MediaEval 2017 - Satellite Task: Visual and textual analysis of social media and satellite images for flood detection @ multimedia satellite task MediaEval 2017
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Presenter: Konstantinos Avgerinakis, Centre for Research & Technology Hellas - Information Technologies Institute, Greece Paper: http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_31.pdf Video: https://youtu.be/IRUxoWsCP2c Authors: Konstantinos Avgerinakis, Anastasia Moumtzidou, Stelios Andreadis, Emmanouil Michail, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris Abstract: This paper presents the algorithms that CERTH team deployed in order to tackle disaster recognition tasks and more specifically Disaster Image Retrieval from Social Media (DIRSM) and Flood-Detection in Satellite images (FDSI). Visual and textual analysis, as well as late fusion of their similarity scores, were deployed in social media images, while color analysis in the RGB and near-infrared channel of satellite images was performed in order to discriminate flooded from non-flooded images. Deep Convolutional Neural Network (DCNN), DBpedia Spotlight and combMAX was implemented to tackle DIRSM, while Mahalanobis Distance-based classification and morphological post-processing were applied to deal with FDSI.
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This document discusses using a generative adversarial network (GAN) for image super resolution. It begins with an abstract that explains super resolution aims to increase image resolution by adding sub-pixel detail. Convolutional neural networks are well-suited for this task. Recent years have seen interest in reconstructing super resolution video sequences from low resolution images. The document then reviews literature on image super resolution techniques including deep learning methods. It describes the methodology which uses a CNN to compare input images to a trained dataset to predict if high-resolution images can be generated from low-resolution images.
Satellite Image Classification with Deep Learning Survey
Satellite imagery is important for many applications including disaster response, law enforcement and environmental monitoring etc. These applications require the manual identification of objects in the imagery. Because the geographic area to be covered is very large and the analysts available to conduct the searches are few, thus an automation is required. Yet traditional object detection and classification algorithms are too inaccurate and unreliable to solve the problem. Deep learning is a part of broader family of machine learning methods that have shown promise for the automation of such tasks. It has achieved success in image understanding by means that of convolutional neural networks. The problem of object and facility recognition in satellite imagery is considered. The system consists of an ensemble of convolutional neural networks and additional neural networks that integrate satellite metadata with image features. Roshni Rajendran | Liji Samuel ""Satellite Image Classification with Deep Learning: Survey"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30031.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/30031/satellite-image-classification-with-deep-learning-survey/roshni-rajendran
BDS_QA.pdf
The document discusses a movie streaming service called JetFlix that maintains a database of movies viewed by users in a binary format. It provides the code for map-reduce functions to determine the most popular movie among users by counting the number of views for each movie across all users. Sample outputs are also given for the map phase for two users and the data structure after the shuffle phase.
Presentation NBMP and PCC
presentation about 2 emerging standards activities that I started and led in MPeG, point cloud compression on a new image and video format, and NBMP for media delivery in 5G networks. Presented at Philips R&D in Eindhoven the Netherlands
Similar to MediaEval 2017 - Satellite Task: Visual and textual analysis of social media and satellite images for flood detection @ multimedia satellite task MediaEval 2017 (20)
Neural Architectures for Still Images - Xavier Giro- UPC Barcelona 2019
Neural Architectures for Still Images - Xavier Giro- UPC Barcelona 2019
Placing Images with Refined Language Models and Similarity Search with PCA-re...
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Enabling Application Integrated Proactive Fault Tolerance
Enabling Application Integrated Proactive Fault Tolerance
Combining textual and visual features for Ad-hoc Video Search
Combining textual and visual features for Ad-hoc Video Search
Visualization of Knowledge Distribution across Development Teams using 2.5D S...
Visualization of Knowledge Distribution across Development Teams using 2.5D S...
AUTO AI 2021 talk Real world data augmentations for autonomous driving : B Ra...
AUTO AI 2021 talk Real world data augmentations for autonomous driving : B Ra...
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
Image super resolution using Generative Adversarial Network.
Image super resolution using Generative Adversarial Network.
Satellite Image Classification with Deep Learning Survey
Satellite Image Classification with Deep Learning Survey
More from multimediaeval
Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal...
Paper: http://ceur-ws.org/Vol-2882/paper62.pdf
YouTube: https://youtu.be/gV-rvV3iFDA
Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri and Julien Morlier : Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal CNN for MediaEval 2020. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This work presents a method for classifying table tennis strokes using spatio-temporal convolutional neural networks. The fine-grained classification is performed on trimmed video segments recorded at 120 fps with different players performing in natural conditions. From those segments, the frames are extracted, their optical flow is computed and the pose of the player is estimated. From the optical flow amplitude, a region of interest is inferred. A three stream spatio-temporal convolutional neural network using combination of those modalities and 3D attention mechanisms is presented in order to perform classification.
Presented by: Pierre-Etienne Martin
HCMUS at MediaEval 2020: Ensembles of Temporal Deep Neural Networks for Table...
Paper: http://ceur-ws.org/Vol-2882/paper50.pdf
Hai Nguyen-Truong, San Cao, N. A. Khoa Nguyen, Bang-Dang Pham, Hieu Dao, Minh-Quan Le, Hoang-Phuc Nguyen-Dinh, Hai-Dang Nguyen and Minh-Triet Tran : HCMUS at MediaEval 2020: Ensembles of Temporal Deep Neural Networks for Table Tennis Strokes Classification Task. Proc. of MediaEval 2020, 14-15 December 2020, Online.
The Sports Video Classification Tasks in the Multimedia Evaluation 2020 Challenge focuses on classifying different types of table tennis strokes in video segments. In this task, we - the HCMUS Team - perform multiple experiments, which includes a combination of models such as SlowFast, Optical Flow, DensePose, R2+1, Channel-Separated Convolutional Networks, to classify 21 types of table tennis strokes from video segments. In total, we submit eight runs corresponding to five different models with different sets of hyper-parameters in each of our models. In addition, we apply some pre-processing techniques on the dataset in order for our model to learn and classify more accurately. According to the evaluation results, one of our team's methods out-performs the other team's. In particular, our best run achieves 31.35\% global accuracy, and all of our methods show potential results in terms of local and global accuracy for action recognition tasks.
Sports Video Classification: Classification of Strokes in Table Tennis for Me...
Paper: http://ceur-ws.org/Vol-2882/paper2.pdf
YouTube: https://youtu.be/-bRL868b8ys
Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri, Laurent Mascarilla, Jordan Calandre and Julien Morlier : Sports Video Classification: Classification of Strokes in Table Tennis for MediaEval 2020. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Fine-grained action classification has raised new challenges compared to classical action classification problems. Sport video analysis is a very popular research topic, due to the variety of application areas, ranging from multimedia intelligent devices with user-tailored digests, up to analysis of athletes' performances. Running since 2019 as a part of MediaEval, we offer a task which consists in classifying table tennis strokes from videos recorded in natural conditions at the University of Bordeaux. The aim is to build tools for teachers, coaches and players to analyse table tennis games. Such tools could lead to an automatic profiling of the player and adaptation of his training for improving his/her sport skills more efficiently.
Presented by: Pierre-Etienne Martin
Predicting Media Memorability from a Multimodal Late Fusion of Self-Attention...
Paper: http://ceur-ws.org/Vol-2882/paper61.pdf
YouTube: https://youtu.be/brmI4g3jLS4
Ricardo Kleinlein, Cristina Luna-Jiménez, Fernando Fernández-Martínez and Zoraida Callejas : Predicting Media Memorability from a Multimodal Late Fusion of Self-Attention and LSTM Models. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper reports on the GTH-UPM team experience in the Predicting Media Memorability task at MediaEval 2020. Teams were requested to predict memorability scores at both short-term and long-term, understanding such score as a measure of whether a video was perdurable in a viewer's memory or not. Our proposed system relies on a late fusion of the scores predicted by three sequential models, each trained over a different modality: video captions, aural embeddings and visual optical flow-based vectors. Whereas single-modality models show a low or zero Spearman correlation coefficient value, their combination considerably boosts performance over development data up to 0.2 in the short-term memorability prediction subtask and 0.19 in the long-term subtask. However, performance over test data drops to 0.016 and -0.041, respectively.
Presented by: Ricardo Kleinlein
Essex-NLIP at MediaEval Predicting Media Memorability 2020 Task
Paper: http://ceur-ws.org/Vol-2882/paper52.pdf
Janadhip Jacutprakart, Rukiye Savran Kiziltepe, John Q. Gan, Giorgos Papanastasiou and Alba G. Seco de Herrera : Essex-NLIP at MediaEval Predicting Media Memorability 2020 Task. Proc. of MediaEval 2020, 14-15 December 2020, Online.
In this paper, we present the methods of approach and the main results from the Essex NLIP Team’s participation in the MediEval 2020 Predicting Media Memorability task. The task requires participants to build systems that can predict short-term and long-term memorability scores on real-world video samples provided. The focus of our approach is on the use of colour-based visual features as well as the use of the video annotation meta-data. In addition, hyper-parameter tuning was explored. Besides the simplicity of the methodology, our approach achieves competitive results. We investigated the use of different visual features. We assessed the performance of memorability scores through various regression models where Random Forest regression is our final model, to predict the memorability of videos.
Overview of MediaEval 2020 Predicting Media Memorability task: What Makes a V...
Paper: http://ceur-ws.org/Vol-2882/paper6.pdf
YouTube: https://youtu.be/ySGGu_4vaxs
Alba García Seco De Herrera, Rukiye Savran Kiziltepe, Jon Chamberlain, Mihai Gabriel Constantin, Claire-Hélène Demarty, Faiyaz Doctor, Bogdan Ionescu and Alan F. Smeaton : Overview of MediaEval 2020 Predicting Media Memorability task: What Makes a Video Memorable? Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper describes the MediaEval 2020 Predicting Media Memorability task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 3rd edition this year, as the prediction of short-term and long-term video memorability (VM) remains a challenging task. In 2020, the format remained the same as in previous editions. This year the videos are a subset of the TRECVid 2019 Video to Text dataset, containing more action rich video content as compare with the 2019 task. In this paper a description of some aspects of this task is provided, including its main characteristics, a description of the collection, the ground truth dataset, evaluation metrics and the requirements for the run submission.
Presented by: Rukiye Savran Kiziltepe
Fooling an Automatic Image Quality Estimator
This document discusses fooling an automatic image quality estimator (BIQA) using adversarial attacks. It presents adapting the definition of an adversarial attack since BIQA predicts a quality score rather than class. It also covers challenges in generating adversarial samples as integer images and quantizing samples before JPEG compression to simulate artifacts. Results show success rates for different quality targets and selection rates by evaluators.
Fooling Blind Image Quality Assessment by Optimizing a Human-Understandable C...
Paper: http://ceur-ws.org/Vol-2882/paper16.pdf
YouTube: https://youtu.be/ix_b9K7j72w
Zhengyu Zhao : Fooling Blind Image Quality Assessment by Optimizing a Human-Understandable Color Filter. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper presents the submission of our RU-DS team to the Pixel Privacy Task 2020. We propose to fool the blind image quality assessment model by transforming images based on optimizing a human-understandable color filter. In contrast to the common work that relies on small, $L_p$-bounded additive pixel perturbations, our approach yields large yet smooth perturbations. Experimental results demonstrate that in the specific context of this task, our approach is able to achieve strong adversarial effects, but has to sacrifice the image appeal.
Presented by: Zhengyu Zhao
Pixel Privacy: Quality Camouflage for Social Images
Paper: http://ceur-ws.org/Vol-2882/paper77.pdf
YouTube: https://youtu.be/8Rr4KknGSac
Zhuoran Liu, Zhengyu Zhao, Martha Larson and Laurent Amsaleg : Pixel Privacy: Quality Camouflage for Social Images. Proc. of MediaEval 2020, 14-15 December 2020, Online.
High-quality social images shared online can be misappropriated for unauthorized goals, where the quality filtering step is commonly carried out by automatic Blind Image Quality Assessment (BIQA) algorithms. Pixel Privacy benchmarks privacy-protective approaches that protect privacy-sensitive images against unethical computer vision algorithms. In the 2020 task, participants are encouraged to develop camouflage methods that can effectively decrease the BIQA quality score of high-quality images and maintain image appeal. The camouflaged images need to be either imperceptible to the human eye, or it can be a visible enhancement.
Presented by: Zhuoran Liu
HCMUS at MediaEval 2020:Image-Text Fusion for Automatic News-Images Re-Matching
Paper: http://ceur-ws.org/Vol-2882/paper73.pdf
YouTube: https://youtu.be/TadJ6y7xZeA
Thuc Nguyen-Quang, Tuan-Duy Nguyen, Thang-Long Nguyen-Ho, Anh-Kiet Duong, Xuan-Nhat Hoang, Vinh-Thuyen Nguyen-Truong, Hai-Dang Nguyen and Minh-Triet Tran : HCMUS at MediaEval 2020:Image-Text Fusion for Automatic News-Images Re-Matching. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Matching text and images based on their semantics has an important role in cross-media retrieval. However, text and images in articles have a complex connection. In the context of MediaEval 2020 Challenge, we propose three multi-modal methods for mapping text and images of news articles to the shared space in order to perform efficient cross-retrieval. Our methods show systemic improvement and validate our hypotheses, while the best-performed method reaches a recall@100 score of 0.2064.
Presented by: Thuc Nguyen-Quang
Efficient Supervision Net: Polyp Segmentation using EfficientNet and Attentio...
Paper: http://ceur-ws.org/Vol-2882/paper72.pdf
Sabarinathan D and Suganya Ramamoorthy : Efficient Supervision Net: Polyp Segmentation using EfficientNet and Attention Unit. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Colorectal cancer is the third most common cause of cancer worldwide. In the era of medical Industry, identifying colorectal cancer in its early stages has been a challenging problem. Inspired by these issues, the main objective of this paper is to develop a Multi supervision net algorithm for segmenting polys on a comprehensive dataset. The risk of colorectal cancer could be reduced by early diagnosis of poly during a colonoscopy. The disease and their symptoms are highly varying and always a need for a continuous update of knowledge for the doctors and medical analyst. The diseases fall into different categories and a small variation of symptoms may lead to higher rate of risk. We have taken Medico polyp challenge dataset, which consists of 1000 segmented polyp images from gastrointestinal track. We proposed an efficient Net B4 as a pre-trained architecture in multi-supervision net. The model is trained with multiple output layers. We present quantitative results on colorectal dataset to evaluate the performance and achieved good results in all the performance metrics. The experimental results proved that the proposed model is robust and provides a good level of accuracy in segmenting polyps on a comprehensive dataset for different metrics such as Dice coefficient, Recall, Precision and F2.
HCMUS at Medico Automatic Polyp Segmentation Task 2020: PraNet and ResUnet++ ...
Paper: http://ceur-ws.org/Vol-2882/paper47.pdf
YouTube: https://youtu.be/vMsM4zg2-JY
Tien-Phat Nguyen, Tan-Cong Nguyen, Gia-Han Diep, Minh-Quan Le, Hoang-Phuc Nguyen-Dinh, Hai-Dang Nguyen and Minh-Triet Tran : HCMUS at Medico Automatic Polyp Segmentation Task 2020: PraNet and ResUnet++ for Polyps Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online.
The Medico task, MediaEval 2020, explores the challenge of building accurate and high-performance algorithms to detect all types of polyps in endoscopic images. We proposed different approaches leveraging the advantages of either ResUnet++ or PraNet model to efficiently segment polyps in colonoscopy images, with modifications on the network structure, parameters, and training strategies to tackle various observed characteristics of the given dataset. Our methods outperform the other teams' methods, for both accuracy and efficiency. After the evaluation, we are at top 2 for task 1 (with Jaccard index of 0.777, best Precision and Accuracy scores) and top 1 for task 2 (with 67.52 FPS and Jaccard index of 0.658).
Depth-wise Separable Atrous Convolution for Polyps Segmentation in Gastro-Int...
Paper: http://ceur-ws.org/Vol-2882/paper31.pdf
Syed Muhammad Faraz Ali, Muhammad Taha Khan, Syed Unaiz Haider, Talha Ahmed, Zeshan Khan and Muhammad Atif Tahir : Depth-wise Separable Atrous Convolution for Polyps Segmentation in Gastro-Intestinal Tract. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Identification of polyps in endoscopic images is critical for the diagnosis of colon cancer. Finding the exact shape and size of polyps requires the segmentation of endoscopic images. This research explores the advantage of using depth-wise separable convolution in the atrous convolution of the ResUNet++ architecture. Deep atrous spatial pyramid pooling was also implemented on the ResUNet++ architecture. The results show that architecture with separable convolution has a smaller size and fewer GFLOPs without degrading the performance too much.
Deep Conditional Adversarial learning for polyp Segmentation
Paper: http://ceur-ws.org/Vol-2882/paper22.pdf
Debapriya Banik and Debotosh Bhattacharjee : Deep Conditional Adversarial learning for polyp Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This approach has addressed the Medico automatic polyp segmentation challenge which is a part of Mediaeval 2020. We have proposed a deep conditional adversarial learning based network for the automatic polyp segmentation task. The network comprises of two interdependent models namely a generator and a discriminator. The generator network is a FCN employed for the prediction of the polyp mask while the discriminator enforces the segmentation to be as similar as the real segmented mask (ground truth). Our proposed model achieved a comparative result on the test dataset provided by the organizers of the challenge.
A Temporal-Spatial Attention Model for Medical Image Detection
Paper: http://ceur-ws.org/Vol-2882/paper21.pdf
Hwang Maxwell, Wu Cai, Hwang Kao-Shing, Xu Yong Si and Wu Chien-Hsing : A Temporal-Spatial Attention Model for Medical Image Detection. Proc. of MediaEval 2020, 14-15 December 2020, Online.
A local region model with attentive temporal-spatial pathways is proposed for automatically learning various target structures. The attentive spatial pathway highlights the salient region to generate bounding boxes and ignores irrelevant regions in an input image. The proposed attention mechanism allows efficient object localization and the overall predictive performance is increased because there are fewer false positives for the object detection task for medical images with manual annotations. The experimental results show that proposed models consistently increase the base architectures' predictive performance for different datasets and training sizes without undue computational efficiency.
HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Netw...
Paper: http://ceur-ws.org/Vol-2882/paper20.pdf
YouTube: https://youtu.be/CVelQl5Luf0
Quoc-Huy Trinh, Minh-Van Nguyen, Thiet-Gia Huynh and Minh-Triet Tran : HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Network and UNet for Polyps Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online.
The Medico: Multimedia Task focuses on developing an efficient and accurate framework to computer-aided diagnosis systems for automatic polyp segmentation to detect all types of polyps in endoscopic images of the gastrointestinal (GI) tract. We are HCMUS-team approach a solution, which includes combination Residual module, Inception module, Adaptive Convolutional neural network with Unet model and PraNet to semantic segmentation all types of polyps in endoscopic images. We submit multiple runs with different architecture and parameters in our model. Our methods show potential results in accuracy and efficiency through multiple experiments.
Fine-tuning for Polyp Segmentation with Attention
Paper: http://ceur-ws.org/Vol-2882/paper15.pdf
Rabindra Khadka : Transfer of Knowledge: Fine-tuning for Polyp Segmentation with Attention. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper describes how the transfer of prior knowledge can effectively take on segmentation tasks with the help of attention mechanisms. The UNet model pretrained on brain MRI dataset was fine-tuned with the polyp dataset. Attention mechanism was integrated to focus on relevant regions in the input images. The implemented architecture is evaluated on 200 validation images based on intersection over union and dice score between groundtruth and predicted region. The model demonstrates a promising result with computational efciency.
Bigger Networks are not Always Better: Deep Convolutional Neural Networks for...
Paper: http://ceur-ws.org/Vol-2882/paper12.pdf
Adrian Krenzer and Frank Puppe : Bigger Networks are not Always Better: Deep Convolutional Neural Networks for Automated Polyp Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper presents our team's (AI-JMU) approach to the Medico automated polyp segmentation challenge. We consider deep convolutional neural networks to be well suited for this task. To determine the best architecture we test and compare state of the art backbones and two different heads. Finally we achieve a Jaccard index of 73.74\% on the challenge test set. We further demonstrate that bigger networks do not always perform better. However the growing network size always increases the computational complexity.
Insights for wellbeing: Predicting Personal Air Quality Index using Regressio...
Paper: http://ceur-ws.org/Vol-2882/paper51.pdf
Amel Ksibi, Amina Salhi, Ala Alluhaidan and Sahar A. El-Rahman : Insights for wellbeing: Predicting Personal Air Quality Index using Regression Approach. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Providing air pollution information to individuals enables them to understand the air quality of their living environments. Thus, the association between people’s wellbeing and the properties of the surrounding environment is an essential area of investigation. This paper proposes Air Quality Prediction through harvesting public/open data and leveraging them to get the Personal Air Quality index. These are usually incomplete. To cope with the problem of missing data, we applied the KNN imputation method. To predict Personal Air Quality Index, we apply a voting regression approach based on three base regressors which are Gradient Boosting regressor, Random Forest regressor, and linear regressor. Evaluating the experimental results using the RMSE metric, we got an average score of 35.39 for Walker and 51.16 for Car.
Use Visual Features From Surrounding Scenes to Improve Personal Air Quality ...
Paper: http://ceur-ws.org/Vol-2882/paper40.pdf
YouTube: https://youtu.be/SL5Hvu1mARY
Trung-Quan Nguyen, Dang-Hieu Nguyen and Loc Tai Tan Nguyen : Use Visual Features From Surrounding Scenes to Improve Personal Air Quality Data Prediction Performance. Proc. of MediaEval 2020, 14-15 December 2020, Online.
In this paper, we propose a method to predict the personal air quality index in an area by using the combination of the levels of the following pollutants: PM2.5, NO2, and O3, measured from the nearby weather stations of that area, and the photos of surrounding scenes taken at that area. Our approach uses the Inverse Distance Weighted (IDW) technique to estimate the missing air pollutant levels and then use regression to integrate visual features from taken photos to optimize the predicted values. After that, we can use those values to calculate the Air Quality Index (AQI). The results show that the proposed method may not improve the performance of the prediction in some cases.
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Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal...
Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal...
HCMUS at MediaEval 2020: Ensembles of Temporal Deep Neural Networks for Table...
HCMUS at MediaEval 2020: Ensembles of Temporal Deep Neural Networks for Table...
Sports Video Classification: Classification of Strokes in Table Tennis for Me...
Sports Video Classification: Classification of Strokes in Table Tennis for Me...
Predicting Media Memorability from a Multimodal Late Fusion of Self-Attention...
Predicting Media Memorability from a Multimodal Late Fusion of Self-Attention...
Essex-NLIP at MediaEval Predicting Media Memorability 2020 Task
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Pixel Privacy: Quality Camouflage for Social Images
Pixel Privacy: Quality Camouflage for Social Images
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HCMUS at MediaEval 2020:Image-Text Fusion for Automatic News-Images Re-Matching
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HCMUS at Medico Automatic Polyp Segmentation Task 2020: PraNet and ResUnet++ ...
Depth-wise Separable Atrous Convolution for Polyps Segmentation in Gastro-Int...
Depth-wise Separable Atrous Convolution for Polyps Segmentation in Gastro-Int...
Deep Conditional Adversarial learning for polyp Segmentation
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A Temporal-Spatial Attention Model for Medical Image Detection
A Temporal-Spatial Attention Model for Medical Image Detection
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HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Netw...
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Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
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- 1. MediaEval - Multimedia Satellite task 2017 Visual and textual analysis of social media and satellite images for flood detection Centre for Research Technology Hellas – Information Technologies institute (CERTH-iti) Multimedia knowledge & Social media analytics Laboratory (MkLab) Konstantinos Avgerinakis : koafgeri@iti.gr 9/14/2017 1
- 2. 2 DBpedia text modality Top-l filter S1 s1 S2 s2 Non- linear graph fusion Relevance score vector s(q) Flood images Query images+ metadata GoogLeNet CombMax MediaEval - Multimedia Satellite task 2017 DIRSM - Methodology Similarity matrixes S and query-based Similarity vectors s
- 3. MediaEval - Multimedia Satellite task 2017 DIRSM – GoogLeNet-DCNN • Fine tune GoogLeNet [1] on 5055 ImageNet concepts and use the last pooling layer as a global keyframe representation • Use training set of DIRSM dataset to fine-tune GoogLeNet. Train an SVM classifier and discriminate between flood and other images • GoogLeNet-DCNN surpassed: • all provided descriptors (acc, gabor, fcth, jcd, cedd, eh, sc, cl, and tamura) • descriptor that fused features from convolutional layer 3a and 3b of the Places205-GoogLeNet network. 3
- 4. MediaEval - Multimedia Satellite task 2017 DIRSM – DBpedia Spotlight !Use training set metadata (i.e. title, description, user tags) to spot water and flood related key- phrases !Disambiguation with Jaccard similarities [2] 4
- 5. MediaEval - Multimedia Satellite task 2017 DIRSM – multimodal fusion !Late fusion with non-linear graph-based techniques (random walk, diffusion-based) in a non-linear weighted way [3]. !Filter top-l multimodal objects with respect to textual concepts !Create lxl similarity matrixes S1, S2 !Create lx1 similarity vectors s1, s2 !Select 10 examples and using combMax acquire final list of most relevant objects 5
- 6. MediaEval - Multimedia Satellite task 2017 DIRSM – Results (1) Fusion Visual Textual 6
- 7. MediaEval - Multimedia Satellite task 2017 DIRSM - Results (2) !Visual modality achieved the highest retrieval rates !Multiple cutoffs helped to improve the visual results by far !Text retrieval need further improvement !Fusion did not achieve good results as it was based on text concepts 9/14/2017 7 Modalities Single cutoff Several cutoffs Visual 78,82% 92,27% Textual 36,15% 39,90% Fusion 68,57% 83,37%
- 8. MediaEval - Multimedia Satellite task 2017 Mahalanobis, SVM, Linear, DiagLinear, Quadratic RGB + Infrared + GT FDSI – methodology Random Sampling Training RGB + Infrared Predict + Post-Processing Test masks 8
- 9. MediaEval - Multimedia Satellite task 2017 FDSI – results Loc02Loc03Loc01 Loc05 loc01 loc02 loc03 loc04 loc05 loc06 Loc07 81.71% 68.33% 82.08% 47.01% 45.84% 64.92% 56.27% river Terra? cloud resolut ion Loc04Loc06 MediaEval - Multimedia Satellite task 2017
- 10. Conclusions !DIRSM !Improve text retrieval !Classify more disaster classes !Localize people and building in danger! !FDSI !Use DCNN and shallow descriptors for a more meaningful representation 9/14/2017 10 MediaEval - Multimedia Satellite task 2017
- 11. Acknowledgement ! This project has received funding from the European Union’s Horizon 2020 research and innovation programme beAWARE under grant agreement No 700475 ! For further info please contact : koafgeri@iti.gr 9/14/2017 11 MediaEval - Multimedia Satellite task 2017 http://beaware-project.eu/
- 12. References ! [1] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich, “Going deeper with convolutions”. In CVPR, 2015 IEEE Computer Society, 1–9 ! [2] Joachim Daiber, Max Jakob, Chris Hokamp, and Pablo N. Mendes, “Improving Efficiency and Accuracy in Multilingual Entity Extraction”, In the 9th International Conference on Semantic Systems (I-Semantics), 2013. ! [3] Ilias Gialampoukidis, Anastasia Moumtzidou, Dimitris Liparas, Theodora Tsikrika, Stefanos Vrochidis, and Ioannis Kompatsiaris, “Multimedia retrieval based on non-linear graph-based fusion and partial least squares regression”, Multimedia Tools and Applications (2017), 1–21. 9/14/2017 12 MediaEval - Multimedia Satellite task 2017