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G-PReDICT: Generalizable Person Re-ID using Domain Invariant Contrastive Techniquesg± 13

G-PReDICT: Generalizable Person Re-ID using Domain Invariant...

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13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Jambigi, Chaitra Masud, Umar Chakraborty, Anirban Dept. of Computational and Data Sciences Indian Institute of Science Karnataka Bengaluru India Dept. of Electronics and Communications Jamia Millia Islamia New Delhi India

ISBN: (纸本)9781450398237

Learning identity-Aware, domain-invariant representations is crucial in solving domain generalizable person ReID (DG-ReID). Existing methods commonly use augmentation techniques either in feature space by mixing instance and batch normalization layers or in pixel space by adversarially generating pseudo domains. However, neither of these techniques guarantee identity preservation. Apart from increasing training data diversity, the augmented positive pairs also encode rich semantic relations which have not been fully explored. To address the above issues, we propose a novel framework for Generalizable Person Re-identification using Domain Invariant Contrastive Techniques (G-PReDICT). Specifically, we use simple yet effective perturbation strategies to hallucinate positive samples across domains by realistically modelling domain variations while preserving the target identities. We harness rich sample-sample relations between the hallucinated positive-negative pairs to learn domain-invariant representations using supervised contrastive learning. We also use a domain independent auxiliary task, i.e. attribute prediction to learn robust representations and introduce attribute annotations for two large scale public benchmarks i.e. CUHK-03 and MSMT17. Extensive experiments on standard benchmarks demonstrate the effectiveness of the proposed method. © 2022 ACM.

关键词： computer vision

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HazeNet: Does the image have haze? 21

HazeNet: Does the image have haze?

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Kalaivani, P. Rooomi, S. Mohamed Mansoor Nathan, D. Sabari Government College of Engineering Bodinayakanur Theni Tamilnadu India Thiagarajar College of Engineering Tamilnadu Madurai India Couger Inc Tokyo Japan

ISBN: (纸本)9781450391276

the visibility of scene is much important in applications of Intelligent Video surveillance systems, Autonomous Vehicle Navigation. the presence of haze limits the visibility, thereby hindering the proper operation of computer vision algorithms. there are lot of algorithms to remove haze unconditionally. But the haze removal on clear images or frames will cause adverse effects. Unnecessary dehazing results in the loss of original information and also delays the following process. To address these challenges, there is a need to recognize the image as hazy and haze-free before applying dehazing on it. Hence, this paper presents three light-weight deep neural network models to automatically classify image as hazy and haze-free in the name of HazeNet, R-HazeNet and RSE-HazeNet. the HazeNet is Convolutional Neural Network having five convolutional blocks and results in classification accuracy of 97.89%. In order to improve the performance of HazeNet further, the Residual blocks have been included after each convolutional layer. the model is called R-HazeNet. the residual blocks help in producing hierarchical features. To strengthen the useful convolutional channel features alone, Squeeze and Excitation blocks have been included with Residual units in RSE-HazeNet model. Both R-HazeNet and RSE-HazeNet produced classification accuracy of 99.98% which demonstrate superior performance compared to the stat-of-the-art methods. the proposed models are light-weight models due to the percentage improvement in number of parameters as a minimum of 93% has been achieved with respect to the the baseline method. © 2021 ACM.

关键词： Convolution

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Design of a System and Method for Optimal selection of Tumor Slice using Linear Ultrasound Imaging for Histopathology 13

Design of a System and Method for Optimal selection of Tumor...

引用

13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Kumar, Abhishek Sheet, Debdoot Indian Institute of Technology Kharagpur India

ISBN: (纸本)9781450398237

In excision biopsy, a tumor mass is surgically removed from the body. Subsequently, it is sliced at an appropriate location and investigated microscopically through a process called histopathology. Any bias in tumor slicing severely influences histopathology outcomes, such as if malignant foci do not appear in the sliced location, then, the tumor would be accidentally reported non-malignant. the standard approach adopted to solve this challenge by a histopathologist is to overcome this bias by slicing at multiple locations for their investigation. Till now, this process has been manual, time-consuming, and error-prone. We aim to design a system and develop a data-driven deep learning approach to assist histopathologists by providing them with a representative slice location for reporting to increase their efficiency and accuracy. We have developed a low cost linear gantry scanner that can acquire images and integrated with a deep learning model to predict the optimal slice representative of pathology in a tumor mass. We achieve an F1 score of 0.97 and an accuracy of 97.5% in predicting an optimal slice using this approach. © 2022 ACM.

关键词： Tumors

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Knowledge sharing and cooperation based adaptive boosting for robust eye detection 14

Knowledge sharing and cooperation based adaptive boosting fo...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Lahiri, Avisek Biswas, Prabir Indian Institute of Technology Kharagpur West721302 India

ISBN: (纸本)1595930361

this paper proposes knowledge sharing and cooperation based Adaptive Boosting (KSC-AdaBoost) for supervised collaborative learning in presence of two different feature spaces (views) representing a training example. In such a binary learner space, two learner agents are trained on the two feature spaces. Difficulty of a training example is ascertained not only by classification performance of an individual learner but also by overall group performance on that training example. Group learning is enhanced by a novel algorithm for assigning weight to training set data. three different models of KSC-AdaBoost are proposed for agglomerating decisions of the two learners. KSC-AdaBoost out-performs traditional AdaBoost and some recent variants of AdaBoost in terms of convergence rate of training set error and generalization accuracy. the paper then presents KSC-AdaBoost based hierarchical model for accurate eye region localization followed by fuzzy rule based system for robust eye center detection. Exhaustive experiments on five publicly available popular datasets reveal the viability of the learning models and superior eye detection accuracy over recent state-of-the-art algorithms. Copyright 2014 ACM.

关键词： Fuzzy rules

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An encoder-decoder based deep architecture for visible to near infrared image transformation 21

An encoder-decoder based deep architecture for visible to ne...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Aswatha, Shashaank M. Malladi, Sai Phani Kumar Mukherjee, Jayanta Philips Benguluru India IIT Kharagpur India

ISBN: (纸本)9781450391276

Near-infrared (NIR) band is also one of the exciting wavelength ranges, besides optical range, to the human visual perception system. the NIR band shares various attributes with visible spectrum range. NIR images have the potential for better representation of vegetation and certain details of a scene that are sometimes opaque to RGB wavelengths. In this paper, we propose a dual encoder-decoder based architecture with different depths for transforming RGB images to NIR images. this network handles scale variations in data and helps to reduce artifacts in the transformed image domain. Two different resolutions of the input image are subjected to feature extraction by a series of five consecutive convolution and ReLu layers. then, their corresponding aggregated features are assembled and up-sampled to regress over the ground truth NIR image. We consider two datasets with different resolutions and properties, EPFL and OMSIV, which provide corresponding RGB-NIR pairs of images of natural scenes. We train and test our network on both the datasets and evaluate its qualitative and quantitative performance. the results of our network in transforming RGB images to NIR images are found to be interesting which are demonstrated through various examples. © 2021 ACM.

关键词： Infrared devices

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Domain adaptation on correlated subspace for unseen multiview headpose classification 14

Domain adaptation on correlated subspace for unseen multivie...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Rajagopal, Anoop K. Ramakrishnan, K.R. Dept. of Electrical Engineering Indian Institute of Science Bengaluru India

ISBN: (纸本)1595930361

Head pose classification from images acquired from far field cameras is a challenging problem because of the low resolution, blur and noise due to subject movements. Further there exists a domain shift in head pose classification between training (source) and testing (target) images. Also more often the head poses in the target set may not exist in the source set and acquiring sufficient samples for training is quite expensive. In this paper, we propose a novel framework to address multi-view unseen head pose classification where the target set belongs to a different domain and has more classes than in the source. A correlated subspace is first derived using Canonical Correlation Analysis (CCA) between corresponding head poses in the source (stationary subjects) and target set (moving subjects). A distance based Domain Adaptation technique is then used in the correlation subspace for classification of unseen head pose in the target set. Experimental results confirm the effectiveness of our approach in improving the classification performance over the state-of-art. Copyright is held by the authors.

关键词： CCA

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Efficient-PSP-Net: A Lightweight Model for the Segmentation of Liver and Tumor in CT Scans 13

Efficient-PSP-Net: A Lightweight Model for the Segmentation ...

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13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Sindhura, Chitimireddy Vengama, Harsha Vardhan Reddy Gorthi, Subrahmanyam Indian Institute of Technology Tirupati Andhra Pradesh India

ISBN: (纸本)9781450398237

the segmentation of the liver and tumors present in the liver is an essential task for the diagnosis of liver cancer and surgical treatment planning. While most of the works were developed with the goal of improving segmentation accuracy, there is a high demand for the development of lightweight architectures to deploy in clinical settings with limited computational resources. Developing such resource-efficient models while maintaining the performance of the state-of-the-Art architectures is challenging due to the high class imbalance and small observable changes between tumorous and non-Tumorous regions, which would generally require extensive and complex deep learning architectures. the contributions of this paper are twofold;First, this paper proposes to encode information extracted at different scales using a "Pyramid Pooling Module"(PPM) architecture. While different variants of pyramidal architectures are widely used in the context of scene parsing in computer vision, this paper explores the use of the PPM architecture for the segmentation of liver and tumor. Second, the paper proposes using the popular lightweight "Efficient-Net"as the backbone network of PSP-Net for feature extraction, thereby reducing the overall model size while achieving relatively similar performance to state-of-the-Art architectures. the performance of the proposed model is evaluated on the MICCAI 2017 LiTS dataset. the model achieved a DSC of 95.06% and 79.08% for liver and tumor, respectively, with only 1.76M parameters. © 2022 ACM.

关键词： Tumors

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Knowledge Distillation with Ensemble Calibration 14

Knowledge Distillation with Ensemble Calibration

引用

14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Mishra, Ishan Jain, Riyanshu Viradiya, Dhruv Patel, Divyam Mishra, Deepak Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798400716256

Knowledge Distillation is a transfer learning and compression technique that aims to transfer hidden knowledge from a teacher model to a student model. However, this transfer often leads to poor calibration in the student model. this can be problematic for high-risk applications that require well-calibrated models to capture prediction uncertainty. To address this issue, we propose a simple and novel technique that enhances the calibration of the student network by using an ensemble of well-calibrated teacher models. We train multiple teacher models using various data-augmentation techniques such as cutout, mixup, CutMix, and AugMix and use their ensemble for knowledge distillation. We evaluate our approach on different teacher-student combinations using CIFAR-10 and CIFAR-100 datasets. Our results demonstrate that our technique improves calibration metrics (such as expected calibration and overconfidence errors) while also increasing the accuracy of the student network. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Distillation

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Seventh International conference on computer graphics and Virtuality, ICCGV 2024

Seventh International Conference on Computer Graphics and Vi...

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7th International conference on computer graphics and Virtuality, ICCGV 2024

ISBN: (纸本)9781510679771

the proceedings contain 15 papers. the topics discussed include: self-supervised 3D face reconstruction based on dense key points;slice-based ray casting volume shadow of volumetric datasets;a GPU computation-based ray tracing engine with user-friendly and scalable rendering features and structures;a lightweight stereo depth estimation network based on mobile devices;a lightweight stereo depth estimation network based on mobile devices;grand challenge of image processing in automatic detection of vehicles running in red lights;anomaly detection algorithm for asymmetric autoencoder based on knowledge distillation;a stereo vision-based real-time 3D hand pose estimation system combining nonlinear optimization;and a lightweight real-time 3D hand gesture tracking solution for mobile devices.

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InMDb: indian Movie Database for Emotion Analysis 14

InMDb: Indian Movie Database for Emotion Analysis

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Garg, Ritik thareja, Rushil Bisht, Manak Singh, Manavjeet Arora, Sarthak Shukla, Jainendra IIIT Delhi Delhi India

ISBN: (纸本)9798400716256

Cinematic experiences, characterized by intricate audio-visual stimuli, foster profound emotional engagement. However, the correlation between audience emotions, physiological responses, film genres, and ratings, particularly in the underexplored Bollywood context, remains largely uncharted. Understanding this intricate interplay can provide filmmakers valuable insights for content adaptation. Addressing this research gap, we introduce "InMDB: indian Movie DataBase," a comprehensive multimodal dataset that examines emotional responses elicited by Bollywood trailers, using both self-reported measures and physiological data. Our meticulous statistical analysis of the dataset deepens the understanding of how emotions and their subsequent physiological responses correlate with, and potentially influence, film ratings and categories, offering novel insights into emotional engagement in the cinematic context. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Motion pictures

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