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AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks

AsymmNet: Towards ultralight convolution neural networks usi...

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

作者： Yang, Haojin Shen, Zhen Zhao, Yucheng Hasso Plattner Inst Potsdam Germany Alibaba Cloud Hangzhou Peoples R China ByteDance Beijing Peoples R China

ISBN: (纸本)9781665448994

Deep convolutional neural networks (CNN) have achieved astonishing results in a large variety of applications. However, using these models on mobile or embedded devices is difficult due to the limited memory and computation resources. Recently, the inverted residual block becomes the dominating solution for the architecture design of compact CNNs. In this work, we comprehensively investigated the existing design concepts, rethink the functional characteristics of two pointwise convolutions in the inverted residuals. We propose a novel design, called asymmetrical bottlenecks. Precisely, we adjust the first pointwise convolution dimension, enrich the information flow by feature reuse, and migrate saved computations to the second pointwise convolution. Doing so we can further improve the accuracy without increasing the computation overhead. The asymmetrical bottlenecks can be adopted as a dropin replacement for the existing CNN blocks. We can thus create AsymmNet by easily stack those blocks according to proper depth and width conditions. Extensive experiments demonstrate that our proposed block design is more beneficial than the original inverted residual bottlenecks for mobile networks, especially useful for those ultralight CNNs within the regime of <220M MAdds. Code is available at https://***/Spark001/AsymmNet

关键词： Convolutional codes computer vision conferences Computational modeling Neural networks 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 ...

引用

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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Fourier Image Transformer

Fourier Image Transformer

引用

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

作者： Buchholz, Tim-Oliver Jug, Florian FMI Biomed Res Basel Switzerland Fdn Human Technopole Milan Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Transformer architectures show spectacular performance on NLP tasks and have recently also been used for tasks such as image completion or image classification. Here we propose to use a sequential image representation, where each prefix of the complete sequence describes the whole image at reduced resolution. Using such Fourier Domain Encodings (FDEs), an auto-regressive image completion task is equivalent to predicting a higher resolution output given a low-resolution input. Additionally, we show that an encoder-decoder setup can be used to query arbitrary Fourier coefficients given a set of Fourier domain observations. We demonstrate the practicality of this approach in the context of computed tomography (CT) image reconstruction. In summary, we show that Fourier Image Transformer (FIT) can be used to solve relevant image analysis tasks in Fourier space, a domain inherently inaccessible to convolutional architectures.

关键词： Image resolution Image coding Computed tomography computer architecture Image representation Transformers pattern recognition

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Detecting Stable Keypoints from Events through Image Gradient Prediction

Detecting Stable Keypoints from Events through Image Gradien...

引用

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

作者： Chiberre, Philippe Perot, Etienne Sironi, Amos Lepetit, Vincent PROPHESEE Paris France Univ Gustave Eiffel CNRS Ecole Ponts LIGM Marne La Vallee France

ISBN: (纸本)9781665448994

We present a method that detects stable keypoints from an event stream at high speed with a low memory footprint. Our key observation connects two points: It should be easier to reconstruct the image gradients rather than the image itself from the events, and the Harris corner detector, one of the most reliable keypoint detectors for short baseline regular images, depends on the image gradients, not the image. We therefore introduce a recurrent convolutional neural network to predict image gradients from events. As image gradients and events are correlated, this prediction task is relatively easy and we can keep this network very small. We train our network solely on synthetic data. Extracting Harris corners from these gradients is then very efficient. Moreover, in contrast to learned methods, we can change the hyperparameters of the detector without retraining. Our experiments confirm that predicting image gradients rather than images is much more efficient, and that our approach predicts stable corner points which are easier to track for a longer time compared to state-of-the-art event-based methods.

关键词： Image analysis Detectors computer architecture Streaming media Cameras pattern recognition Reliability

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Multi-encoder Network for Parameter Reduction of a Kernel-based Interpolation Architecture

Multi-encoder Network for Parameter Reduction of a Kernel-ba...

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

作者： Khalifeh, Issa Blanch, Marc Gorriz Izquierdo, Ebroul Mrak, Marta British Broadcasting Corp London W12 7TQ England Queen Mary Univ London London E1 4NS England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Video frame interpolation involves the synthesis of new frames from existing ones. Convolutional neural networks (CNNs) have been at the forefront of the recent advances in this field. One popular CNN-based approach involves the application of generated kernels to the input frames to obtain an interpolated frame. Despite all the benefits interpolation methods offer, many of these networks require a lot of parameters, with more parameters meaning a heavier computational burden. Reducing the size of the model typically impacts performance negatively. This paper presents a method for parameter reduction for a popular flow-less kernel-based network (Adaptive Collaboration of Flows). Through our technique of removing the layers that require the most parameters and replacing them with smaller encoders, we reduce the number of parameters of the network and even achieve better performance compared to the original method. This is achieved by deploying rotation to force each individual encoder to learn different features from the input images. Ablations are conducted to justify design choices and an evaluation on how our method performs on full-length videos is presented.

关键词： Training Interpolation computer vision conferences Force computer architecture pattern recognition

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Embedded vision System for Atmospheric Turbulence Mitigation 29

Embedded Vision System for Atmospheric Turbulence Mitigation

引用

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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Separating Texture and Illumination for Single-Shot Structured Light Reconstruction

Separating Texture and Illumination for Single-Shot Structur...

引用

IEEE conference on computer vision and pattern recognition (CVPR)

作者： Vo, Minh Narasimhan, Srinivasa G. Sheikh, Yaser Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)9781479943098

Active illumination based methods have a trade-off between acquisition time and resolution of the estimated 3D shapes. Multi-shot approaches can generate dense reconstructions but require stationary scenes. In contrast, singleshot methods are applicable to dynamic objects but can only estimate sparse reconstructions and are sensitive to surface texture. In this work, we develop a single-shot approach to produce dense reconstructions of highly textured objects. The key to our approach is an image decomposition scheme that can recover the illumination and the texture images from their mixed appearance. Despite the complex appearances of the illuminated textured regions, our method can accurately compute per pixel warps from the illumination pattern and the texture template to the observed image. The texture template is obtained by interleaving the projection sequence with an all-white pattern. Our estimated warping functions are reliable even with infrequent interleaved projection. Thus, we obtain detailed shape reconstruction and dense motion tracking of the textured surfaces. We validate the approach on synthetic and real data containing subtle non-rigid surface deformations.

关键词： Lighting illumination single-shot GRAIN pattern Image decomposition surface texture Reconstructive Surgical Procedures shape reconfiguration texture acquisition time structured light Reconstructions Dense

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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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Deep-FlexISP: A Three-Stage Framework for Night Photography Rendering

Deep-FlexISP: A Three-Stage Framework for Night Photography ...

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

作者： Liu, Shuai Feng, Chaoyu Wang, Xiaotao Wang, Hao Zhu, Ran Li, Yongqiang Lei, Lei Xiaomi Inc Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Night photography rendering is challenging due to images' high noise level, less vivid color, and low dynamic range. In this work, we propose a three-stage cascade framework named Deep-FlexISP, which decomposes the ISP into three weakly correlated sub-tasks: raw image denoising, white balance, and Bayer to sRGB mapping, for the following considerations. First, task decomposition can enhance the learning ability of the framework and make it easier to converge. Second, weak correlation sub-tasks do not influence each other too much, so the framework has a high degree of freedom. Finally, noise, color, and brightness are essential for night photographs. Our framework can flexibly adjust different styles according to personal preferences with the vital learning ability and the degree of freedom. Compared with the other Deep-ISP methods, our proposed Deep-FlexISP shows state-of-the-art performance and achieves first place in people's choice and photographer's choice in NTIRE 2022 Night Photography Render Challenge.

关键词： Photography Image color analysis Brightness Noise reduction Rendering (computer graphics) pattern recognition Task analysis

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A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings

A Modular Multimodal Architecture for Gaze Target Prediction...

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

作者： Gupta, Anshul Tafasca, Samy Odobez, Jean-Marc Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Predicting where a person is looking is a complex task, requiring to understand not only the person's gaze and scene content, but also the 3D scene structure and the person's situation (are they manipulating? interacting or observing others? attentive?) to detect obstructions in the line of sight or apply attention priors that humans typically have when observing others. In this paper, we hypothesize that identifying and leveraging such priors can be better achieved through the exploitation of explicitly derived multimodal cues such as depth and pose. We thus propose a modular multimodal architecture allowing to combine these cues using an attention mechanism. The architecture can naturally be exploited in privacy-sensitive situations such as surveillance and health, where personally identifiable information cannot be released. We perform extensive experiments on the GazeFollow and VideoAttentionTarget public datasets, obtaining state-of-the-art performance and demonstrating very competitive results in the privacy setting case. (1)

关键词： Three-dimensional displays Fuses Surveillance computer architecture Predictive models Skeleton pattern recognition

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