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共 6449 条记录，以下是711-720 订阅

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Learning to Detect Phone-related Pedestrian Distracted Behaviors with Synthetic Data

Learning to Detect Phone-related Pedestrian Distracted Behav...

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

作者： Hatay, Emre Ma, Jin Sun, Huiming Fang, Jianwu Gao, Zhiqiang Yu, Hongkai Cleveland State Univ Cleveland OH 44115 USA Changan Univ Xian Peoples R China

ISBN: (纸本)9781665448994

Due to the popularity and mobility of smart phones, phone-related pedestrian distracted behaviors, e.g., Texting, Game Playing, and Phone calls, have caused many traffic fatalities and accidents. As an advanced driver-assistance or autonomous-driving system, computer vision could be used to automatically detect distractions from cameras installed on the vehicle for useful safety intervention. The state-of-the-art method models this problem as a standard supervised learning method with a two-branch Convolutional Neural Network (CNN) followed by a voting on all image frames. In contrast, this paper proposes a new synthetic dataset named SYN-PPDB (448 synchronized video pairs of 53,760 computer game images) for this research problem and models it as a transfer learning problem from synthetic data to real data. A new deep learning model embedded with spatial-temporal feature learning and pose-aware transfer learning is proposed. Experimental results show that we could improve the state-of-the-art overall recognition accuracy from 84.27% to 96.67%.

关键词： computer vision Computational modeling Transfer learning Supervised learning Games pattern recognition Synchronization

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Image Reconstruction from Neuromorphic Event Cameras using Laplacian-Prediction and Poisson Integration with Spiking and Artificial Neural Networks

Image Reconstruction from Neuromorphic Event Cameras using L...

引用

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

作者： Duwek, Hadar Cohen Shalumov, Albert Tsur, Elishai Ezra Open Univ Israel Neurobiomorph Engn Lab NBEL Dept Math & Comp Sci Raanana Israel

ISBN: (纸本)9781665448994

Event cameras are robust neuromorphic visual sensors, which communicate transients in luminance as events. Current paradigm for image reconstruction from event data relies on direct optimization of artificial Convolutional Neural Networks (CNNs). Here we proposed a two-phase neural network, which comprises a CNN, optimized for Laplacian prediction followed by a Spiking Neural Network (SNN) optimized for Poisson integration. By introducing Laplacian prediction into the pipeline, we provide image reconstruction with a network comprising only 200 parameters. We converted the CNN to SNN, providing a full neuromorphic implementation. We further optimized the network with Mish activation and a novel convoluted CNN design, proposing a hybrid of spiking and artificial neural network with < 100 parameters. Models were evaluated on both N-MNIST and N-Caltech101 datasets.

关键词： Visualization Laplace equations Neuromorphics Pipelines Cameras Sensors pattern recognition

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Image Compression with Recurrent Neural Network and Generalized Divisive Normalization

Image Compression with Recurrent Neural Network and Generali...

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

作者： Islam, Khawar Dang, L. Minh Lee, Sujin Moon, Hyeonjoon Sejong Univ Comp Vis & Pattern Recognit Lab Seoul South Korea Sejong Univ Dept Artificial Intelligence Seoul South Korea

ISBN: (纸本)9781665448994

Image compression is a method to remove spatial redundancy between adjacent pixels and reconstruct a high-quality image. In the past few years, deep learning has gained huge attention from the research community and produced promising image reconstruction results. Therefore, recent methods focused on developing deeper and more complex networks, which significantly increased network complexity. In this paper, two effective novel blocks are developed: analysis and synthesis block that employs the convolution layer and Generalized Divisive Normalization (GDN) in the variablerate encoder and decoder side. Our network utilizes a pixel RNN approach for quantization. Furthermore, to improve the whole network, we encode a residual image using LSTM cells to reduce unnecessary information. Experimental results demonstrated that the proposed variable-rate framework with novel blocks outperforms existing methods and standard image codecs, such as George's [11] and JPEG in terms of image similarity. The project page along with code and models are available at https://***/khawar512/cvpr image compress

关键词： Image coding Recurrent neural networks Quantization (signal) Convolution Redundancy Transform coding Decoding

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Single View Geocentric Pose in the Wild

Single View Geocentric Pose in the Wild

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

作者： Christie, Gordon Foster, Kevin Hagstrom, Shea Hager, Gregory D. Brown, Myron Z. Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA

ISBN: (纸本)9781665448994

Current methods for Earth observation tasks such as semantic mapping, map alignment, and change detection rely on near-nadir images;however, often the first available images in response to dynamic world events such as natural disasters are oblique. These tasks are much more difficult for oblique images due to observed object parallax. There has been recent success in learning to regress an object's geocentric pose, defined as height above ground and orientation with respect to gravity, by training with airborne lidar registered to satellite images. We present a model for this novel task that exploits affine invariance properties to outperform state of the art performance by a wide margin. We also address practical issues required to deploy this method in the wild for real-world applications. Our data and code are publicly available(1).

关键词： Training Earth computer vision Satellites Laser radar conferences Semantics

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Essentials for Class Incremental Learning

Essentials for Class Incremental Learning

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

作者： Mittal, Sudhanshu Galesso, Silvio Brox, Thomas Univ Freiburg Freiburg Germany

ISBN: (纸本)9781665448994

Contemporary neural networks are limited in their ability to learn from evolving streams of training data. When trained sequentially on new or evolving tasks, their accuracy drops sharply, making them unsuitable for many real-world applications. In this work, we shed light on the causes of this well known yet unsolved phenomenon often referred to as catastrophic forgetting - in a class-incremental setup. We show that a combination of simple components and a loss that balances intra-task and inter-task learning can already resolve forgetting to the same extent as more complex measures proposed in literature. Moreover, we identify poor quality of the learned representation as another reason for catastrophic forgetting in class-IL. We show that performance is correlated with secondary class information (dark knowledge) learned by the model and it can be improved by an appropriate regularizer. With these lessons learned, class-incremental learning results on CIFAR-100 and ImageNet improve over the state-of-the-art by a large margin, while keeping the approach simple.

关键词： Learning systems computer vision Art conferences Neural networks Training data Boosting

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Scaled 360 layouts: Revisiting non-central panoramas

Scaled 360 layouts: Revisiting non-central panoramas

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

作者： Berenguel-Baeta, Bruno Bermudez-Cameo, Jesus Guerrero, Jose J. Univ Zaragoza I3A Zaragoza Spain

ISBN: (纸本)9781665448994

From a non-central panorama, 3D lines can be recovered by geometric reasoning. However, their sensitivity to noise and the complex geometric modeling required has led these panoramas being very little investigated. In this work we present a novel approach for 3D layout recovery of indoor environments using single non-central panoramas. We obtain the boundaries of the structural lines of the room from a non-central panorama using deep learning and exploit the properties of non-central projection systems in a new geometrical processing to recover the scaled layout. We solve the problem for Manhattan environments, handling occlusions, and also for Atlanta environments in an unified method. The experiments performed improve the state-of-the-art methods for 3D layout recovery from a single panorama. Our approach is the first work using deep learning with non-central panoramas and recovering the scale of single panorama layouts.

关键词： Deep learning computer vision Three-dimensional displays Sensitivity conferences Layout Neural networks

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Generalizable Multi-Camera 3D Pedestrian Detection

Generalizable Multi-Camera 3D Pedestrian Detection

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

作者： Lima, Joao Paulo Roberto, Rafael Figueiredo, Lucas Simoes, Francisco Teichrieb, Veronica Univ Fed Pernambuco Ctr Informat Voxar Labs Recife PE Brazil Univ Fed Rural Pernambuco Dept Comp Recife PE Brazil

ISBN: (纸本)9781665448994

We present a multi-camera 3D pedestrian detection method that does not need to train using data from the target scene. We estimate pedestrian location on the ground plane using a novel heuristic based on human body poses and person's bounding boxes from an off-the-shelf monocular detector. We then project these locations onto the world ground plane and fuse them with a new formulation of a clique cover problem. We also propose an optional step for exploiting pedestrian appearance during fusion by using a domain-generalizable person re-identification model. We evaluated the proposed approach on the challenging WILDTRACK dataset. It obtained a MODA of 0.569 and an F-score of 0.78, superior to state-of-the-art generalizable detection techniques.

关键词： Training computer vision Three-dimensional displays Sensitivity Fuses conferences Pose estimation

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LoL-V2T: Large-Scale Esports Video Description Dataset

LoL-V2T: Large-Scale Esports Video Description Dataset

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

作者： Tanaka, Tsunehiko Simo-Serra, Edgar Waseda Univ Tokyo Japan

ISBN: (纸本)9781665448994

Esports is a fastest-growing new field with a largely online-presence, and is creating a demand for automatic domain-specific captioning tools. However, at the current time, there are few approaches that tackle the esports video description problem. In this work, we propose a large-scale dataset for esports video description, focusing on the popular game "League of Legends". The dataset, which we call LoL-V2T, is the largest video description dataset in the video game domain, and includes 9,723 clips with 62,677 captions. This new dataset presents multiple new video captioning challenges such as large amounts of domain-specific vocabulary, subtle motions with large importance, and a temporal gap between most captions and the events that occurred. In order to tackle the issue of vocabulary, we propose a masking the domain-specific words and provide additional annotations for this. In our results, we show that the dataset poses a challenge to existing video captioning approaches, and the masking can significantly improve performance. Our dataset and code is publicly available(1).

关键词： Training Vocabulary computer vision Video description conferences Focusing Games

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Practical Cross-modal Manifold Alignment for Robotic Grounded Language Learning

Practical Cross-modal Manifold Alignment for Robotic Grounde...

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

作者： Nguyen, Andre T. Richards, Luke E. Kebe, Gaoussou Youssouf Raff, Edward Darvish, Kasra Ferraro, Frank Matuszek, Cynthia Booz Allen Hamilton Mclean VA 22102 USA Univ Maryland Baltimore Cty Baltimore MD 21228 USA

ISBN: (纸本)9781665448994

We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world items. Our approach learns these embeddings by sampling triples of anchor, positive, and negative data points from RGB-depth images and their natural language descriptions. We show that our approach can benefit from, but does not require, post-processing steps such as Pro-crustes analysis, in contrast to some of our baselines which require it for reasonable performance. We demonstrate the effectiveness of our approach on two datasets commonly used to develop robotic-based grounded language learning systems, where our approach outperforms four baselines, including a state-of-the-art approach, across five evaluation metrics.

关键词： Manifolds Measurement Learning systems computer vision conferences Natural languages Robot sensing systems

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Instagram Filter Removal on Fashionable Images

Instagram Filter Removal on Fashionable Images

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

作者： Kinli, Furkan Ozcan, Baris Kirac, Furkan Ozyegin Univ Video Vis & Graph Lab Istanbul Turkey

ISBN: (纸本)9781665448994

Social media images are generally transformed by filtering to obtain aesthetically more pleasing appearances. However, CNNs generally fail to interpret both the image and its filtered version as the same in the visual analysis of social media images. We introduce Instagram Filter Removal Network (IFRNet) to mitigate the effects of image filters for social media analysis applications. To achieve this, we assume any filter applied to an image substantially injects a piece of additional style information to it, and we consider this problem as a reverse style transfer problem. The visual effects of filtering can be directly removed by adaptively normalizing external style information in each level of the encoder. Experiments demonstrate that IFRNet outperforms all compared methods in quantitative and qualitative comparisons, and has the ability to remove the visual effects to a great extent. Additionally, we present the filter classification performance of our proposed model, and analyze the dominant color estimation on the images unfiltered by all compared methods.

关键词： computer vision Image color analysis Social networking (online) conferences Multimedia Web sites Estimation Information filters

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