of-the-art techniques to solve the Anomaly Detection problem in the classic setting and adapt them to work in the Continual Learning setting. To validate the approaches, we use a real-world dataset of images with pixel-based anomalies to provide a reliable benchmark and serve as a foundation for further advancements in the field. We provide a comprehensive analysis, discussing which Anomaly Detection methods and which families of approaches seem more suitable for the Continual Learning setting.

关键词： Anomaly Detection computer vision Continual Learning

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HaLViT: Half of the Weights are Enough

HaLViT: Half of the Weights are Enough

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Koyun, Onur Can Toreyin, Behcet Ugur Istanbul Tech Univ Dept Artificial Intelligence & Data Engn Signal Proc Computat Intelligence Res Grp SP4CING Inst Informat Istanbul Turkiye

ISBN: (纸本)9798350365474

Deep learning architectures like Transformers and Convolutional Neural Networks (CNNs) have led to ground-breaking advances across numerous fields. However, their extensive need for parameters poses challenges for implementation in environments with limited resources. In our research, we propose a strategy that focuses on the utilization of the column and row spaces of weight matrices, significantly reducing the number of required model parameters without substantially affecting performance. This technique is applied to both Bottleneck and Attention layers, achieving a notable reduction in parameters with minimal impact on model efficacy. Our proposed model, HaLViT, exemplifies a parameter-efficient vision Transformer. Through rigorous experiments on the ImageNet dataset and COCO dataset, HaLViT's performance validates the effectiveness of our method, offering results comparable to those of conventional models.

关键词： CNN computer vision deep learning Efficient Parameter Transformer vision Transformer ViT

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Prompt Learning with One-Shot Setting based Feature Space Analysis in vision-and-Language Models

Prompt Learning with One-Shot Setting based Feature Space An...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Hirohashi, Yuki Hirakawa, Tsubasa Yamashita, Takayoshi Fujiyoshi, Hironobu OMRON Corp Kyoto Japan Chubu Univ Kasugai Aichi Japan

ISBN: (纸本)9798350365474

By using few-shot data and labels, prompt learning obtains optimal prompts that are capable of achieving high performance on downstream tasks. Existing prompt learning methods generate high-quality prompts that are suitable for downstream tasks but tend to perform poorly in scenarios where only very limited data (e.g., one-shot) is available. We address on this challenging one-shot scenario and propose a novel architecture for prompt learning, called Image-Text Feature Alignment Branch (ITFAB). ITFAB aligns text features closer to the centroids of image features and separates text features with different classes to resolve misalignment in the feature space, thereby facilitating the acquisition of high-quality prompts with very limited data. In one-shot setting, our method outperforms the existing CoOp and CoCoOp methods and in some cases even surpasses CoCoOp's 16-shot performance. Testing on different datasets and domain, show that ITFAB almost matches CoCoOp's effectiveness. It also works with current prompt learning methods like MapLe and PromptSRC, improving their performance in one-shot setting.

关键词： Prompt Learning vision-and-Language Model

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Federated Learning with a Single Shared Image

Federated Learning with a Single Shared Image

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Soni, Sunny Saeed, Aaqib Asano, Yuki M. Univ Amsterdam Amsterdam Netherlands TU Eindhoven Eindhoven Netherlands

ISBN: (纸本)9798350365474

Federated Learning (FL) enables multiple machines to collaboratively train a machine learning model without sharing of private training data. Yet, especially for heterogeneous models, a key bottleneck remains the transfer of knowledge gained from each client model with the server. One popular method, FedDF, uses distillation to tackle this task with the use of a common, shared dataset on which predictions are exchanged. However, in many contexts such a dataset might be difficult to acquire due to privacy and the clients might not allow for storage of a large shared dataset. To this end, in this paper, we introduce a new method that improves this knowledge distillation method to only rely on a single shared image between clients and server. In particular, we propose a novel adaptive dataset pruning algorithm that selects the most informative crops generated from only a single image. With this, we show that federated learning with distillation under a limited shared dataset budget works better by using a single image compared to multiple individual ones. Finally, we extend our approach to allow for training heterogeneous client architectures by incorporating a non-uniform distillation schedule and client-model mirroring on the server side.

关键词： computer vision Federated Learning Limited Data Representation Learning

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HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing

HarvestNet: A Dataset for Detecting Smallholder Farming Acti...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Xu, Jonathan Elmustafa, Amna Weldegebriel, Liya Negash, Emnet Lee, Richard Meng, Chenlin Ermon, Stefano Lobell, David Stanford Univ Stanford CA 94305 USA Univ Waterloo Waterloo ON Canada Univ Ghent Ghent Belgium Mekelle Univ Mekele Ethiopia

ISBN: (纸本)9798350365474

Small farms contribute to a large share of the productive land in developing countries. In regions such as subSaharan Africa, where 80% of farms are small (under 2 ha in size), the task of mapping smallholder cropland is an important part of tracking sustainability measures such as crop productivity. However, the visually diverse and nuanced appearance of small farms has limited the effectiveness of traditional approaches to cropland mapping. Here we introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems throughout the world. We present HarvestNet, a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023, collected using expert knowledge and satellite images, totaling 7k hand-labeled images and 2k ground-collected labels. We also benchmark a set of baselines, including SOTA models in remote sensing, with our best models having around 80% classification performance on hand labelled data and 90% and 98% accuracy on ground truth data for Tigray and Amhara, respectively. We also perform a visual comparison with a widely used pre-existing coverage map and show that our model detects an extra 56,621 hectares of cropland in Tigray. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions. The dataset can be accessed through https://***/s/45a7b45556b90a9a11d2, while the code for the dataset and benchmarks is publicly available at https://***/jonxuxu/harvestpiles.

关键词： agriculture computer vision dataset harvest piles machine learning Remote sensing sustainability

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Multi-Modal Hit Detection and Positional Analysis in Padel Competitions

Multi-Modal Hit Detection and Positional Analysis in Padel C...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Decorte, Robbe Pare, Martin Vanhaeverbeke, Jelle Taelman, Joachim Slembrouck, Maarten Verstockt, Steven Univ Ghent imec IDLab Ghent Belgium

ISBN: (纸本)9798350365474

Padel is a rapidly growing racquet sport and has gained popularity globally due to its accessibility and exciting gameplay dynamics. Effective coordination between teammates hinges on maintaining an appropriate distance, allowing for seamless transitions between offensive and defensive maneuvers. A balanced inter-player distance and distance to the net not only facilitates efficient communication but also enhances the team's ability to exploit openings in the opponent's defense while minimizing vulnerabilities. We introduce a new open dataset of padel rallies with annotations for hits and player-ball interactions, a predictive model for detecting hits based on audio signals, a reidentification algorithm for pose tracking, and a framework for calculating inter-player and player-net distances during rallies. Our predictive model achieves an average F1-score of 92% for hit detection, demonstrating robust performance across different match conditions. Furthermore, we develop a system for accurately assigning hits to individual players, achieving an overall accuracy of 83.70% for player-specific assignment and 86.83% for team-based assignment.

关键词： computer vision Deep Learning Player Tracking Sound Event Detection Sports Data Science

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A Dual-Mode Approach for vision-Based Navigation in a Lunar Landing Scenario

A Dual-Mode Approach for Vision-Based Navigation in a Lunar ...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ostrogovich, Luca Del Prete, Roberto Tomasicchio, Giuseppe Longepe, Nicolas Renga, Alfredo Univ Naples Federico II Dept Ind Engn Naples Italy Telespazio SRL Rome Italy ESA ESRIN Lab Frascati Italy

ISBN: (纸本)9798350365474

In this research, a novel approach for autonomous spacecraft navigation, particularly in lunar contexts, is presented, focusing on vision-based techniques. The system incorporates lunar crater recognition in conjunction with feature tracking to enhance the accuracy of spacecraft navigation. This system underwent comprehensive evaluation through a purpose-built software simulation, replicating lunar conditions for thorough evaluation and refinement. The methodology integrates established navigational methods with advanced artificial intelligence algorithms, resulting in significant navigational accuracy. The system demonstrates precise capabilities in determining the spacecraft position, with an average accuracy of approximately 270 m for the absolute navigation mode, while the relative mode exhibited an average error of 27.4 m and 0.8 m in determining the horizontal and vertical lander displacements relative to terrain. Initial tests on embedded systems-akin to those on-board spacecraft-were conducted. These tests are pivotal in demonstrating the system's operational viability within the constraints of limited bandwidth and rapid processing requirements characteristic of space missions. The promising results from these tests suggest potential applicability in real-world space missions, enhancing autonomous navigation capabilities in lunar and potentially other extraterrestrial environments.

关键词： Lunar Navigation Onboard AI Visual Based Navigation

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GRAFIQS: Face Image Quality Assessment Using Gradient Magnitudes

GRAFIQS: Face Image Quality Assessment Using Gradient Magnit...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kolf, Jan Niklas Damer, Naser Boutros, Fadi Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9798350365474

Face Image Quality Assessment (FIQA) estimates the utility of face images for automated face recognition (FR) systems. We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-trained FR model weights to minimize differences between testing samples and the distribution of the FR training dataset. To achieve that, we propose quantifying the discrepancy in Batch Normalization statistics (BNS), including mean and variance, between those recorded during FR training and those obtained by processing testing samples through the pretrained FR model. We then generate gradient magnitudes of pretrained FR weights by backpropagating the BNS through the pretrained model. The cumulative absolute sum of these gradient magnitudes serves as the FIQ for our approach. Through comprehensive experimentation, we demonstrate the effectiveness of our training-free and quality labeling-free approach, achieving competitive performance to recent state-of-the-art FIQA approaches without relying on quality labeling, the need to train regression networks, specialized architectures, or designing and optimizing specific loss functions.

关键词： Biometrics computer vision Face Image Quality Assessment Face recognition

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Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling

Label-free Anomaly Detection in Aerial Agricultural Images w...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Shikhar, Sambal Sobti, Anupam Plaksha Univ Mohali Punjab India

ISBN: (纸本)9798350365474

Detecting various types of stresses (nutritional, water, nitrogen, etc.) in agricultural fields is critical for farmers to ensure maximum productivity. However, stresses show up in different shapes and sizes across different crop types and varieties. Hence, this is posed as an anomaly detection task in agricultural images. Accurate anomaly detection in agricultural UAV images is vital for early identification of field irregularities. Traditional supervised learning faces challenges in adapting to diverse anomalies, necessitating extensive annotated data. In this work, we overcome this limitation with self-supervised learning using a masked image modeling approach. Masked Autoencoders (MAE) extract meaningful normal features from unlabeled image samples which produces high reconstruction error for the abnormal pixels during reconstruction. To remove the need of using only "normal" data while training, we use an anomaly suppression loss mechanism that effectively minimizes the reconstruction of anomalous pixels and allows the model to learn anomalous areas without explicitly separating "normal" images for training. Evaluation on the Agriculture-vision data challenge shows a 6.3% mIOU score improvement in comparison to prior state of the art in unsupervised and self-supervised methods. A single model generalizes across all the anomaly categories in the Agri-vision Challenge Dataset [5].

关键词： Anomaly Detection computer vision Masked Image Modelling Precision Agriculture UAV Images

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