MediaEval 2016 - Tag Propagation in Talking Face Graphs

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Presenter: Guillaume Gravier PUCMinas and IRISA at Multimodal Person Discovery In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Gabriel Sargent, Gabriel Barbosa de Fonseca, Izabela Lyon Freire, Ronan Sicre, Zenilton K. G. Patrocínio Jr, Silvio Jamil F. Guimarães, and Guillaume Gravier Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_34.pdf Video: https://youtu.be/O2Ecgy89hN4 Abstract: This paper describes the systems developed by PUC Minas and IRISA for the person discovery task at MediaEval 2016. We adopt a graph-based representation and investigate two tag-propagation approaches to associate overlays cooccurring with some speaking faces to other visually or audio-visually similar speaking faces. Given a video, we first build a graph from the detected speaking faces (nodes) and their audio-visual similarities (edges). Each node is associated to its co-occurring overlays (tags) when they exist. Then, we consider two tagpropagation approaches, respectively based on a random walk strategy and on Kruskal’s algorithm.

The MOTIF team

Tag propaga(on
in talking face graphs

Gabriel Sargent, Gabriel Fonseca, Izabela Lyon
Freire, Silvio J. Guimarães, Guillaume Gravier

The principle
ProporBon of nodes labeled: ~6% in 2016 (~25% in 2015)

Graph construcBon
node: speaking face ——> facetrack with overlapping speech segment
+ co-occurring text overlays —> with name enBty detecBon

edge weight: visual/audiovisual similarity between two
speaking faces

•  Visual similarity:- each facetrack = central face
- each central face = CNN-based feature vector
- cosine similarity

•  Audio similarity1: binary similarity from speaker diarizaBon annotaBons

•  Audio similarity2:- each speech segment described by GMMs using cepstral features
- similarity: approximaBon of Kullback-Leibler divergence

•  Fusion: weighted average of audio and visual similariBes

Tag propagaBon
Random Walk
•  Tag propagaBon is computed by applying a random
walk with absorbing states on a probability graph.
0.3
0.2
0.9
0.8
0.5
0.9
1
1
0.8

Tag propagaBon
Hierarchical
•  Based on Kruskal’s algorithm for calculaBng
minimum spanning trees.
•  Label propagaBon happens when two sets are
merged into one.
merge
Tagged Set Untagged set
New Tagged Set

Submissions and results

Conclusions
•  We’re quite happy with the results ;)
•  Tag propagaBon more crucial in 2016 than in 2016
•  minimum spanning tree beger than random walk
•  Next steps
•  improve voice comparison (in progress)
•  improve iniBal labeling (because no possible recovery)
•  beger tuning (as usual, done late in the game)

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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.

A Temporal-Spatial Attention Model for Medical Image Detection

A Temporal-Spatial Attention Model for Medical Image Detection

A Temporal-Spatial Attention Model for Medical Image Detection

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.

HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Netw...

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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.

Fine-tuning for Polyp Segmentation with Attention

Fine-tuning for Polyp Segmentation with Attention

Fine-tuning for Polyp Segmentation with Attention

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.

Bigger Networks are not Always Better: Deep Convolutional Neural Networks for...

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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.

Insights for wellbeing: Predicting Personal Air Quality Index using Regressio...

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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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Ultrasound color Doppler imaging has been routinely used for the diagnosis of cardiovascular diseases, enabling real-time flow visualization through the Doppler effect. Yet, its inability to provide true flow velocity vectors due to its one-dimensional detection limits its efficacy. To overcome this limitation, various VFI schemes, including multi-angle beams, speckle tracking, and transverse oscillation, have been explored, with some already available commercially. However, many of these methods still rely on autocorrelation, which poses inherent issues such as underestimation, aliasing, and the need for large ensemble sizes. Conversely, speckle-tracking-based VFI enables lateral velocity estimation but suffers from significantly lower accuracy compared to axial velocity measurements. To address these challenges, we have presented a speckle-tracking-based VFI approach utilizing multi-angle ultrafast plane wave imaging. Our approach involves estimating axial velocity components projected onto individual steered plane waves, which are then combined to derive the velocity vector. Additionally, we've introduced a VFI visualization technique with high spatial and temporal resolutions capable of tracking flow particle trajectories. Simulation and flow phantom experiments demonstrate that the proposed VFI method outperforms both speckle-tracking-based VFI and autocorrelation VFI counterparts by at least a factor of three. Furthermore, in vivo measurements on carotid arteries using the Prodigy ultrasound scanner demonstrate the effectiveness of our approach compared to existing methods, providing a more robust imaging tool for hemodynamic studies. Learning objectives: - Understand fundamental limitations of color Doppler imaging. - Understand principles behind advanced vector flow imaging techniques. - Familiarize with the ultrasound speckle tracking technique and its implications in flow imaging. - Explore experiments conducted using multi-angle plane wave ultrafast imaging, specifically utilizing the pulse-sequence mode on a 128-channel ultrasound research platform.

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fruit fly, this slide mainly made for pumpkin fruit fly, this is also known as drosophila melangastor, this type of fruit fly destroyed the mainly vegetables crops. if you want to known examples this types of fly which is destroy the pumpkin, tomato, brinjal, potato, bottle guard, ridge guard, bitter guard, cucumber, water melon, musk melon, bean, long bean and other many vegetables which has fruits. they distryed fruit fly. thank you...

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Context. WASP-76 b has been a recurrent subject of study since the detection of a signature in high-resolution transit spectroscopy data indicating an asymmetry between the two limbs of the planet. The existence of this asymmetric signature has been confirmed by multiple studies, but its physical origin is still under debate. In addition, it contrasts with the absence of asymmetry reported in the infrared (IR) phase curve. Aims. We provide a more comprehensive dataset of WASP-76 b with the goal of drawing a complete view of the physical processes at work in this atmosphere. In particular, we attempt to reconcile visible high-resolution transit spectroscopy data and IR broadband phase curves. Methods. We gathered 3 phase curves, 20 occultations, and 6 transits for WASP-76 b in the visible with the CHEOPS space telescope. We also report the analysis of three unpublished sectors observed by the TESS space telescope (also in the visible), which represents 34 phase curves. Results. WASP-76 b displays an occultation of 260±11 and 152±10 ppm in TESS and CHEOPS bandpasses respectively. Depending on the composition assumed for the atmosphere and the data reduction used for the IR data, we derived geometric albedo estimates that range from 0.05 ± 0.023 to 0.146 ± 0.013 and from <0.13 to 0.189 ± 0.017 in the CHEOPS and TESS bandpasses, respectively. As expected from the IR phase curves, a low-order model of the phase curves does not yield any detectable asymmetry in the visible either. However, an empirical model allowing for sharper phase curve variations offers a hint of a flux excess before the occultation, with an amplitude of ∼40 ppm, an orbital offset of ∼−30◦ , and a width of ∼20◦ . We also constrained the orbital eccentricity of WASP-76 b to a value lower than 0.0067, with a 99.7% confidence level. This result contradicts earlier proposed scenarios aimed at explaining the asymmetry observed in high-resolution transit spectroscopy. Conclusions. In light of these findings, we hypothesise that WASP-76 b could have night-side clouds that extend predominantly towards its eastern limb. At this limb, the clouds would be associated with spherical droplets or spherically shaped aerosols of an unknown species, which would be responsible for a glory effect in the visible phase curves.

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MediaEval 2016 - Tag Propagation in Talking Face Graphs

1. The MOTIF team Tag propaga(on in talking face graphs Gabriel Sargent, Gabriel Fonseca, Izabela Lyon Freire, Silvio J. Guimarães, Guillaume Gravier

2. The principle ProporBon of nodes labeled: ~6% in 2016 (~25% in 2015)

3. Graph construcBon node: speaking face ——> facetrack with overlapping speech segment + co-occurring text overlays —> with name enBty detecBon edge weight: visual/audiovisual similarity between two speaking faces •  Visual similarity:- each facetrack = central face - each central face = CNN-based feature vector - cosine similarity •  Audio similarity1: binary similarity from speaker diarizaBon annotaBons •  Audio similarity2:- each speech segment described by GMMs using cepstral features - similarity: approximaBon of Kullback-Leibler divergence •  Fusion: weighted average of audio and visual similariBes

4. Tag propagaBon Random Walk •  Tag propagaBon is computed by applying a random walk with absorbing states on a probability graph. 0.3 0.2 0.9 0.8 0.5 0.9 1 1 0.8

5. Tag propagaBon Hierarchical •  Based on Kruskal’s algorithm for calculaBng minimum spanning trees. •  Label propagaBon happens when two sets are merged into one. merge Tagged Set Untagged set New Tagged Set

6. Submissions and results

7. Conclusions •  We’re quite happy with the results ;) •  Tag propagaBon more crucial in 2016 than in 2016 •  minimum spanning tree beger than random walk •  Next steps •  improve voice comparison (in progress) •  improve iniBal labeling (because no possible recovery) •  beger tuning (as usual, done late in the game)