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Deep Neural Networks and Convolution Neural Network for computer vision based Crime Similarity pattern Analysis 5

Deep Neural Networks and Convolution Neural Network for Comp...

引用

5th International conference on Smart Electronics and Communication, ICOSEC 2024

作者： Monika, E. Kumar, T. Rajesh Saveetha Institue of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331504403

In today's society, preventing and mitigating crime is crucial for maintaining public safety, promoting community well-being, and ensuring the overall security of a society. Each day, criminals perpetrate thousands of crimes. In the field of law enforcement and public safety, crime pattern similarity analysis is a crucial task that aims to unearth hidden linkages and repeating behaviors inside criminal episodes. Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) are two concepts of advanced neural network designs that can be integrated to improve understanding and prediction of crime patterns similarity analysis. This study explores the use of CNNs and DNNs in crime pattern similarity analysis, offering a novel strategy to help law enforcement organizations understand the complex relationships in crime-related data. This research aims to improve crime prevention, investigation, and public safety by exposing hidden patterns, foreseeing upcoming trends, and enabling data-driven decision-making. It is feasible to improve the model's convergence, stability, and capacity to learn efficiently from various and complicated surveillance image data by using RMSprop as an optimizer when training CNN and DNN models for crime similarity pattern identification in computer vision applications. © 2024 ieee.

关键词： Convolutional neural networks

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Blind Localization and Clustering of Anomalies in Textures

Blind Localization and Clustering of Anomalies in Textures

引用

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

作者： Andrei-Timotei Ardelean Tim Weyrich Friedrich-Alexander-Universität Erlangen-Nürnberg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Anomaly detection and localization in images is a growing field in computer vision. In this area, a seemingly understudied problem is anomaly clustering, i.e., identifying and grouping different types of anomalies in a fully unsupervised manner. In this work, we propose a novel method for clustering anomalies in largely stationary images (textures) in a blind setting. That is, the input consists of normal and anomalous images without distinction and without labels. What contributes to the difficulty of the task is that anomalous regions are often small and may present only subtle changes in appearance, which can be easily overshadowed by the genuine variance in the texture. Moreover, each anomaly type may have a complex appearance distribution. We introduce a novel scheme for solving this task using a combination of blind anomaly localization and contrastive learning. By identifying the anomalous regions with high fidelity, we can restrict our focus to those regions of interest; then, contrastive learning is employed to increase the separability of different anomaly types and reduce the intra-class variation. Our experiments show that the proposed solution yields significantly better results compared to prior work, setting a new state of the art. Project page: ***/pub/***.

关键词： Location awareness computer vision Image texture Art conferences Pipelines Contrastive learning

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MuIT: An End-to-End Multitask Learning Transformer

MuIT: An End-to-End Multitask Learning Transformer

引用

2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Bhattacharjee, Deblina Zhang, Tong Susstrunk, Sabine Salzmann, Mathieu School of Computer and Communication Sciences Epfl Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edge detection. Based on the Swin transformer model, our framework encodes the input image into a shared representation and makes predictions for each vision task using task-specific transformer-based decoder heads. At the heart of our approach is a shared attention mechanism modeling the dependencies across the tasks. We evaluate our model on several multitask benchmarks, showing that our MulT framework outperforms both the state-of-the art multitask convolutional neural network models and all the respective single task transformer models. Our experiments further highlight the benefits of sharing attention across all the tasks, and demonstrate that our MulT model is robust and generalizes well to new domains. Our project website is at https://***/MulT/. © 2022 ieee.

关键词： Heart Image segmentation Computational modeling Image edge detection Semantics Estimation Predictive models

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Point-Supervised Semantic Segmentation of Natural Scenes via Hyperspectral Imaging

Point-Supervised Semantic Segmentation of Natural Scenes via...

引用

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

作者： Tianqi Ren Qiu Shen Ying Fu Shaodi You Nanjing Univerisity Beijing Institute of Technology University of Amsterdam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Natural scene semantic segmentation is an important task in computer vision. While training accurate models for semantic segmentation relies heavily on detailed and accurate pixel-level annotations, which are hard and time-consuming to be collected especially for complicated natural scenes. Weakly-supervised methods can reduce labeling cost greatly at the expense of significant performance degradation. In this paper, we explore the possibility of introducing hyperspectral imaging to improve the performance of weakly-supervised semantic segmentation. We take two challenging hyperspectral datasets of outdoor natural scenes as example, and randomly label dozens of points with semantic categories to conduct a point-supervised semantic segmentation benchmark. Then, a spectral and spatial fusion method is proposed to generate detailed pixel-level annotations, which are used to supervise the semantic segmentation models. With multiple experiments we find that hyperspectral information can be greatly helpful to point-supervised semantic segmentation as it is more distinctive than RGB. As a result, our proposed method with only point-supervision can achieve approximate performance of the fully-supervised method in many cases. 1

关键词： Training Degradation computer vision Costs Accuracy Annotations Semantic segmentation

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What Makes Multimodal In-Context Learning Work?

What Makes Multimodal In-Context Learning Work?

引用

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

作者： Folco Bertini Baldassini Mustafa Shukor Matthieu Cord Laure Soulier Benjamin Piwowarski CNRS ISIR Sorbonne Université Paris France Valeo.ai Paris France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Large Language Models have demonstrated remarkable performance across various tasks, exhibiting the capacity to swiftly acquire new skills, such as through In-Context Learning (ICL) with minimal demonstration examples. In this work, we present a comprehensive framework for investigating Multimodal ICL (M-ICL) in the context of Large Multimodal Models. We consider the best open-source multimodal models (e.g., IDEFICS, OpenFlamingo) and a wide range of multimodal tasks. Our study unveils several noteworthy findings: (1) M-ICL primarily relies on text-driven mechanisms, showing little to no influence from the image modality. (2) When used with advanced-ICL strategy (like RICES), M-ICL is not better than a simple strategy based on majority voting over context examples. Moreover, we identify several biases and limitations of M-ICL that warrant consideration prior to deployment. Code available at ***/folbaeni/multimodal-icl

关键词： Training Analytical models computer vision Codes Large language models Impedance matching conferences

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

作者： Tarun Sharma Danelle E. Cline Duane Edgington California Institute of Technology USA MBARI USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

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： Location awareness computer vision Accuracy conferences Distributed databases Object detection Robustness

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Large-scale Dataset Pruning with Dynamic Uncertainty

Large-scale Dataset Pruning with Dynamic Uncertainty

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

作者： Muyang He Shuo Yang Tiejun Huang Bo Zhao Beijing Academy of Artificial Intelligence Peking University University of Technology Sydney

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The state of the art of many learning tasks, e.g., image classification, is advanced by collecting larger datasets and then training larger models on them. As the outcome, the increasing computational cost is becoming unaffordable. In this paper, we investigate how to prune the large-scale datasets, and thus produce an informative subset for training sophisticated deep models with negligible performance drop. We propose a simple yet effective dataset pruning method by exploring both the prediction uncertainty and training dynamics. We study dataset pruning by measuring the variation of predictions during the whole training process on large-scale datasets, i.e., ImageNet-1K and ImageNet-21K, and advanced models, i.e., Swin Transformer and ConvNeXt. Extensive experimental results indicate that our method outperforms the state of the art and achieves 25% lossless pruning ratio on both ImageNet1K and ImageNet-21K. The code and pruned datasets are available at https://***/BAAI-DCAI/Dataset-Pruning.

关键词： Training computer vision Uncertainty Computational modeling conferences Predictive models Transformers

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ShiftAddAug: Augment Multiplication-Free Tiny Neural Network with Hybrid Computation

ShiftAddAug: Augment Multiplication-Free Tiny Neural Network...

引用

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

作者： Yipin Guo Zihao Li Yilin Lang Qinyuan Ren College of Control Science and Engineering Zhejiang University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Operators devoid of multiplication, such as Shift and Add, have gained prominence for their compatibility with hardware. However, neural networks (NNs) employing these operators typically exhibit lower accuracy compared to conventional NNs with identical structures. ShiftAddAug uses costly multiplication to augment efficient but less powerful multiplication-free operators, improving performance without any inference overhead. It puts a ShiftAdd tiny NN into a large multiplicative model and encourages it to be trained as a sub-model to obtain additional supervision. In order to solve the weight discrepancy problem between hybrid operators, a new weight sharing method is proposed. Additionally, a novel two stage neural architecture search is used to obtain better augmentation effects for smaller but stronger multiplication-free tiny neural networks. The superiority of ShiftAddAug is validated through experiments in image classification and semantic segmentation, consistently delivering noteworthy enhancements. Remarkably, it secures up to a 4.95% increase in accuracy on the CIFAR100 compared to its directly trained counterparts, even surpassing the performance of multiplicative NNs.

关键词： Training computer vision Accuracy Computational modeling Semantic segmentation conferences Hardware

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Affine-based Deformable Attention and Selective Fusion for Semi-dense Matching

Affine-based Deformable Attention and Selective Fusion for S...

引用

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

作者： Hongkai Chen Zixin Luo Yurun Tian Xuyang Bai Ziyu Wang Lei Zhou Mingmin Zhen Tian Fang David McKinnon Yanghai Tsin Long Quan Apple Inc Hong Kong University of Science and Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Identifying robust and accurate correspondences across images is a fundamental problem in computer vision that enables various downstream tasks. Recent semi-dense matching methods emphasize the effectiveness of fusing relevant cross-view information through Transformer. In this paper, we propose several improvements upon this paradigm. Firstly, we introduce affine-based local attention to model cross-view deformations. Secondly, we present selective fusion to merge local and global messages from cross attention. Apart from network structure, we also identify the importance of enforcing spatial smoothness in loss design, which has been omitted by previous works. Based on these augmentations, our network demonstrate strong matching capacity under different settings. The full version of our network achieves state-of-the-art performance among semi-dense matching methods at a similar cost to LoFTR, while the slim version reaches LoFTR baseline’s performance with only 15% computation cost and 18% parameters.

关键词： Geometry Deformable models computer vision Costs Deformation Shape Fuses

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DGBD: Depth Guided Branched Diffusion for Comprehensive Controllability in Multi-View Generation

DGBD: Depth Guided Branched Diffusion for Comprehensive Cont...

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

作者： Hovhannes Margaryan Daniil Hayrapetyan Wenyan Cong Zhangyang Wang Humphrey Shi Picsart AI Research (PAIR) UT Austin SHI Labs @ Georgia Tech Oregon & UIUC

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents an innovative approach to multi-view generation that can be comprehensively controlled over both perspectives (viewpoints) and non-perspective attributes (such as depth maps). Our controllable dual-branch pipeline, named Depth Guided Branched Diffusion (DGBD), leverages depth maps and perspective information to generate images from alternative viewpoints while preserving shape and size fidelity. In the first DGBD branch, we fine-tune a pre-trained diffusion model on multi-view data, introducing a regularized batch-aware self-attention mechanism for multi-view consistency and generalization. Direct control over perspective is then achieved through cross-attention conditioned on camera position. Meanwhile, the second DGBD branch introduces non-perspective control using depth maps. Qualitative and quantitative experiments validate the effectiveness of our approach, surpassing or matching the performance of state-of-the-art novel view and multi-view synthesis methods.

关键词： Geometry computer vision Shape conferences Pipelines Text to image Cameras

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