MediaEval 2016 - EUMSSI Team at the MediaEval Person Discovery Challenge

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Presenter: Nam Le EUMSSI Team at the MediaEval Person Discovery Challenge 2016 In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Nam D. Le, Sylvain Meignier, Jean-Marc Odobez Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_46.pdf Video: https://youtu.be/axt_PxhIrJ4 Abstract: We present the results of the EUMSSI team’s participation in the Multimodal Person Discovery task. The goal is to identify all people who simultaneously appear and speak in a video corpus. In the proposed system, besides improving each modality, we emphasize on the ranking of multiple results from both audio stream and visual stream.

Science

EUMSSI
team
at
the
MediaEval
Person
Discovery
Challenge
2016
Nam
Le,
Jean-‐Marc
Odobez,
Sylvain
Meignier
{nle,
odobez}@idiap.ch
sylvain.meignier@univ-‐lemans.fr

Overview
07/12/2016
Olivier Truchot
Marisol Turaine

Video
OCR
and
NER3
07/12/2016
Original
Image
Text region
detection
Text
extraction
Text
recognition
Hypothesis
merging
• Multiple
image
segmentations
of
the
same
region

è all
results
are
compared
and
aggregated
over
time

è several
hypotheses
è high
recall
• NER
based
on
MITIE
with
heuristics.

Face
diarization4
07/12/2016
DPM
CRF-multi-target
Face
clustering Hierarchical clustering
shots
Face
tracking
Face
detection

Talking
face
detection5
07/12/2016
Face
track
9
directions
of
optical
flows
PCA
⇒ 𝒙 𝒕
x% x& x'(&
LSTM LSTM LSTM…
x& x) x'
Mean
Pooling Classifier
ℎ%
ℎ&
h'(&
DW
dataset
for
talking
face
&
dubbing:
http://bit.ly/dw-‐dubbing

• LIUM
diarization tool:

www-‐lium.univ-‐lemans.fr/en/content/liumspkdiarization
• Input:
a
video
• Output:
homogeneous
segments

Speaker
diarization6
07/12/2016

Result
ranking7
07/12/2016
• Direct naming: maximize co-occurrences between clusters and
named entities.
− Face naming: name 𝑁-
.
and talking score 𝑡 𝑁-
.
− Speaker naming: name 𝑁-
0
and equal score 1.0
• For one shot 𝑠 : 𝑄6 =
∅
• Names which face agrees with speaker naming rank highest:
− If ∃𝑁;
0
/𝑁-
.
= 𝑁;
0
: 𝑄6
← 𝑁-
.
, 2.0 + 𝑡 𝑁-
.
• Otherwise, face naming has higher rank:
− If ∄𝑁;
0
/𝑁-
.
= 𝑁;
0
: 𝑄6
← 𝑁-
.
, 1.0 + 𝑡 𝑁-
.
− If ∄𝑁-
0
/𝑁-
.
= 𝑁;
0
: 𝑄6
← 𝑁-
0
, 1.0

Result
ranking8
07/12/2016
Shot
1 Shot
2 Shot
3 Shot
4
Query: Results:
2
– 4
– 1
-‐ 3

Submissions9
07/12/2016
MAP@1 MAP@10 MAP@100
Sub.
(1) 30.3 22.0 21.0
Sub.
(2) 58.6 42.9 42.0
Sub. (3) 64.2 53.1 52.1
Sub.
(4) 68.3 56.2 54.7
Sub.
(5) 79.2 65.2 63.4
Face
diarization Baseline
OCR-‐NER Face
namingSub.
(1)
Face
diarization Our
OCR-‐NER Face
namingSub.
(2)
Face
diarization Our
OCR-‐NER
Talking

face
naming
Sub.
(3)
Face
diarization OCR-‐NER Talking
face

naming
+
Speaker
naming
Sub.
(4)
Speaker

diarization
OCR-‐NER
Sub.
(4)
+
Sub.
(1)
+
Baseline
2Sub.
(5)

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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

Overview of MediaEval 2020 Predicting Media Memorability task: What Makes a V...

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper45.pdf Benoit Bonnet, Teddy Furon and Patrick Bas : Fooling an Automatic Image Quality Estimator. Proc. of MediaEval 2020, 14-15 December 2020, Online. In this paper we present our work on the 2020 MediaEval task: Pixel "Privacy: Quality Camouflage for Social Images". Blind Image Quality Assessment (BIQA) is a classifier that for any given image will return a quality score. Our task is to modify an image to decrease its BIQA score while maintaining a good perceived quality. Since BIQA is a deep neural network, we worked on an adversarial attack approach of the problem.

Fooling an Automatic Image Quality Estimator

multimediaeval

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

Fooling Blind Image Quality Assessment by Optimizing a Human-Understandable C...

multimediaeval

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

Pixel Privacy: Quality Camouflage for Social Images

multimediaeval

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

HCMUS at MediaEval 2020:Image-Text Fusion for Automatic News-Images Re-Matching

multimediaeval

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.

Efficient Supervision Net: Polyp Segmentation using EfficientNet and Attentio...

multimediaeval

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).

HCMUS at Medico Automatic Polyp Segmentation Task 2020: PraNet and ResUnet++ ...

multimediaeval

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.

Depth-wise Separable Atrous Convolution for Polyps Segmentation in Gastro-Int...

multimediaeval

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.

Deep Conditional Adversarial learning for polyp Segmentation

multimediaeval

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

multimediaeval

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...

multimediaeval

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

multimediaeval

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...

multimediaeval

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...

multimediaeval

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.

Use Visual Features From Surrounding Scenes to Improve Personal Air Quality ...

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MediaEval 2016 - EUMSSI Team at the MediaEval Person Discovery Challenge

1. EUMSSI team at the MediaEval Person Discovery Challenge 2016 Nam Le, Jean-‐Marc Odobez, Sylvain Meignier {nle, odobez}@idiap.ch sylvain.meignier@univ-‐lemans.fr

2. Overview 07/12/2016 Olivier Truchot Marisol Turaine

3. Video OCR and NER3 07/12/2016 Original Image Text region detection Text extraction Text recognition Hypothesis merging • Multiple image segmentations of the same region è all results are compared and aggregated over time è several hypotheses è high recall • NER based on MITIE with heuristics.

4. Face diarization4 07/12/2016 DPM CRF-multi-target Face clustering Hierarchical clustering shots Face tracking Face detection

5. Talking face detection5 07/12/2016 Face track 9 directions of optical flows PCA ⇒ 𝒙 𝒕 x% x& x'(& LSTM LSTM LSTM… x& x) x' Mean Pooling Classifier ℎ% ℎ& h'(& DW dataset for talking face & dubbing: http://bit.ly/dw-‐dubbing

6. • LIUM diarization tool: www-‐lium.univ-‐lemans.fr/en/content/liumspkdiarization • Input: a video • Output: homogeneous segments Speaker diarization6 07/12/2016

7. Result ranking7 07/12/2016 • Direct naming: maximize co-occurrences between clusters and named entities. − Face naming: name 𝑁- . and talking score 𝑡 𝑁- . − Speaker naming: name 𝑁- 0 and equal score 1.0 • For one shot 𝑠 : 𝑄6 = ∅ • Names which face agrees with speaker naming rank highest: − If ∃𝑁; 0 /𝑁- . = 𝑁; 0 : 𝑄6 ← 𝑁- . , 2.0 + 𝑡 𝑁- . • Otherwise, face naming has higher rank: − If ∄𝑁; 0 /𝑁- . = 𝑁; 0 : 𝑄6 ← 𝑁- . , 1.0 + 𝑡 𝑁- . − If ∄𝑁- 0 /𝑁- . = 𝑁; 0 : 𝑄6 ← 𝑁- 0 , 1.0

8. Result ranking8 07/12/2016 Shot 1 Shot 2 Shot 3 Shot 4 Query: Results: 2 – 4 – 1 -‐ 3

9. Submissions9 07/12/2016 MAP@1 MAP@10 MAP@100 Sub. (1) 30.3 22.0 21.0 Sub. (2) 58.6 42.9 42.0 Sub. (3) 64.2 53.1 52.1 Sub. (4) 68.3 56.2 54.7 Sub. (5) 79.2 65.2 63.4 Face diarization Baseline OCR-‐NER Face namingSub. (1) Face diarization Our OCR-‐NER Face namingSub. (2) Face diarization Our OCR-‐NER Talking face naming Sub. (3) Face diarization OCR-‐NER Talking face naming + Speaker naming Sub. (4) Speaker diarization OCR-‐NER Sub. (4) + Sub. (1) + Baseline 2Sub. (5)

10. 12/7/16 The End10

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