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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1781-1790 订阅

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Yoga-82: A New Dataset for Fine-grained Classification of Human Poses

Yoga-82: A New Dataset for Fine-grained Classification of Hu...

引用

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

作者： Verma, Manisha Kumawat, Sudhakar Nakashima, Yuta Raman, Shanmuganathan Osaka Univ Osaka Japan Indian Inst Technol Gandhinagar Gandhinagar India

ISBN: (纸本)9781728193601

Human pose estimation is a well-known problem in computer vision to locate joint positions. Existing datasets for learning of poses are observed to be not challenging enough in terms of pose diversity, object occlusion and view points. This makes the pose annotation process relatively simple and restricts the application of the models that have been trained on them. To handle more variety in human poses, we propose the concept of fine-grained hierarchical pose classification, in which we formulate the pose estimation as a classification task, and propose a dataset, Yoga-82, for large-scale yoga pose recognition with 82 classes. Yoga-82 consists of complex poses where fine annotations may not be possible. To resolve this, we provide hierarchical labels for yoga poses based on the body configuration of the pose. The dataset contains a three-level hierarchy including body positions, variations in body positions, and the actual pose names. We present the classification accuracy of the state-of-the-art convolutional neural network architectures on Yoga-82. We also present several hierarchical variants of DenseNet in order to utilize the hierarchical labels.

关键词： computer vision

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Improving In-field Cassava Whitefly Pest Surveillance with Machine Learning

Improving In-field Cassava Whitefly Pest Surveillance with M...

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

作者： Tusubira, Jeremy Francis Nsumba, Solomon Ninsiima, Flavia Akera, Benjamin Acellam, Guy Nakatumba, Joyce Mwebaze, Ernest Quinn, John Oyana, Tonny Makerere Univ Artificial Intelligence Lab Kampala Uganda Google Res Mountain View CA USA Makerere Univ Geospatial Data & Computat Intelligence Lab Kampala Uganda

ISBN: (纸本)9781728193601

Whiteflies are the major vector responsible for the transmission of cassava related diseases in tropical environments, and knowing the numbers of whiteflies is key in detecting and identifying their spread and prevention. However, the current approach for counting whiteflies is a simple visual inspection, where a cassava leaf is turned upside down to reveal the underside where the whiteflies reside to enable a manual count. Repeated across many cassava farms, this task is quite tedious and time-consuming. In this paper, we propose a method to automatically count whiteflies using computer vision techniques. To implement this approach, we collected images of infested cassava leaves and trained a computer vision detector using Haar Cascade and Deep Learning techniques. The two techniques were used to identify the pest in images and return a count. Our results show that this novel method produces a white-fly count with high precision. This method could be applied to similar object detection scenarios similar to the whitefly problem with minor adjustments.

关键词： computer vision

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Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors

Privacy-friendly Synthetic Data for the Development of Face ...

引用

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

作者： Naser Damer Cé sar Augusto Fontanillo Ló pez Meiling Fang Noé mie Spiller Minh Vu Pham Fadi Boutros Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Centre for IT &#x0026 IP Law KU Leuven Leuven Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

The main question this work aims at answering is: "can morphing attack detection (MAD) solutions be successfully developed based on synthetic data?". Towards that, this work introduces the first synthetic-based MAD development dataset, namely the Synthetic Morphing Attack Detection Development dataset (SMDD). This dataset is utilized successfully to train three MAD backbones where it proved to lead to high MAD performance, even on completely unknown attack types. Additionally, an essential aspect of this work is the detailed legal analyses of the challenges of using and sharing real biometric data, rendering our proposed SMDD dataset extremely essential. The SMDD dataset, consisting of 30,000 attack and 50,000 bona fide samples, is publicly available for research purposes 1 .

关键词： Training Data privacy computer vision Law Face recognition conferences Training data

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Video Action Detection: Analysing Limitations and Challenges

Video Action Detection: Analysing Limitations and Challenges

引用

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

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Beyond possessing large enough size to feed data hungry machines (eg, transformers), what attributes measure the quality of a dataset? Assuming that the definitions of such attributes do exist, how do we quantify among their relative existences? Our work attempts to explore these questions for video action detection. The task aims to spatio-temporally localize an actor and assign a relevant action class. We first analyze the existing datasets on video action detection and discuss their limitations. Next, we propose a new dataset, Multi Actor Multi Action (MAMA) which overcomes these limitations and is more suitable for real world applications. In addition, we perform a biasness study which analyzes a key property differentiating videos from static images: the temporal aspect. This reveals if the actions in these datasets really need the motion information of an actor, or whether they predict the occurrence of an action even by looking at a single frame. Finally, we investigate the widely held assumptions on the importance of temporal ordering: is temporal ordering important for detecting these actions? Such extreme experiments show existence of biases which have managed to creep into existing methods inspite of careful modeling.

关键词： pattern recognition conferences computer vision

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Decoupling Video and Human Motion: Towards Practical Event Detection in Athlete Recordings

Decoupling Video and Human Motion: Towards Practical Event D...

引用

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

作者： Einfalt, Moritz Lienhart, Rainer Univ Augsburg Augsburg Germany

ISBN: (纸本)9781728193601

In this paper we address the problem of motion event detection in athlete recordings from individual sports. In contrast to recent end-to-end approaches, we propose to use 2D human pose sequences as an intermediate representation that decouples human motion from the raw video information. Combined with domain-adapted athlete tracking, we describe two approaches to event detection on pose sequences and evaluate them in complementary domains: swimming and athletics. For swimming, we show how robust decision rules on pose statistics can detect different motion events during swim starts, with a F-1 score of over 91% despite limited data. For athletics, we use a convolutional sequence model to infer stride-related events in long and triple jump recordings, leading to highly accurate detections with 96% in F-1 score at only +/- 5ms temporal deviation. Our approach is not limited to these domains and shows the flexibility of pose-based motion event detection.

关键词： computer vision

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Generative Flows as a General Purpose Solution for Inverse Problems

Generative Flows as a General Purpose Solution for Inverse P...

引用

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

作者： José A. Chá vez San Pablo Catholic University Peru

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Due to the success of generative flows to model data distributions, they have been explored in inverse problems. Given a pre-trained generative flow, previous work proposed to minimize the 2-norm of the latent variables as a regularization term. The intuition behind it was to ensure high likelihood latent variables that produce the closest restoration. However, high-likelihood latent variables may generate unrealistic samples as we show in our experiments. We therefore propose a solver to directly produce high-likelihood reconstructions. We hypothesize that our approach could make generative flows a general purpose solver for inverse problems. Furthermore, we propose 1 × 1 coupling functions to introduce permutations in a generative flow. It has the advantage that its inverse does not require to be calculated in the generation process. Finally, we evaluate our method for denoising, deblurring, inpainting, and colorization. We observe a compelling improvement of our method over prior works.

关键词： Couplings computer vision Inverse problems conferences Noise reduction Data models pattern recognition

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An Upgraded Object Detection Model for Enhanced Perception and Decision Making in Autonomous Vehicles

An Upgraded Object Detection Model for Enhanced Perception a...

引用

2022 ieee International conference on Communications workshops, ICC workshops 2022

作者： Rawlley, Oshin Gupta, Shashank Birla Institute of Technology and Science Department of Computer Science and Information Systems Rajasthan Pilani India

ISBN: (纸本)9781665426718

In Internet of Vehicles (IoV), accurate object detection is one of the basic requirements for Autonomous Vehicles (AV s) and vision-based Self Driver Assistance System (VSDAS). With restricted processing power of sensor nodes and network bandwidth, enhanced object detection with low energy consumption and acceptable rate of accuracy is still a major issue that makes VSDAS untrust-worthy and unsustainable. Considering this situation, in this paper, the authors propose an upgraded Object Detection model for simulating the enhanced perception and decision making in autonomous vehicles. In this paper, we evaluate the performance of three methods namely Histogram of Ori-ented Gradients (HOG), Local Binary pattern (LBP), HAAR utilizing KITTI dataset. The results reveal that Haar exhibits higher detection rate than the other two methods. For the enhanced object detection, we utilize a frame similarity difference technique for filtering out the duplicate frames and generating key frames. Finally, an upgraded Haar-cascade classification algorithm is proposed for accurate and fast object detection. Our comprehensive experimental outcomes on the eminent publicly available dataset (KITTI) showed that our model not only significantly improves the performance of object detection however, also saves the energy consumption of edee devices. © 2022 ieee.

关键词： Object detection

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Hot-started NAS for Task-specific Embedded Applications

Hot-started NAS for Task-specific Embedded Applications

引用

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

作者： Lotte Hendrickx Wiebe Van Ranst Toon Goedemé EAVISE-PSI-ESAT KU Leuven Sint-Katelijne-Waver Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Neural architecture search (NAS) has proven its worth in discovering new neural networks. Combining the possibility to satisfy multiple objectives in one search, it is especially useful for getting the most out of embedded devices with limited resources. However, research into small and efficient neural networks precedes NAS. We investigate the influence of combining this pre-existing knowledge with NAS techniques, for which we propose to hot-start the NAS search with a human-designed optimal network. Our experiments show that doing so speeds up the NAS process significantly, but the resulting optimal model at the end is only marginally better. Since embedded devices are often used for a specific task, we also explore the impact of using a task-specific dataset in the NAS process. Our experiments demonstrate that for a constrained problem, a smaller network can be found as compared to a general problem.

关键词： Knowledge engineering computer vision conferences Neural networks Size measurement Search problems pattern recognition

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Take the Scenic Route: Improving Generalization in vision-and-Language Navigation

Take the Scenic Route: Improving Generalization in Vision-an...

引用

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

作者： Yu, Felix Deng, Zhiwei Narasimhan, Karthik Russakovsky, Olga Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781728193601

In the vision-and-Language Navigation (VLN) task, an agent with egocentric vision navigates to a destination given natural language instructions. The act of manually annotating these instructions is timely and expensive, such that many existing approaches automatically generate additional samples to improve agent performance. However, these approaches still have difficulty generalizing their performance to new environments. In this work, we investigate the popular Room-to-Room (R2R) VLN benchmark and discover that what is important is not only the amount of data you synthesize, but also how you do it. We find that shortest path sampling, which is used by both the R2R benchmark and existing augmentation methods, encode biases in the action space of the agent which we dub as action priors. We then show that these action priors offer one explanation toward the poor generalization of existing works. To mitigate such priors, we propose a path sampling method based on random walks to augment the data. By training with this augmentation strategy, our agent is able to generalize better to unknown environments compared to the baseline, significantly improving model performance in the process.

关键词： computer vision

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RIT-18: A Novel Dataset for Compositional Group Activity Understanding

RIT-18: A Novel Dataset for Compositional Group Activity Und...

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

作者： Chen, Junwen Hao, Haiting Hong, Hanbin Kong, Yu Rochester Inst Technol Golisano Coll Comp & Informat Sci Rochester NY 14623 USA

ISBN: (纸本)9781728193601

Group activity understanding is a challenging task as multiple people are involved, and their relations may vary over time. Currently, the literature of group activity is limited to group activity recognition, because videos are trimmed in very short duration and focus on a single activity. This slows down the progress in the group activity domain. In this paper, we propose a new large-scale untrimmed compositional group activity dataset RIT-18 based on the volleyball games captured from YouTube. Each clip in our dataset depicts an entire rally which spans the duration from serve to a point being scored. Comprehensive annotations including group activity labels, temporal boundaries of activities, key persons, and winning teams are provided. We describe group activity recognition, future activity anticipation, and rally-level winner prediction challenges, and evaluate several baseline methods over these challenges. We report their performance on our dataset and demonstrate further efforts need to be made. The dataset is available at https://***/actionlab/rit-18.

关键词： computer vision

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