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Embedded vision System for Atmospheric Turbulence Mitigation 29

Embedded Vision System for Atmospheric Turbulence Mitigation

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29th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Deshmukh, Ajinkya Bhosale, Gaurav Medasani, Swarup Reddy, Karthik Hemanthakumar, P. Chandrasekhar, A. Kirankumar, P. Vijayasagar, K. Uurmi Syst Pvt Ltd Hyderabad Andhra Pradesh India

ISBN: (纸本)9781509014378

Outdoor surveillance systems that involve farfield operations often encounter atmospheric turbulence perturbations due to a series of randomized reflections and refraction effecting incoming light rays. The resulting distortions make it hard to discriminate between true moving objects and turbulence induced motion. Current algorithms are not effective in detecting true moving objects in the scene and also rely on computationally complex warping methods. In this paper, we describe a real time embedded solution connected with traditional cameras to both rectify turbulence distortions and reliably detect and track true moving targets. Our comparisons with other methods shows better turbulence rectification with less false and miss detections. FPGA-DSP based embedded realization of our algorithm achieves nearly 15x speed-up along with lesser memory requirement over a quad core PC implementation. The proposed system is suitable for persistence surveillance systems and optical sight devices.

关键词： Atmospheric turbulence

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3DQ-Nets: Visual Concepts Emerge in Pose Equivariant 3D Quantized Neural Scene Representations

3DQ-Nets: Visual Concepts Emerge in Pose Equivariant 3D Quan...

引用

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

作者： Prabhudesai, Mihir Lal, Shamit Tung, Hsiao-Yu Fish Harley, Adam W. Potdar, Shubhankar Fragkiadaki, Katerina Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781728193601

Concept learning lies at the very heart of intelligence, providing organizing principles with which to comprehend the world (6). Most computer vision models learn concept classifiers or detectors using labelled examples of object boxes, poses and categories. Self-supervised or unsupervised approaches mostly focus on (pre)training CNNs on auxiliary pretext tasks to lessen the need for human labels for a downstream recognition task. The visual "concepts" learnt from pretext tasks are implicit and represented as distributed neural CNN activations (7). We see the following limitations with representing concepts (solely) as neural activations across multiple layers of a deep network: i) visual memory and computation are not separated (3), which means that computation increases exponentially with the number of visual concepts learnt, ii) concepts are not stored somewhere and cannot be referred to or retrieved on demand, iii) the number of concepts cannot increase automatically based on new visual experiences, rather it is determined by the processing architecture;this misaligns with the idea that animals are capable of spontaneous concept instantiation in novel scenes (2), iv) concepts cannot be mentally manipulated by imagining variations, transformations or mental simulations (8), v) concepts do not have any spatial dimension and are hard to use for spatial reasoning. © 2020 IEEE.

关键词： computer vision

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WiFi and vision Multimodal Learning for Accurate and Robust Device -Free Human Activity recognition 32

WiFi and Vision Multimodal Learning for Accurate and Robust ...

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zou, Han Yang, Jianfei Das, Hari Prasanna Liu, Huihan Zhou, Yuxun Spanos, Costas J. Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781728125060

Human activity recognition plays an indispensable role in a myriad of emerging applications in context-aware services. Accurate activity recognition systems usually require the user to carry mobile or wearable devices, which is inconvenient for long term usage. In this paper, we design WiVi, a novel human activity recognition scheme that is able to identify common human activities in an accurate and device free manner via multimodal machine learning using only commercial WiFi-enabled IoT devices and camera. For sensing using WiFi, a new platform is developed to extract fine-grained WiFi channel information and transform them into WiFi frames. A tailored convolutional neural network model is designed to extract high-level representative features among the WiFi frames in order to provide human activity estimation. We utilized a variant of C3D model for activity sensing using vision. Following this, WiVi performs multimodal fusion at the decision level to combine the strength of WiFi and vision by constructing an ensembled DNN model. Extensive experiments are conducted in an indoor environment, demonstrating that WiVi achieves 97.5% activity recognition accuracy and is robust under unfavorable situations, as each modality provides the complementary sensing when the other faces its limiting conditions.

关键词： Information services

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Cascaded Siamese Self-supervised Audio to Video GAN

Cascaded Siamese Self-supervised Audio to Video GAN

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

作者： Aldausari, Nuha Sowmya, Arcot Marcus, Nadine Mohammadi, Gelareh Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Generating meaningful videos that are synchronised to audio signals is a complex synthesis task that requires generation of not only realistic videos but also coherent video motions that conform to the provided audio signals. While tremendous effort has been expended on audio-to-video generative models, these models rely heavily on supervised signals such as face/body key points or 3D meshes. However, key point annotation requires time and effort. Besides, some dataset domains do not have predictable structure, which makes the extraction of points of interest infeasible. Our proposed model consists of a cascaded generator-discriminator architecture that works at the pixel level to generate videos according to the associated soundtracks. It adopts a new self-supervised temporal augmentation technique to optimise the correlation between the audio signal and the generated video instead of relying on supervised signals. The proposed architecture has proven its effectiveness in extensive experiments that compared different models across two datasets.

关键词： Solid modeling Correlation Three-dimensional displays Computational modeling conferences computer architecture pattern recognition

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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 ...

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

作者： Damer, Naser Lopez, Cesar Augusto Fontanillo Fang, Meiling Spiller, Noemie Pham, Minh Vu Boutros, Fadi Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany Katholieke Univ Leuven Ctr IT & IP Law Leuven Belgium

ISBN: (纸本)9781665487399

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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The Unreasonable Effectiveness of CLIP Features for Image Captioning: An Experimental Analysis

The Unreasonable Effectiveness of CLIP Features for Image Ca...

引用

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

作者： Barraco, Manuele Cornia, Marcella Cascianelli, Silvia Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Generating textual descriptions from visual inputs is a fundamental step towards machine intelligence, as it entails modeling the connections between the visual and textual modalities. For years, image captioning models have relied on pre-trained visual encoders and object detectors, trained on relatively small sets of data. Recently, it has been observed that large-scale multi-modal approaches like CLIP (Contrastive Language-Image Pre-training), trained on a massive amount of image-caption pairs, provide a strong zero-shot capability on various vision tasks. In this paper, we study the advantage brought by CLIP in image captioning, employing it as a visual encoder. Through extensive experiments, we show how CLIP can significantly out-perform widely-used visual encoders and quantify its role under different architectures, variants, and evaluation protocols, ranging from classical captioning performance to zero-shot transfer.

关键词： Visualization computer vision Protocols conferences Detectors Distance measurement Data models

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Cross Transferring Activity recognition to Word Level Sign Language Detection

Cross Transferring Activity Recognition to Word Level Sign L...

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

作者： Radhakrishnan, Srijith Mohan, Nikhil C. Varma, Manisimha Varma, Jaithra Pai, Smitha N. Manipal Acad Higher Educ Manipal Inst Technol Dept Informat & Commun Technol Manipal 576104 Karnataka India Manipal Acad Higher Educ Manipal Inst Technol Dept Comp Sci & Engn Manipal 576104 Karnataka India Manipal Acad Higher Educ Manipal Inst Technol Dept Data Sci & Comp Applicat Manipal 576104 Karnataka India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The lack of large scale labelled datasets in word-level sign language recognition (WSLR) poses a challenge to detecting sign language from videos. Most WSLR approaches operate on datasets that do not model real-world settings very well, as they do not have a high degree of variability in terms of signers, background, lighting and inter signer variation. We chose the MS-ASL dataset to overcome these limitations as they model open-world settings very well. This paper benchmarks successful action recognition architectures on the MS-ASL dataset using transfer learning. We have achieved new state-of-the-art accuracy (92.35%) with an improvement of 7.03% over the previous state-of-the-art introduced by the MS-ASL paper. We have analyzed how action-recognition architectures fair in the task of WSLR, and we propose SlowFast 8x8 ResNet 101 as a robust and suitable architecture for the task of WSLR.

关键词： computer vision conferences Transfer learning Lighting Gesture recognition computer architecture Assistive technologies

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Patch-wise Contrastive Style Learning for Instagram Filter Removal

Patch-wise Contrastive Style Learning for Instagram Filter R...

引用

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

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

ISBN: (纸本)9781665487399

Image-level corruptions and perturbations degrade the performance of CNNs on different downstream vision tasks. Social media filters are one of the most common resources of various corruptions and perturbations for real-world visual analysis applications. The negative effects of these distractive factors can be alleviated by recovering the original images with their pure style for the inference of the downstream vision tasks. Assuming these filters substantially inject a piece of additional style information to the social media images, we can formulate the problem of recovering the original versions as a reverse style transfer problem. We introduce Contrastive Instagram Filter Removal Network (CIFR), which enhances this idea for Instagram filter removal by employing a novel multi-layer patch-wise contrastive style learning mechanism. Experiments show our proposed strategy produces better qualitative and quantitative results than the previous studies. Moreover, we present the results of our additional experiments for proposed architecture within different settings. Finally, we present the inference outputs and quantitative comparison of filtered and recovered images on localization and segmentation tasks to encourage the main motivation for this problem.

关键词： Training Matched filters Social networking (online) Perturbation methods Multimedia Web sites Semantics computer architecture

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Beyond VQA: Generating Multi-word Answers and Rationales to Visual Questions

Beyond VQA: Generating Multi-word Answers and Rationales to ...

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

作者： Dua, Radhika Kancheti, Sai Srinivas Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India

ISBN: (纸本)9781665448994

Visual Question Answering is a multi-modal task that aims to measure high-level visual understanding. Contemporary VQA models are restrictive in the sense that answers are obtained via classification over a limited vocabulary (in the case of open-ended VQA), or via classification over a set of multiple-choice-type answers. In this work, we present a completely generative formulation where a multi-word answer is generated for a visual query. To take this a step forward, we introduce a new task: ViQAR (Visual Question Answering and Reasoning), wherein a model must generate the complete answer and a rationale that seeks to justify the generated answer. We propose an end-to-end architecture to solve this task and describe how to evaluate it. We show that our model generates strong answers and rationales through qualitative and quantitative evaluation, as well as through a human Turing Test.

关键词： Deep learning Visualization Vocabulary Computational modeling conferences computer architecture Knowledge discovery

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Guidance Network with Staged Learning for Image enhancement

Guidance Network with Staged Learning for Image enhancement

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

作者： Liang, Luming Zharkov, Ilya Amjadi, Faezeh Joze, Hamid Reza Vaezi Pradeep, Vivek Microsoft One Microsoft Way Redmond WA 98052 USA

ISBN: (纸本)9781665448994

Many important yet not fully resolved problems in computational photography and image enhancement, e.g. generating well-lit images from their low-light counterparts or producing RGB images from their RAW camera inputs share a common nature: discovering a color mapping between input pixels to output pixels based on both global information and local details. We propose a novel deep neural network architecture to learn the RAW to RGB mapping based on this common nature. This architecture consists of both global and local sub-networks, where the first sub-network focuses on determining illumination and color mapping, the second sub-network deals with recovering image details. The result of the global network serves as a guidance to the local network to form the final RGB images. Our method outperforms state-of-the-art with a significantly smaller size of network features on various image enhancement tasks.

关键词： Training Photography Image color analysis Lighting computer architecture Cameras Service-oriented architecture

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