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Three Stream Graph Attention Network using Dynamic Patch Selection for the classification of micro-expressions

Three Stream Graph Attention Network using Dynamic Patch Sel...

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

作者： Jain, Ankith Kumar, Rakesh Bhanu, Bir Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

To understand the genuine emotions expressed by humans during social interactions, it is necessary to recognize the subtle changes on the face (micro-expressions) demonstrated by an individual. Facial micro-expressions are brief, rapid, spontaneous gestures and non-voluntary facial muscle movements beneath the skin. Therefore, it is a challenging task to classify facial micro-expressions. This paper presents an end-to-end novel three-stream graph attention network model to capture the subtle changes on the face and recognize micro-expressions (MEs) by exploiting the relationship between optical flow magnitude, optical flow direction, and the node locations features. A facial graph representational structure is used to extract the spatial and temporal information using the three frames. The varying dynamic patch size of optical flow features is used to extract the local texture information across each landmark point. The network only utilizes the landmark points location features and optical flow information across these points and generates good results for the classification of MEs. A comprehensive evaluation of SAMM and the CASME II datasets demonstrates the high efficacy, efficiency, and generalizability of the proposed approach and achieves better results than the state-of-the-art methods.

关键词： Emotion recognition computer vision Face recognition conferences Feature extraction Skin Facial muscles

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vision-based Neural Scene Representations for Spacecraft

Vision-based Neural Scene Representations for Spacecraft

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

作者： Mergy, Anne Lecuyer, Gurvan Derksen, Dawa Izzo, Dario European Space Agcy Noordwijk NL-2201 AZ Noordwijk Netherlands

ISBN: (纸本)9781665448994

In advanced mission concepts with high levels of autonomy, spacecraft need to internally model the pose and shape of nearby orbiting objects. Recent works in neural scene representations show promising results for inferring generic three-dimensional scenes from optical images. Neural Radiance Fields (NeRF) have shown success in rendering highly specular surfaces using a large number of images and their pose. More recently, Generative Radiance Fields (GRAF) achieved full volumetric reconstruction of a scene from unposed images only, thanks to the use of an adversarial framework to train a NeRF. In this paper, we compare and evaluate the potential of NeRF and GRAF to render novel views and extract the 3D shape of two different spacecraft, the Soil Moisture and Ocean Salinity satellite of ESA's Living Planet Programme and a generic cube sat. Considering the best performances of both models, we observe that NeRF has the ability to render more accurate images regarding the material specularity of the spacecraft and its pose. For its part, GRAF generates precise novel views with accurate details even when parts of the satellites are shadowed while having the significant advantage of not needing any information about the relative pose.

关键词： Space vehicles computer vision Surface reconstruction Sea surface Satellites Three-dimensional displays Shape

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Cross-stream Selective Networks for Action recognition 32

Cross-stream Selective Networks for Action Recognition

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

作者： Pan, Bowen Sun, Jiankai Lin, Wuwei Wang, Limin Lin, Weiyao Shanghai Jiao Tong Univ Shanghai Peoples R China SenseTime Res Hong Kong Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728125060

Combining multiple information streams has shown obvious improvements in video action recognition. Most existing works handle each stream independently or perform a simple combination on temporally simultaneous samples in multi-streams, which fails to make full use of the stream wise complementary property due to the negligence of the temporal pattern gaps among streams. In this paper, we propose a cross-stream selective network (CSN) to properly integrate and evaluate information in multi-streams. The proposed CSN first introduces a local selective-sampling module (LSM), which can find asynchronous correspondences among streams and construct high-correlated sample groups across multiple information streams. This LSM can effectively deal with the temporal dis-alignment among different streams, leading to a better integration of cross-stream information. We further introduce a global adaptive weighting module (GAM). It adaptively evaluates the importance weights for each cross-stream sample group and selects temporally more important ones in action recognition. With the integration of cross-stream information, our GAM can obtain more reasonable importance than the existing single-stream weighting schemes. Extensive experiments on benchmark datasets of UCF101 and HMDB51 demonstrate the effectiveness of our approach over previous state-of-the-art methods.

关键词： computer vision

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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...

引用

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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Motion Aware Double Attention Network for Dynamic Scene Deblurring

Motion Aware Double Attention Network for Dynamic Scene Debl...

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

作者： Yang, Dan Yamac, Mehmet Huawei Technol Oy Finland Co Ltd Helsinki Finland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Motion deblurring in dynamic scenes is a challenging task when the blurring is caused by one or a combination of various reasons such as moving objects, camera movement, etc. Since event cameras can detect changes in intensity with a low latency, necessary motion information is inherently captured in event data, which could be quite useful for deblurring standard camera images. The degradation intensity does not show homogeneity across an image due to factors like object depth, speed, etc. We propose a two-branch network structure, Motion Aware Double Attention Network (MADANet), that pays special attention to areas with high blur. As part of the network, event data is first used by the high blur region segmentation module that creates a probability-like score for areas exhibiting high relative motion to the camera. Then, the event data is also injected to feature maps in the main body, where there is a second attention mechanism available for each branch. The effective usage of event data and two-level attention mechanisms makes the network very compact. During the experiment, it was shown that the proposed network could achieve state-of-the-art performance not only on the benchmark dataset from GoPro, but also on two newly collected datasets, one of which contains real event data.

关键词： Degradation Image segmentation Motion segmentation Dynamics Data visualization Cameras pattern recognition

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TinyOps: ImageNet Scale Deep Learning on Microcontrollers

TinyOps: ImageNet Scale Deep Learning on Microcontrollers

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

作者： Sadiq, Sulaiman Hare, Jonathon Maji, Partha Craske, Simon Merrett, Geoff, V Univ Southampton Southampton Hants England ARM Ltd Cambridge England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep Learning on microcontroller (MCU) based IoT devices is extremely challenging due to memory constraints. Prior approaches focus on using internal memory or external memories exclusively which limit either accuracy or latency. We find that a hybrid method using internal and external MCU memories outperforms both approaches in accuracy and latency. We develop TinyOps, an inference engine which accelerates inference latency of models in slow external memory, using a partitioning and overlaying scheme via the available Direct Memory Access (DMA) peripheral to combine the advantages of external memory (size) and internal memory (speed). Experimental results show that architectures deployed with TinyOps significantly outperform models designed for internal memory with up to 6% higher accuracy and importantly, 1.3-2.2x faster inference latency to set the state-of-the-art in TinyML ImageNet classification. Our work shows that the TinyOps space is more efficient compared to the internal or external memory design spaces and should be explored further for TinyML applications.

关键词： Deep learning computer vision Microcontrollers conferences Memory management pattern recognition Internet of Things

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Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks 32

Real-Time 6DOF Pose Relocalization for Event Cameras with St...

引用

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

作者： Anh Nguyen Thanh-Toan Do Caldwell, Darwin G. Tsagarakis, Nikos G. Ist Italiano Tecnol Genoa Italy Univ Liverpool Liverpool Merseyside England AIOZ Pte Ltd Singapore Singapore

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

We present a new method to relocalize the 6DOF pose of an event camera solely based on the event stream. Our method first creates the event image from a list of events that occurs in a very short time interval, then a Stacked Spatial LSTM Network (SP-LSTM) is used to learn the camera pose. Our SP-LSTM is composed of a CNN to learn deep features from the event images and a stack of LSTM to learn spatial dependencies in the image feature space. We show that the spatial dependency plays an important role in the relocalization task with event images and the SP-LSTM can effectively learn this information. The extensively experimental results on a publicly available dataset show that our approach outperforms recent state-of-the-art methods by a substantial margin, as well as generalizes well in challenging training/testing splits. The source code and trained models are available at https://***/ nqanh/pose_relocalization.

关键词： Cameras Robot vision systems Three-dimensional displays Geometry Task analysis Streaming media computer vision

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Multi-Camera Vehicle Tracking Based on Occlusion-aware and Inter-vehicle Information

Multi-Camera Vehicle Tracking Based on Occlusion-aware and I...

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

作者： Liu, Yuming Zhang, Xiaochun Zhang, Bingzhen Zhang, Xiaoyong Wang, Sen Xu, Jianrong Shenzhen Urban Transport Planning Ctr Co Ltd Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the demands of analyzing and predicting traffic flow for applications in smart cities, Multi-Target Multi-Camera vehicle Tracking(MTMCT) at the city scale has become a fundamental problem. The MTMCT is challenging due to the view variations, frequent occlusions, and similar vehicle models in the same camera. This work proposes an MTMCT framework based on occlusion-aware and intervehicle information that can effectively match vehicle tracklets. The occlusion-aware module segments the tracklets of an occluded and occluding vehicle pair. It recalculates the similarity of the complete tracklets, which can handle the occlusions and suppress false detections. This work proposes an inter-vehicle information module to improve the matching accuracy. The module can enhance the ability to distinguish similar vehicles under the same camera at different times. The proposed whole framework consists of four modules: (1) vehicle detection and feature extraction by re-identification models, (2) single-camera tracking (SCT) to produce initial tracklets with an occlusion-aware module, (3) tracklets similarity by inter-vehicle association, (4) clustering in adjacent cameras for multi-camera tracklets matching. The proposed method obtains IDF1 score of 0.8285 on the Track-1 multi-camera vehicle tracking task of the 2022 AI City Challenge.

关键词： computer vision Smart cities Vehicle detection conferences Cameras Feature extraction pattern recognition

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FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography

FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model...

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

作者： Yang, Julia Barnett, Alina Jade Donnelly, Jon Kishore, Satvik Fang, Jerry Schwartz, Fides Regina Chen, Chaofan Lo, Joseph Y. Rudin, Cynthia Duke Univ Durham NC 27708 USA Brigham & Womens Hosp 75 Francis St Boston MA 02115 USA Univ Maine Orono ME USA

ISBN: (纸本)9798350365474

Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-stakes environments, uninterpretable ("black box") deep learning models are unsuitable and there is a call in these fields to make interpretable models. Recent work in interpretable computer vision provides transparency to these formerly black boxes by utilizing prototypes for case-based explanations, achieving high accuracy in applications including mammography. However, these models struggle with precise feature localization, reasoning on large portions of an image when only a small part is relevant. This paper addresses this gap by proposing a novel multi-scale interpretable deep learning model for mammographic mass margin classification. Our contribution not only offers an interpretable model with reasoning aligned with radiologist practices, but also provides a general architecture for computer vision with user-configurable prototypes from coarse-to fine-grained prototypes.

关键词： breast cancer cancer computer vision deep learning interpretability interpretable machine learning machine learning mammography medical imaging neural networks

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Age and Gender Classification using Convolutional Neural Networks

Age and Gender Classification using Convolutional Neural Net...

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IEEE conference on computer vision and pattern recognition (CVPR)

作者： Levi, Gil Hassner, Tal Open Univ Israel Dept Math & Comp Sci Raanana Israel

ISBN: (纸本)9781467367592

Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. In this paper we show that by learning representations through the use of deep-convolutional neural networks (CNN), a significant increase in performance can be obtained on these tasks. To this end, we propose a simple convolutional net architecture that can be used even when the amount of learning data is limited. We evaluate our method on the recent Adience benchmark for age and gender estimation and show it to dramatically outperform current state-of-the-art methods.

关键词： Benchmark testing computer architecture Estimation Face Face recognition Neurons Training

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