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Proceedings - 2020 ieee/CVF conference on computer vision and pattern recognition, cvpr 2020

Proceedings - 2020 IEEE/CVF Conference on Computer Vision an...

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2020 ieee/CVF conference on computer vision and pattern recognition, cvpr 2020

The proceedings contain 2 papers. The topics discussed include: attention mechanism exploits temporal contexts: real-time 3D human pose reconstruction;and cascaded deep monocular 3D human pose estimation with evolutio...

ISBN: (纸本)9781728171685

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Fine grained pointing recognition for natural drone guidance

Fine grained pointing recognition for natural drone guidance

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

作者： Barbed, O. L. Azagra, P. Teixeira, L. Chli, M. Civera, J. Murillo, A. C. Univ Zaragoza DIIS I3A Zaragoza Spain Swiss Fed Inst Technol Vis Robot Lab Zurich Switzerland

ISBN: (纸本)9781728193601

Human action recognition systems are typically focused on identifying different actions, rather than fine grained variations of the same action. This work explores strategies to identify different pointing directions in order to build a natural interaction system to guide autonomous systems such as drones. Commanding a drone with hand-held panels or tablets is common practice but intuitive user-drone interfaces might have significant benefits. The system proposed in this work just requires the user to provide occasional high-level navigation commands by pointing the drone towards the desired motion direction. Due to the lack of data on these settings, we present a new benchmarking video dataset to validate our framework and facilitate future research on the area. Our results show good accuracy for pointing direction recognition, while running at interactive rates and exhibiting robustness to variability in user appearance, viewpoint, camera distance and scenery.

关键词： Drones

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The Role of 'Sign' and 'Direction' of Gradient on the Performance of CNN

The Role of 'Sign' and 'Direction' of Gradient on the Perfor...

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

作者： Agarwal, Akshay Singh, Richa Vatsa, Mayank IIIT Delhi Delhi India IIT Jodhpur Jodhpur Rajasthan India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

State-of-the-art deep learning models have achieved superlative performance across multiple computer vision applications such as object recognition, face recognition, and digits/character classification. Most of these models highly rely on the gradient information flows through the network for learning. By utilizing this gradient information, a simple gradient sign method based attack is developed to fool the deep learning models. However, the primary concern with this attack is the perceptibility of noise for large degradation in classification accuracy. This research address the question of whether an imperceptible gradient noise can be generated to fool the deep neural networks? For this, the role of sign function in the gradient attack is analyzed. The analysis shows that without-sign function, i.e. gradient magnitude, not only leads to a successful attack mechanism but the noise is also imperceptible to the human observer. Extensive quantitative experiments performed using two convolutional neural networks validate the above observation. For instance, AlexNet architecture yields 63.54% accuracy on the CIFAR-10 database which reduces to 0.0% and 26.39% when sign (i.e., perceptible) and without-sign (i.e., imperceptible) of the gradient is utilized, respectively. Further, the role of the direction of the gradient for image manipulation is studied. When an image is manipulated in the positive direction of the gradient, an adversarial image is generated. On the other hand, if the opposite direction of the gradient is utilized for image manipulation, it is observed that the classification error rate of the CNN model is reduced. On AlexNet, the error rate of 36.46% reduces to 4.29% when images of CIFAR-10 are manipulated in the negative direction of the gradient. To explore other enthusiastic results on multiple object databases, including CIFAR-100, fashion-MNIST, and SVHN, please refer to the full paper.

关键词： Databases Machine learning Computational modeling Object recognition computer vision Training Optimization

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AdaMT-Net: An Adaptive Weight Learning Based Multi-Task Learning Model For Scene Understanding

AdaMT-Net: An Adaptive Weight Learning Based Multi-Task Lear...

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

作者： Jha, Ankit Kumar, Awanish Banerjee, Biplab Chaudhuri, Subhasis Indian Inst Technol Dept Elect Engn Mumbai Maharashtra India Indian Inst Technol Ctr Studies Resources Engn Mumbai Maharashtra India

ISBN: (纸本)9781728193601

We tackle the problem of deep end-to-end multi-task learning (MTL) for visual scene understanding from monocular images in this paper. It is proven that learning several related tasks together helps in attaining improved performance per-task than training them independently. This is due to the fact that related tasks share important feature characteristics among themselves, which the MTL techniques can effectively explore for improved joint training. Based on this premise, we are interested in generic to specific feature extraction given the different tasks within a common framework. To this end, we propose a typical U-Net based encoder-decoder architecture called AdaMT-Net, where the densely-connected deep convolutional neural network (CNN) based feature encoder is shared among the tasks while the soft-attention based task-specific decoder modules produce the desired outcomes. One major issue in MTL is to assign the weights for the task-specific loss-terms in the final cumulative optimization function. As opposed to the manual approach, we propose a novel adaptive weight learning strategy by carefully exploring the loss-gradients per-task over the training iterations. Experimental results on the benchmark CityScapes, NYUv2, and ISPRS datasets confirm that AdaMT-Net achieves state-of-the-art performance on most of the evaluation metrics.

关键词： computer vision

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Multi-modal Dense Video Captioning

Multi-modal Dense Video Captioning

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

作者： Iashin, Vladimir Rahtu, Esa Tampere Univ Tampere Finland

ISBN: (纸本)9781728193601

Dense video captioning is a task of localizing interesting events from an untrimmed video and producing textual description (captions) for each localized event. Most of the previous works in dense video captioning are solely based on visual information and completely ignore the audio track. However, audio, and speech, in particular, are vital cues for a human observer in understanding an environment. In this paper, we present a new dense video captioning approach that is able to utilize any number of modalities for event description. Specifically, we show how audio and speech modalities may improve a dense video captioning model. We apply automatic speech recognition (ASR) system to obtain a temporally aligned textual description of the speech (similar to subtitles) and treat it as a separate input alongside video frames and the corresponding audio track. We formulate the captioning task as a machine translation problem and utilize recently proposed Transformer architecture to convert multi-modal input data into textual descriptions. We demonstrate the performance of our model on ActivityNet Captions dataset. The ablation studies indicate a considerable contribution from audio and speech components suggesting that these modalities contain substantial complementary information to video frames. Furthermore, we provide an in-depth analysis of the ActivityNet Caption results by leveraging the category tags obtained from original YouTube videos. Code is publicly available: ***/v-iashin/MDVC.

关键词： Speech recognition

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Fine-Grained recognition in High-throughput Phenotyping

Fine-Grained Recognition in High-throughput Phenotyping

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

作者： Lyu, Beichen Smith, Stuart D. Cherkauer, Keith A. Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781728193601

Fine-Grained recognition aims to classify sub-category objects such as bird species and car models from imagery. In High-throughput Phenotyping, the required task is to classify individual plant cultivars to assist plant breeding, which has posed three challenges: 1) it is easy to overfit complex features and models, 2) visual conditions change during and between image collection opportunities, and 3) analysis of thousands of cultivars require high-throughput data collection and analysis. To tackle these challenges, we propose a simple but intuitive descriptor, Radial Object Descriptor, to represent plant cultivar objects based on contour. This descriptor is invariant under scaling, rotation, and translation, as well as robust under changes to the plant's growth stage and camera's view angle. Furthermore, we complement this mid-level feature by fusing it with the low-level features (Histogram of Oriented Gradients) and deep features (ResNet-18), respectively. We extensively test our fusion approaches using two real world experiments. One experiment is on a novel benchmark dataset (HTP-Soy) in which we collect similar to 2, 000 high-resolution aerial images of outdoor soybean plots. Another experiment is on three datasets of indoor rosette plants. For both experiments, our fusion approaches achieve superior accuracies while maintaining better generalization as compared with traditional approaches.

关键词： Model automobiles

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A method for detecting text of arbitrary shapes in natural scenes that improves text spotting

A method for detecting text of arbitrary shapes in natural s...

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

作者： Wang, Qitong Zheng, Yi Betke, Margrit Boston Univ Boston MA 02215 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Understanding the meaning of text in images of natural scenes like highway signs or store front emblems is particularly challenging if the text is foreshortened in the image or the letters are artistically distorted. We introduce a pipeline-based text spotting framework that can both detect and recognize text in various fonts, shapes, and orientations in natural scene images with complicated backgrounds. The main contribution of our work is the text detection component, which we call UHT, short for UNet, Heatmap, and Textfill. UHT uses a UNet to compute heatmaps for candidate text regions and a textfill algorithm to produce tight polygonal boundaries around each word in the candidate text. Our method trains the UNet with groundtruth heatmaps that we obtain from text bounding polygons provided by groundtruth annotations. Our text spotting framework, called UHTA, combines UHT with the state-of-the-art text recognition system ASTER. Experiments on four challenging and public scene-text-detection datasets (Total-Text, SCUT-CTW1500, MSRA-TD500, and COCO-Text) show the effectiveness and generalization ability of UHT in detecting not only multilingual (potentially rotated) straight but also curved text in scripts of multiple languages. Our experimental results of UHTA on the Total-Text dataset show that UHTA outperforms four state-of-the-art text spotting frameworks by at least 9.1 percent points in the F-measure, which suggests that UHTA may be used as a complete text detection and recognition system in real applications.

关键词： Heating systems Skeleton Feature extraction Shape Text recognition computer vision Training

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CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents

CascadeTabNet: An approach for end to end table detection an...

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

作者： Prasad, Devashish Gadpal, Ayan Kapadni, Kshitij Visave, Manish Sultanpure, Kavita Pune Inst Comp Technol Pune Maharashtra India

ISBN: (纸本)9781728193601

An automatic table recognition method for interpretation of tabular data in document images majorly involves solving two problems of table detection and table structure recognition. The prior work involved solving both problems independently using two separate approaches. More recent works signify the use of deep learning-based solutions while also attempting to design an end to end solution. In this paper, we present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model. We propose CascadeTabNet: a Cascade mask Region-based CNN High-Resolution Network (Cascade mask R-CNN HRNet) based model that detects the regions of tables and recognizes the structural body cells from the detected tables at the same time. We evaluate our results on ICDAR 2013, ICDAR 2019 and TableBank public datasets. We achieved 3rd rank in ICDAR 2019 post-competition results for table detection while attaining the best accuracy results for the ICDAR 2013 and TableBank dataset. We also attain the highest accuracy results on the ICDAR 2019 table structure recognition dataset. Additionally, we demonstrate effective transfer learning and image augmentation techniques that enable CNNs to achieve very accurate table detection results. Code and dataset has been made available at: https://***/DevashishPrasad/CascadeTabNet

关键词： Deep learning

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An Interface between Grassmann manifolds and vector spaces

An Interface between Grassmann manifolds and vector spaces

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

作者： Souza, Lincon S. Sogi, Naoya Gatto, Bernardo B. Kobayashi, Takumi Fukui, Kazuhiro Univ Tsukuba Grad Sch Sci & Technol Tsukuba Ibaraki Japan Univ Tsukuba Ctr Artificial Intelligence Res C AIR Tsukuba Ibaraki Japan Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

In this paper, we propose a method to map data from a Grassmann manifold to a vector space while maximizing discrimination capability for classification. Subspaces are a practical and robust representation for image set recognition. However, as they exist on a Grassmann manifold, machine learning tools constructed on Euclidean geometry cannot be promptly utilized. Recently, methods to construct end-to-end learnable models for subspaces are starting to be explored, but they require multiple matrix decompositions and can be hard to compute and extend. Therefore we introduce a layer to map Grassmann manifold-valued data to vector space, in such a way that it can be seamlessly used as a layer along with other powerful tools defined on Euclidean space. The key idea of our method is to formulate the manifold logarithmic map (log) as a learnable model, where we seek to learn a tangency point that minimizes a loss function with respect to the data. The log effectively transforms a manifold point into a tangent vector. This log model can be learned with Riemannian stochastic gradient descent on the target manifold. We demonstrate the effectiveness of our proposed method on the applications of hand shape recognition, face identification and facial emotion recognition.

关键词： Manifolds Image recognition Tools Computational modeling Kernel Machine learning Data models

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computer-aided diagnosis system of lung carcinoma using Convolutional Neural Networks

Computer-aided diagnosis system of lung carcinoma using Conv...

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

作者： Han, Fangjian Yu, Li Jiang, Yi Lensee Biotechnol Co Ltd Ningbo Peoples R China Cent South Univ Xiangya Hosp 2 Changsha Peoples R China

ISBN: (纸本)9781728193601

With the number of lung cancers' morbidity and mortality is showing a trend of increasing year by year, the demand for pathologists is increasing rapidly, so in this study, we aimed to design a medical pathologically assistant diagnostic system to help pathologists complete diagnostic analysis tasks. A Deep Convolutional Neural Network(DCNN) is adopted to automatically distinguish tumor tissues from normal tissues in digitized hematoxylin and eosin (H&E) stained lung cell pathological slides that collected from The Cancer Genome Atlas (TCGA) and collaborate hospitals, we trained and evaluate WSIs(the whole slide images) captured at 10x magnification and other higher magnification, results show the difference are negligible. Moreover, we also compared the training effect of different models on same level magnification WSIs, the results show that performance of Resnet-18 network model and Resnet-50 network model is nearly consistent. Actually processing time based on Resnet-18 model is shorter than Resnet-50 model, so we don't need deeper network for study. Our system was shown to enormous advantages in accuracy, sensitivity and efficiency, could reduce the burden on pathologists, enable them to spend more time on advanced decision-making tasks, would be widely applied to pathological diagnosis, clinical practice, scientific research and so on.

关键词： Decision making

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