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vision and Language Navigation using Multi-head Attention Mechanism

Vision and Language Navigation using Multi-head Attention Me...

引用

International conference on Big Data and Information Analytics (BigDIA)

作者： Sai Mao Junmin Wu Siqi Hong School of Computer Science and Technology University of Science and Technology of China Hefei China School of Software Engineering University of Science and Technology of China Suzhou China

ISBN: (数字)9781665422321

ISBN: (纸本)9781665446662

In the wake of the developments of deep learning, more and more research focus on the intersection fields of natural language processing and machine vision, vision and language navigation (VLN) is one of them. the VLN task needs an embodied agent to carry out the natural language instruction and navigate inside a real 3D environment with the help of visual information, planning a trajectory from start point to goal location. In this paper, inspired by the previous works, we introduce a multi-head attention module with a parallel attention computing method which apply multi-head attention mechanism on visual and textual input to enhance the performance of the model. Specifically, first we design a multihead attention module with trainable parameters, which can extract associated attention from the textual and visual information, extracted attention variables are used to promote the agent aware of which part of the sentence or image is more important. Second, in order to help the agent perceives more useful information, we perform a parallel computing method on extracted attention and input, i.e. visual and textual input feature, then use a layer normalization to combine them. the results of experiments indicate our proposed module enables the model to obtain better performance and surpasses the baseline model. the success rate of our model is 51% and oracle success rate is 62% with low navigation error.

关键词： Visualization Navigation Computational modeling Feature extraction Data models Data mining Task analysis

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BatchOut: Batch-level feature augmentation to improve robustness to adversarial examples 11

BatchOut: Batch-level feature augmentation to improve robust...

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Subramanya, Akshayvarun Mopuri, Konda Reddy Babu, R. Venkatesh Indian Inst Sci Video Analyt Lab Bangalore Karnataka India

ISBN: (纸本)9781450366151

Machine Learning models are known to be susceptible to small but structured changes to their inputs that can result in wrong inferences. It has been shown that such samples, called adversarial samples, can be created rather easily for standard neural network architectures. these adversarial samples pose a serious threat for deploying state-of-the-art deep neural network models in the real world. We propose a feature augmentation technique called BatchOut to learn robust models towards such examples. the proposed approach is a generic feature augmentation technique that is not specific to any adversary and handles multiple attacks. We evaluate our algorithm on benchmark datasets and architectures to show that models trained using our method are less susceptible to adversaries created using multiple methods.

关键词： Machine Learning Convolutional Neural Networks Adversarial examples Feature Augmentation

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Cluster Loss for Person Re-Identification 11

Cluster Loss for Person Re-Identification

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Alex, Doney Sami, Zishan Banerjee, Sumandeep Panda, Subrat Capillary Technol Bengaluru India Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9781450366151

Person re-identification (ReID) is an important problem in computer vision, especially for video surveillance applications. the problem focuses on identifying people across different cameras or across different frames of same camera. the main challenge lies in identifying similarity of the same person against large appearance and structure variations, while differentiating between individuals. Recently, deep learning networks with triplet loss has become a common framework for person ReID. However, triplet loss focuses on obtaining correct orders on the training set. We demonstrate that it performs inferior in a clustering task. In this paper, we design a cluster loss, which can lead to the model output with a larger interclass variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalisation ability and can achieve a higher accuracy on the test set especially for a clustering task. We also introduce a batch hard training mechanism for improving the results and faster convergence of training.

关键词： Cameras

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VidSeg-GAN: Generative Adversarial Network for Video Object Segmentation Tasks 11

VidSeg-GAN: Generative Adversarial Network for Video Object ...

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Adak, Saptakatha Das, Sukhendu Indian Inst Technol Madras Chennai Tamil Nadu India

ISBN: (纸本)9781450366151

Video object segmentation aims to segment objects in a video sequence, given some user annotation which indicates the object of interest. Although Convolutional Neural Networks (CNNs) have been used in the recent past for the purpose of foreground segmentation in videos, adversarial training methods have not been used effectively to solve this problem, in spite of its extensive use for solving many other problems in computer vision. Earlier, flow features and motion trajectories have been extensively used to capture the temporal consistency between subsequent frames to segment moving objects in videos. However, we show that our proposed framework of processing the video frames independently using a deep generative adversarial network (GAN), is able to maintain the temporal coherency across frames without the use of any explicit trajectory based information, to provide superior results. Our main contribution lies in introducing a GAN based framework along with the incorporation of an Intersection-over-Union score based novel cost function for training the model, to solve the problem of foreground object segmentation in videos. the proposed method, when evaluated on popular real-world video segmentation datasets viz. DAVIS, SegTrack-v2 and YouTube-Objects, exhibits substantial performance gain over the recent state-of-the-art methods.

关键词： Generative adversarial networks

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Dynamic Gesture Recognition with Pose-based CNN Features derived from videos using LSTM 11

Dynamic Gesture Recognition with Pose-based CNN Features der...

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Roy, Kankana Sahay, Rajiv R. Indian Inst Technol Kharagpur Dept Comp Sci & Engn Computat Vis Lab Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Elect Engn Computat Vis Lab Kharagpur W Bengal India

ISBN: (纸本)9781450366151

In this work, we address the problem of dynamic gesture recognition using a pose based video descriptor. the proposed approach takes as input video frames and extracts pose-specific image regions which are further processed by a pre-trained Convolutional Neural Network (CNN) to derive a pose-based descriptor for each frame. A Long Short Term Memory (LSTM) network is trained from scratch for dynamic gesture classification by learning long-term spatiotemporal relations among features. We also demonstrate that only using video data (RGB frames and optical flow) one can design an effective model for recognizing dynamic gestures. We utilize ChaLearn multi-modal gesture challenge dataset [13] and Cambridge hand gesture dataset [18] for evaluation of the proposed algorithm achieving an accuracy of 91.27% and 96% respectively using only RGB data.

关键词： Long short term memory pose-based CNN feature video descriptor dynamic gesture classification

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Temporal Modeling of EEG Signals using Block Sparse Variational Bayes Framework 11

Temporal Modeling of EEG Signals using Block Sparse Variatio...

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Sharma, Shruti Chaudhury, Santanu Jayadeva, Jayadeva Indian Inst Technol Delhi New Delhi India

ISBN: (纸本)9781450366151

Compressed Sensing (CS) has emerged as an alternate method to acquire high dimensional signals effectively by exploiting the sparsity assumption. However, owing to non-sparse and non-stationary nature, it is extremely difficult to process Electroencephalograph (EEG) signals using CS paradigm. the success of Bayesian algorithms in recovering non-sparse signals has triggered the research in CS based models for neurophysiological signal processing. In this paper, we address the problem of Temporal Modeling of EEG Signals using Block Sparse Variational Bayes (SVB) Framework. Temporal correlation of EEG signals is modeled blockwise using normal variance scale mixtures parameterized via some random and deterministic parameters. Variational inference is exploited to infer the random parameters and Expectation Maximization (EM) is used to obtain the estimate of deterministic parameters. To validate the framework, we present experimental results for benchmark State Visual Evoked Potential (SSVEP) dataset with 40-target Brain-computer Interface (BCI) speller using two frequency recognition algorithms viz. Canonical Correlation Analysis (CCA) and L1-regularized Multiway CCA. Results show that the proposed temporal model is highly useful in processing SSVEP-EEG signals irrespective of the recognition algorithms used.

关键词： Brain computer interface

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Dynamic Hand Gesture Recognition using Convolutional Neural Network with RGB-D Fusion 18

Dynamic Hand Gesture Recognition using Convolutional Neural ...

引用

Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Bindu Verma Ayesha Choudhary School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi Delhi

ISBN: (纸本)9781450366151

In this paper, we propose a novel, real-time dynamic hand gesture recognition framework using convolutional neural network with depth and RGB data fusion. Hand gestures are a natural form of communication between humans as well as between human and machine. they also find important applications in areas such as sign language recognition, man-machine interaction and behavior understanding. Natural hand gestures are complex hand movements in space and time and are challenging to recognize. In our proposed framework, we use both RGB and depth data to automatically recognize dynamic hand gestures. Initially, we work with RGB and depth data separately. We find the motion history of the gesture performed with RGB data and independently with depth data to store the motion information of the moving hands. Motion history of the performed gesture stores the rich information of the movement. then, we use transfer learning on two separate VGG16 networks, where one network is fine-tuned using RGB motion history while the other network is fine-tuned using depth motion history, to configure them for dynamic hand gesture recognition problem. then, using the two fine-tunned VGG16 networks, we extract the features of both the motion history images obtained from RGB and depth data separately, for each dynamic hand gesture. We then, integrate the features obtained from both the networks using weighted summation, to accurately and robustly recognize the dynamic hand gesture. We perform experiments on standard and the publicly available dynamic hand gesture datasets and show that our method outperforms state of the art methods.

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Estimation of Ball Possession Statistics in Soccer Video 11

Estimation of Ball Possession Statistics in Soccer Video

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Sarkar, Saikat Chakrabarti, Amlan Mukherjee, Dipti Prasad Univ Calcutta Bangabasi Coll Kolkata India Univ Calcutta AK Choudhury Sch Informat Technol Kolkata India Indian Stat Inst Kolkata India

ISBN: (纸本)9781450366151

In this work, we have estimated ball possession statistics from the video of a soccer match. the ball possession statistics is calculated based on the valid pass counts of two playing teams. We propose a player-ball interaction energy function to detect ball pass event. Based on position and velocity of the ball and players, a model for interaction energy is defined. the energy increases when the ball is closer and about to collide with a player. Lower energy denotes that the ball is freely moving and not near to any player. the interaction energy generates a binary state sequence which determines a valid pass or a miss-pass. We assess the performance of our model on publicly available soccer videos and have achieved close to 83% accuracy.

关键词： Ball possession statistics Pass detection Player-ball interaction energy

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Perfectly Secure Shamir's Secret Sharing Scheme for Privacy Preserving image processing Over Cloud 11

Perfectly Secure Shamir's Secret Sharing Scheme for Privacy ...

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Sadhya, Debanjan Agarwal, Akshay Raman, Balasubramanian Indian Inst Technol Roorkee Roorkee Uttar Pradesh India Manipal Inst Technol Manipal India

ISBN: (纸本)9781450366151

Cloud computing is a highly prospective paradigm in which computational resources from third parties are used for processing outsourced data. Nonetheless, the distributed architecture of this concept poses many security and privacy threats for the data owners. Shamir's secret sharing is an effective technique for distributing and processing secret images over the encrypted domain. However, it has got some critical limitations primarily due to the presence of correlated information between the image pixels. Our study addresses this problem by proposing a perfectly secure Shamir's secret sharing scheme for images. Our work builds upon the formal notion of perfect secrecy for encoding the Shamir's shares in a particular manner such that they (i.e. encoded shares) do not reveal any additional information about the original image. Importantly, we have provided both theoretical and empirical validation of our proposed approach. We have also performed several image filtering operations on the stored shares and found the resulting PSNR and NCC values to be similar in the plain and encrypted domains. Hence our work provides a privacy-preserving and secure framework for working with images over a cloud-based architecture.

关键词： Cloud Computing Shamir's Secret Sharing Perfect Secrecy Encrypted Domain

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Automatic Microstructural Classification with Convolutional Neural Network 6th

Automatic Microstructural Classification with Convolutional ...

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6th conference on Information and Communication Technologies of Ecuador (TIC-EC)

作者： Lorena, Guachi Robinson, Guachi Stefania, Perri Pasquale, Corsonello Fabiano, Bini Franco, Marinozzi Yachay Tech Univ Hacienda San Jose S-N Urcuqui 100119 Ecuador Sapienza Univ Rome Via Eudossiana 18 I-00184 Rome RM Italy Univ Calabria Via P Bucci I-87036 Arcavacata Di Rende Italy

ISBN: (纸本)9783030028282;9783030028275

Microstructural characterization allows knowing the components of a microstructure in order to determine the influence on mechanical properties, such as the maximum load that a body can support before breaking out. In almost all real solutions, microstructures are characterized by human experts, and its automatic identification is still a challenge. In fact, a microstructure typically is a combination of different constituents, also called phases, which produce complex substructures that store information related to origin and formation mode of a material defining all its physical and chemical properties. Convolutional neural networks (CNNs) are a category of deep artificial neural networks that show great success in computer vision applications, such as image and video recognition. In this work we explore and compare four outstanding CNNs architectures with increasing depth to analyze their capability of classifying correctly microstructural images into seven classes. Experiments are done referring to ultrahigh carbon steel microstructural images. As the main result, this paper provides a point-of-view to choose CNN architectures for microstructural image identification considering accuracy, training time, and the number of multiply and accumulate operations performed by convolutional layers. the comparison demonstrates that the addition of two convolutional layers in the LeNet network leads to a higher accuracy without considerably lengthening the training.

关键词： Microstructure characterization image processing CNN

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