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Multi-modal Dense Video Captioning

Multi-modal Dense Video Captioning

引用

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

引用

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...

引用

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...

引用

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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Font-ProtoNet: Prototypical Network based Font Identification of Document Images in Low Data Regime

Font-ProtoNet: Prototypical Network based Font Identificatio...

引用

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

作者： Goel, Nikita Sharma, Monika Vig, Lovekesh TCS Res New Delhi India

ISBN: (纸本)9781728193601

While optical character recognition has attracted considerable interest from researchers in recent times, automating font identification in printed / scanned documents is still not a well explored problem. With the increasing variety of fonts in the open community, identifying the different fonts used in a given document image can often provide important visual cues for document understanding. Font identification is a challenging task owing to smaller inter-class variations and limited availability of labeled image data for a large variety of font images. In the absence of the original true type format (ttf) files, even synthetic data generation is not possible. To this end, we propose to utilize recent few-shot learning techniques like prototypical networks for font identification in scanned / printed document images using character images from different fonts as input for scarce data scenarios and call the proposed method Font-ProtoNet. This approach uses an initial set of classes to learn an embedding and centroid representations (as class prototypes), that are used to classify novel samples based on euclidean distance. We demonstrate that Font-ProtoNet gives encouraging results by training prototypical networks in few-shot learning settings on a synthetic dataset of 200 font classes and using the trained network to identify fonts on a synthetic dataset of 100 novel font classes. We have also tested our approach on the real-world Adobe Visual Font recognition (AdobeVFR) dataset and obtained 59.86% and 71.01% word-level accuracy of font identification using 1-shot and 5-shot i.e.,1 and 5 character images per font class, respectively.

关键词： Optical character recognition

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Metric Learning with A-based Scalar Product for Image-set recognition

Metric Learning with A-based Scalar Product for Image-set Re...

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

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

ISBN: (纸本)9781728193601

In this paper, we propose a metric learning method for image set recognition using subspace representation. The subspace representation is effective for image set recognition where each image set is compactly represented by a subspace in a high dimensional vector space. In this framework, the similarity between two given image sets is measured by the canonical angles between the two corresponding subspaces. Many types of methods utilizing the concept of canonical angles have been developed and studied extensively. However, there still remains large potential in improving the ability to measure canonical angles. Our key idea is to learn a general scalar product space (metric space) that produces more valid canonical angles between two subspaces. To realize this idea, we first introduce an A-based scalar product instead of the standard scalar product, where A is a symmetric positive definite matrix and the canonical angles between two subspaces are measured through the A-based scalar product. We learn a discriminative metric space by optimizing metric A in terms of the Fisher ratio from local Fisher discriminant analysis. Besides, we introduce a mechanism to automatically reduce the dimension of the metric space by imposing a low-rank constraint on metric A. The effectiveness of the proposed methods is validated through extensive classification experiments on three real-world datasets.

关键词： Vector spaces

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Glaucoma Precognition: Recognizing Preclinical Visual Functional Signs of Glaucoma

Glaucoma Precognition: Recognizing Preclinical Visual Functi...

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

作者： Gupta, Krati Thakur, Anshul Goldbaum, Michael Yousefi, Siamak Indian Inst Technol Mandi Sch Comp & Elect Engn Mandi Himachal Prades India Univ Calif San Diego Dept Ophthalmol San Diego CA 92103 USA Univ Tennessee Hlth Sci Ctr Dept Ophthalmol Memphis TN 38103 USA Univ Tennessee Hlth Sci Ctr Dept Genet Genom & Informat Memphis TN USA

ISBN: (纸本)9781728193601

Deep archetypal analysis (DAA) has recently been proposed as an unsupervised approach for discovering latent structures in data. However, while a few approaches have used classical archetypal analysis (AA), DAA has not been incorporated in medical image analysis as yet. The purpose of this study is to develop a precognition framework to identify preclinical signs of glaucomatous vision loss using convex representations derived from DAA. We first develop an AA structure and a novel DAA framework to recognize hidden patterns of visual functional loss, and then project visual field data over the identified patterns to obtain a representation for glaucoma precognition several years prior to disease onset. We then develop a glaucoma classification framework using class-balanced bagging with neural networks to address the class imbalance problem. In contrast to other classification approaches, DAA, applied to a unique prospective longitudinal dataset with approximately eight years of visual field tests from normal eyes that developed glaucoma, has allowed visualization of the early signs of glaucoma and development of a construct for glaucoma precognition. Our findings suggest that our proposed glaucoma precognition approach could significantly advance state-of-the-art glaucoma prediction.

关键词： vision

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LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking

LightTrack: A Generic Framework for Online Top-Down Human Po...

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

作者： Ning, Guanghan Pei, Jian Huang, Heng JD Finance Amer Corp Mountain View CA 94043 USA Simon Fraser Univ Burnaby BC Canada Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (纸本)9781728193601

In this paper, we propose a simple yet effective framework, named LightTrack, for online human pose tracking. Existing methods usually perform human detection, pose estimation and tracking in sequential stages, where pose tracking is regarded as an offline bipartite matching problem. Our proposed framework is designed to be generic, efficient and truly online for top-down approaches. For efficiency, Single-Person Pose Tracking (SPT) and Visual Object Tracking (VOT) are incorporated as a unified online functioning entity, easily implemented by a replaceable single-person pose estimator. To mitigate offline optimization costs, the framework also unifies SPT with online identity association and sheds first light upon bridging multiperson keypoint tracking with Multi-Target Object Tracking (MOT). Specifically, we propose a Siamese Graph Convolution Network (SGCN) for human pose matching as a Re-ID module. In contrary to other Re-ID modules, we use a graphical representation of human joints for matching. The skeleton-based representation effectively captures human pose similarity and is computationally inexpensive. It is robust to sudden camera shifts that introduce human drifting. The proposed framework is general enough to fit other pose estimators and candidate matching mechanisms. Extensive experiments show that our method outperforms other online methods and is very competitive with offline state-of-the-art methods while maintaining higher frame rates. Code and models are publicly available at https://***/Guanghan/lighttrack.

关键词： computer vision

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