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检索条件"任意字段=IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops"

共 8962 条记录，以下是1171-1180 订阅

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Robust Automatic Motorcycle Helmet Violation Detection for an Intelligent Transportation System

Robust Automatic Motorcycle Helmet Violation Detection for a...

引用

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

作者： Duong Nguyen-Ngoc Tran Long Hoang Pham Hyung-Joon Jeon Huy-Hung Nguyen Hyung-Min Jeon Tai Huu-Phuong Tran Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

Video surveillance-based automatic detection of motorcycle helmet usage can enhance the effectiveness of educational and enforcement initiatives aimed at boosting road safety. Current detection methods, however, have room for enhancement, such as the inability to pinpoint individual motorcycles or differentiate between drivers and passengers in terms of helmet usage. This paper introduces a framework designed to detect and identify individual motorcycles while recording specific helmet usage for riders. The proposed classification approach for helmet usage demonstrates increased efficiency in comparison to previous research. Our findings highlight the exceptional accuracy of deep learning, with our method achieving a score of 0.7754 on the AI City 2023 Challenge Track 5 public leaderboard.

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Simulating Task-Free Continual Learning Streams From Existing Datasets

Simulating Task-Free Continual Learning Streams From Existin...

引用

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

作者： Aristotelis Chrysakis Marie-Francine Moens Department of Computer Science KU Leuven Leuven Belgium

Task-free continual learning is the subfield of machine learning that focuses on learning online from a stream whose distribution changes continuously over time. In contrast, previous works evaluate task-free continual learning using streams with distributions that change not continuously, but only at a few distinct points in time. In order to address the discrepancy between the definition and evaluation of task-free continual learning, we propose a principled algorithm that can permute any labeled dataset into a stream that is continuously nonstationary. We empirically show that the streams generated by our algorithm are less structured than the ones conventionally used in the literature. Moreover, we use our simulated task-free streams to benchmark multiple methods applicable to the task-free setting. We hope that our work will allow other researchers to better evaluate learning performance on continuously nonstationary streams.

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Learnable Prompt for Few-Shot Semantic Segmentation in Remote Sensing Domain

Learnable Prompt for Few-Shot Semantic Segmentation in Remot...

引用

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

作者： Steve Andreas Immanuel Hagai Raja Sinulingga TelePIX Seoul South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Few-shot segmentation is a task to segment objects or regions of novel classes within an image given only a few annotated examples. In the generalized setting, the task extends to segment both the base and the novel classes. The main challenge is how to train the model such that the addition of novel classes does not hurt the base classes performance, also known as catastrophic forgetting. To mitigate this issue, we use SegGPT as our base model and train it on the base classes. Then, we use separate learnable prompts to handle predictions for each novel class. To handle various object sizes which typically present in remote sensing domain, we perform patch-based prediction. To address the discontinuities along patch boundaries, we propose a patch-and-stitch technique by re-framing the problem as an image inpainting task. During inference, we also utilize image similarity search over image embeddings for prompt selection and novel class filtering to reduce false positive predictions. Based on our experiments, our proposed method boosts the weighted mIoU of a simple fine-tuned SegGPT from 15.96 to 35.08 on the validation set of few-shot Open-EarthMap dataset given in the challenge.

关键词： computer vision Filtering Semantic segmentation conferences pattern recognition Remote sensing

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Deep Dehazing Powered by Image Processing Network

Deep Dehazing Powered by Image Processing Network

引用

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

作者： Guisik Kim Jinhee Park Junseok Kwon Korea Electronics Technology Institute Chung-Ang University Seoul Korea School of Computer Science and Engineering Chung-Ang University Seoul Korea

Image processing is a very fundamental technique in the field of low-level vision. However, with the development of deep learning over the past five years, most low-level vision methods tend to ignore this technique. Recent dehazing methods also refrain from using conventional image processing techniques, whereas only focusing on the development of new deep neural network (DNN) architectures. Unlike this recent trend, we show that image processing techniques are still competitive, if they are incorporated into DNNs. In this paper, we utilize conventional image processing techniques (i.e. curve adjustment, retinex decomposition, and multiple image fusion) for accurate dehazing. Moreover, we employ direct learning for stable dehazing performance. The proposed method can perform with low computational cost and easy to learn. The experimental results demonstrate that the proposed method produces accurate dehazing results compared to recent algorithms.

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Learning Optimized Low-Light Image Enhancement for Edge vision Tasks

Learning Optimized Low-Light Image Enhancement for Edge Visi...

引用

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

作者： S M A Sharif Azamat Myrzabekov Nodirkhuja Khujaev Roman Tsoy Seongwan Kim Jaeho Lee Opt-AI Inc. LG Sciencepark Seoul South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Low-light image enhancement (LLIE) has a significant role in edge vision applications (EVA). Despite its widespread practicability, the existing LLIE methods are impractical due to their high computational costs. This study proposed a framework to learn optimized low-light image enhancement to tackle the limitations of existing enhancement methods for accelerating EVA. The proposed framework incorporates a lightweight and mobile-friendly deep network. We optimized our proposed model with INT8 precision with a post-training quantization strategy and deployed it on an edge device. The LLIE model has achieved over 199 frames per second (FPS) on a low-power edge board. Additionally, we evaluated the practicability of an optimized model for accelerating the vision application of an edge environment. The experimental results illustrate that our optimized method can significantly accelerate the performance of SOTA vision algorithms in challenging low-light conditions for numerous everyday vision tasks, including object detection and image registration.

关键词： Performance evaluation YOLO Training Image registration Quantization (signal) Image edge detection Hardware

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Exploring the Impact of Dataset Bias on Dataset Distillation

Exploring the Impact of Dataset Bias on Dataset Distillation

引用

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

作者： Yao Lu Jianyang Gu Xuguang Chen Saeed Vahidian Qi Xuan Zhejiang University of Technology Zhejiang University Duke University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Dataset Distillation (DD) is a promising technique to synthesize a smaller dataset that preserves essential information from the original dataset. This synthetic dataset can serve as a substitute for the original large-scale one, and help alleviate the training workload. However, current DD methods typically operate under the assumption that the dataset is unbiased, overlooking potential bias issues within the dataset itself. To fill in this blank, we systematically investigate the influence of dataset bias on DD. Given that there are no suitable biased datasets for DD, we first construct two biased datasets, CMNIST-DD and CCIFAR10-DD, to establish a foundation for subsequent analysis. Then we utilize existing DD methods to generate synthetic datasets on CMNIST-DD and CCIFAR10-DD, and evaluate their performance following the standard process. Experiments demonstrate that biases present in the original dataset significantly impact the performance of the synthetic dataset in most cases, which highlights the necessity of identifying and mitigating biases in the original datasets during DD. Finally, we reformulate DD within the context of a biased dataset. Our code along with biased datasets are available at https://***/yaolu-zjut/Biased-DD.

关键词： Training computer vision Codes Prevention and mitigation conferences Mathematical models pattern recognition

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SrvfRegNet: Elastic Function Registration Using Deep Neural Networks

SrvfRegNet: Elastic Function Registration Using Deep Neural ...

引用

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

作者： Chen, Chao Srivastava, Anuj Florida State Univ Dept Stat Tallahassee FL 32306 USA

ISBN: (纸本)9781665448994

Registering functions (curves) using time warpings (re-parameterizations) is central to many computer vision and shape analysis solutions. While traditional registration methods minimize penalized-L-2 norm, the elastic Riemannian metric and square-root velocity functions (SRVFs) have resulted in significant improvements in terms of theory and practical performance. This solution uses the dynamic programming algorithm to minimize the L-2 norm between SRVFs of given functions. However, the computational cost of this elastic dynamic programming framework - O(nT(2)k) - where T is the number of time samples along curves, n is the number of curves, and k < T is a parameter - limits its use in applications involving big data. This paper introduces a deep-learning approach, named SRVF Registration Net or SrvfRegNet to overcome these limitations. SrvfRegNet architecture trains by optimizing the elastic metric-based objective function on the training data and then applies this trained network to the test data to perform fast registration. In case the training and the test data are from different classes, it generalizes to the test data using transfer learning, i.e., retraining of only the last few layers of the network. It achieves the state-of-the-art alignment performance albeit at much reduced computational cost. We demonstrate the efficiency and efficacy of this framework using several standard curve datasets.

关键词： Training Measurement computer vision Shape Transfer learning Training data Dynamic programming

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The Casual Conversations v2 Dataset : A diverse, large benchmark for measuring fairness and robustness in audio/vision/speech models

The Casual Conversations v2 Dataset : A diverse, large bench...

引用

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

作者： Bilal Porgali Vítor Albiero Jordan Ryda Cristian Canton Ferrer Caner Hazirbas Meta AI

This paper introduces a new large consent-driven dataset aimed at assisting in the evaluation of algorithmic bias and robustness of computer vision and audio speech models in regards to 11 attributes that are self-provided or labeled by trained annotators. The dataset includes 26,467 videos of 5,567 unique paid participants, with an average of almost 5 videos per person, recorded in Brazil, India, Indonesia, Mexico, Vietnam, Philippines, and the USA, representing diverse demographic characteristics. The participants agreed for their data to be used in assessing fairness of AI models and provided self-reported age, gender, language/dialect, disability status, physical adornments, physical attributes and geo-location information, while trained annotators labeled apparent skin tone using the Fitzpatrick Skin Type and Monk Skin Tone scales, and voice timbre. Annotators also labeled for different recording setups and per-second activity annotations.

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AffordanceLLM: Grounding Affordance from vision Language Models

AffordanceLLM: Grounding Affordance from Vision Language Mod...

引用

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

作者： Shengyi Qian Weifeng Chen Min Bai Xiong Zhou Zhuowen Tu Li Erran Li AWS AI Amazon

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Affordance grounding refers to the task of finding the area of an object with which one can interact. It is a fundamental but challenging task, as a successful solution requires the comprehensive understanding of a scene in multiple aspects including detection, localization, and recognition of objects with their parts, of geo-spatial configuration/layout of the scene, of 3D shapes and physics, as well as of the functionality and potential interaction of the objects and humans. Much of the knowledge is hidden and beyond the image content with the supervised labels from a limited training set. In this paper, we make an attempt to improve the generalization capability of the current affordance grounding by taking the advantage of the rich world, abstract, and human-object-interaction knowledge from pre-trained large-scale vision language models [40]. Under the AGD20K benchmark, our proposed model demonstrates a significant performance gain over the competing methods for in-the-wild object affordance grounding. We further demonstrate it can ground affordance for objects from random Internet images, even if both objects and actions are unseen during training.

关键词： Training Location awareness Three-dimensional displays Grounding Shape Affordances Performance gain

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Burst Image Super-Resolution with Base Frame Selection

Burst Image Super-Resolution with Base Frame Selection

引用

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

作者： Sanghyun Kim Minjung Lee Woohyeok Kim Deunsol Jung Jaesung Rim Sunghyun Cho Minsu Cho Pohang University of Science and Technology (POSTECH) South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Burst image super-resolution has been a topic of active research in recent years due to its ability to obtain a high resolution image using complementary information between multiple frames in the burst. In this work, we explore using burst shots with non-uniform exposures to confront real-world practical scenarios by introducing a new benchmark dataset, dubbed Non-uniformly Exposed Burst Image (NEBI), that includes the burst frames at varying exposure times to obtain a broader range of irradiance and motion characteristics within a scene. As burst shots with non-uniform exposures exhibit varying levels of degradation, fusing information of the burst shots into the first frame as a base frame may not result in optimal image quality. To address this limitation, we propose a Frame Selection Network (FSN) for non-uniform scenarios. This network seamlessly integrates into existing super-resolution methods in a plug-and-play manner with low computational cost. The comparative analysis reveals the effectiveness of the non-uniform setting for the practical scenario and our FSN on synthetic-/real- NEBI datasets.

关键词： Degradation Image quality computer vision conferences Superresolution Benchmark testing pattern recognition

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