Visual7W Grounded Question Answering in Images

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Universitat Politècnica de Catalunya

Slides by Issey Masuda about the paper: Zhu, Yuke, Oliver Groth, Michael Bernstein, and Li Fei-Fei. "Visual7W: Grounded Question Answering in Images." CVPR 2016. We have seen great progress in basic perceptual tasks such as object recognition and detection. However, AI models still fail to match humans in high-level vision tasks due to the lack of capacities for deeper reasoning. Recently the new task of visual question answering (QA) has been proposed to evaluate a model's capacity for deep image understanding. Previous works have established a loose, global association between QA sentences and images. However, many questions and answers, in practice, relate to local regions in the images. We establish a semantic link between textual descriptions and image regions by object-level grounding. It enables a new type of QA with visual answers, in addition to textual answers used in previous work. We study the visual QA tasks in a grounded setting with a large collection of 7W multiple-choice QA pairs. Furthermore, we evaluate human performance and several baseline models on the QA tasks. Finally, we propose a novel LSTM model with spatial attention to tackle the 7W QA tasks.

Visual7W
Grounded Question Answering in Images
Yuke Zhu, Oliver Groth, Michael Bernstein, Li Fei-Fei
Slides by Issey Masuda Mora
Computer Vision Reading Group (09/05/2016)
[arXiv] [web] [GitHub]

Context

Visual Question Answering
Goal: predict the answer of a given question related to an image

Motivation
New Turing test? How to evaluate AI’s image
understanding?

Visual7W

The 7W
WHAT
WHERE
WHEN
WHO
WHY
HOW
WHICH
Questions: multi-choice
4 candidates, only one correct

Grounding: image-text correspondences
Exploit the relation between image regions and nouns in the questions

The new answer is...
Question-Answer types:
● Telling questions: the answer is
text
● Pointing questions: a new type of
QA that they introduce where the
answers are image regions

Related work

Common approach
Who is under the umbrella?
Extract visual
features
Embedding
Merge Predict answer Two women

The Dataset

Visual7W Dataset
Characteristics:
● 47.300 images from COCO dataset
● 327.939 QA pairs
● 561.459 object bounding boxes spread across 36.579 categories

Creating the Dataset
Procedure:
● Write QA pairs
● 3 AMT workers evaluate as good or bad each pair
● Only the ones with at least 2 good evaluations are considered
● Write the 3 wrong answers (having the right one)
● Extract object names and draw bounding boxes for each one

The Model

Attention-based model
Pointing questions model

Experiments &
Results

Experiments
Different experiments have been conducted depending on the information given to
the subject:
● Only the question
● Question + Image
Subjects/models:
● Human
● Logistic regression
● LSTM
● LSTM + attention model

Results

Conclusions

Conclusions
● Visual QA model has been presented
● Attention model to focus on local regions of the image
● Dataset created with goundings

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Machine translation and computer vision have greatly benefited of the advances in deep learning. The large and diverse amount of textual and visual data have been used to train neural networks whether in a supervised or self-supervised manner. Nevertheless, the convergence of the two field in sign language translation and production is still poses multiple open challenges, like the low video resources, limitations in hand pose estimation, or 3D spatial grounding from poses. This talk will present these challenges and the How2✌️Sign dataset (https://how2sign.github.io) recorded at CMU in collaboration with UPC, BSC, Gallaudet University and Facebook. https://imatge.upc.edu/web/publications/sign-language-translation-and-production-multimedia-and-multimodal-challenges-all

Open challenges in sign language translation and production

Open challenges in sign language translation and production

Open challenges in sign language translation and production

Universitat Politècnica de Catalunya

https://imatge-upc.github.io/synthref/ Integrating computer vision with natural language processing has achieved significant progress over the last years owing to the continuous evolution of deep learning. A novel vision and language task, which is tackled in the present Master thesis is referring video object segmentation, in which a language query defines which instance to segment from a video sequence. One of the biggest challenges for this task is the lack of relatively large annotated datasets since a tremendous amount of time and human effort is required for annotation. Moreover, existing datasets suffer from poor quality annotations in the sense that approximately one out of ten language expressions fails to uniquely describe the target object. The purpose of the present Master thesis is to address these challenges by proposing a novel method for generating synthetic referring expressions for an image (video frame). This method pro- duces synthetic referring expressions by using only the ground-truth annotations of the objects as well as their attributes, which are detected by a state-of-the-art object detection deep neural network. One of the advantages of the proposed method is that its formulation allows its application to any object detection or segmentation dataset. By using the proposed method, the first large-scale dataset with synthetic referring expressions for video object segmentation is created, based on an existing large benchmark dataset for video instance segmentation. A statistical analysis and comparison of the created synthetic dataset with existing ones is also provided in the present Master thesis. The conducted experiments on three different datasets used for referring video object segmentation prove the efficiency of the generated synthetic data. More specifically, the obtained results demonstrate that by pre-training a deep neural network with the proposed synthetic dataset one can improve the ability of the network to generalize across different datasets, without any additional annotation cost. This outcome is even more important taking into account that no additional annotation cost is involved.

Generation of Synthetic Referring Expressions for Object Segmentation in Videos

Generation of Synthetic Referring Expressions for Object Segmentation in Videos

Generation of Synthetic Referring Expressions for Object Segmentation in Videos

Universitat Politècnica de Catalunya

Master MATT thesis defense by Juan José Nieto Advised by Víctor Campos and Xavier Giro-i-Nieto. 27th May 2021. Pre-training Reinforcement Learning (RL) agents in a task-agnostic manner has shown promising results. However, previous works still struggle to learn and discover meaningful skills in high-dimensional state-spaces. We approach the problem by leveraging unsupervised skill discovery and self-supervised learning of state representations. In our work, we learn a compact latent representation by making use of variational or contrastive techniques. We demonstrate that both allow learning a set of basic navigation skills by maximizing an information theoretic objective. We assess our method in Minecraft 3D maps with different complexities. Our results show that representations and conditioned policies learned from pixels are enough for toy examples, but do not scale to realistic and complex maps. We also explore alternative rewards and input observations to overcome these limitations. https://imatge.upc.edu/web/publications/discovery-and-learning-navigation-goals-pixels-minecraft

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Universitat Politècnica de Catalunya

Peter Muschick MSc thesis Universitat Pollitecnica de Catalunya, 2020 Sign language recognition and translation has been an active research field in the recent years with most approaches using deep neural networks to extract information from sign language data. This work investigates the mostly disregarded approach of using human keypoint estimation from image and video data with OpenPose in combination with transformer network architecture. Firstly, it was shown that it is possible to recognize individual signs (4.5% word error rate (WER)). Continuous sign language recognition though was more error prone (77.3% WER) and sign language translation was not possible using the proposed methods, which might be due to low accuracy scores of human keypoint estimation by OpenPose and accompanying loss of information or insufficient capacities of the used transformer model. Results may improve with the use of datasets containing higher repetition rates of individual signs or focusing more precisely on keypoint extraction of hands.

Learn2Sign : Sign language recognition and translation using human keypoint e...

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Universitat Politècnica de Catalunya

Intepretability / Explainable AI for Deep Neural Networks

Intepretability / Explainable AI for Deep Neural Networks

Intepretability / Explainable AI for Deep Neural Networks

Universitat Politècnica de Catalunya

Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.

Convolutional Neural Networks - Xavier Giro - UPC TelecomBCN Barcelona 2020

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Universitat Politècnica de Catalunya

Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.

Self-Supervised Audio-Visual Learning - Xavier Giro - UPC TelecomBCN Barcelon...

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Universitat Politècnica de Catalunya

Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.

Attention for Deep Learning - Xavier Giro - UPC TelecomBCN Barcelona 2020

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Attention for Deep Learning - Xavier Giro - UPC TelecomBCN Barcelona 2020

Universitat Politècnica de Catalunya

https://telecombcn-dl.github.io/dlai-2020/ Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.

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Universitat Politècnica de Catalunya

https://telecombcn-dl.github.io/drl-2020/ This course presents the principles of reinforcement learning as an artificial intelligence tool based on the interaction of the machine with its environment, with applications to control tasks (eg. robotics, autonomous driving) o decision making (eg. resource optimization in wireless communication networks). It also advances in the development of deep neural networks trained with little or no supervision, both for discriminative and generative tasks, with special attention on multimedia applications (vision, language and speech).

Q-Learning with a Neural Network - Xavier Giró - UPC Barcelona 2020

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Universitat Politècnica de Catalunya

Giro-i-Nieto, X. One Perceptron to Rule Them All: Language, Vision, Audio and Speech. In Proceedings of the 2020 International Conference on Multimedia Retrieval (pp. 7-8). Tutorial page: https://imatge.upc.edu/web/publications/one-perceptron-rule-them-all-language-vision-audio-and-speech-tutorial Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalization properties of deep neural representation. This tutorial will firstly review the basic neural architectures to encode and decode vision, text and audio, to later review the those models that have successfully translated information across modalities.

Language and Vision with Deep Learning - Xavier Giró - ACM ICMR 2020 (Tutorial)

Language and Vision with Deep Learning - Xavier Giró - ACM ICMR 2020 (Tutorial)

Language and Vision with Deep Learning - Xavier Giró - ACM ICMR 2020 (Tutorial)

Universitat Politècnica de Catalunya

Image segmentation is a classic computer vision task that aims at labeling pixels with semantic classes. These slides provide an overview of the basic approaches applied from the deep learning field to tackle this challenge and presents the basic subtasks (semantic, instance and panoptic segmentation) and related datasets. Presented at the International Summer School on Deep Learning (ISSonDL) 2020 held online and organized by the University of Gdansk (Poland) between the 30th August and 2nd September. http://2020.dl-lab.eu/virtual-summer-school-on-deep-learning/

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Image Segmentation with Deep Learning - Xavier Giro & Carles Ventura - ISSonD...

Universitat Politècnica de Catalunya

https://imatge-upc.github.io/rvos-mots/ Video object segmentation can be understood as a sequence-to-sequence task that can benefit from the curriculum learning strategies for better and faster training of deep neural networks. This work explores different schedule sampling and frame skipping variations to significantly improve the performance of a recurrent architecture. Our results on the car class of the KITTI-MOTS challenge indicate that, surprisingly, an inverse schedule sampling is a better option than a classic forward one. Also, that a progressive skipping of frames during training is beneficial, but only when training with the ground truth masks instead of the predicted ones.

Curriculum Learning for Recurrent Video Object Segmentation

Curriculum Learning for Recurrent Video Object Segmentation

Curriculum Learning for Recurrent Video Object Segmentation

Universitat Politècnica de Catalunya

Deep neural networks have achieved outstanding results in various applications such as vision, language, audio, speech, or reinforcement learning. These powerful function approximators typically require large amounts of data to be trained, which poses a challenge in the usual case where little labeled data is available. During the last year, multiple solutions have been proposed to leverage this problem, based on the concept of self-supervised learning, which can be understood as a specific case of unsupervised learning. This talk will cover its basic principles and provide examples in the field of multimedia.

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Universitat Politècnica de Catalunya

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1. Visual7W Grounded Question Answering in Images Yuke Zhu, Oliver Groth, Michael Bernstein, Li Fei-Fei Slides by Issey Masuda Mora Computer Vision Reading Group (09/05/2016) [arXiv] [web] [GitHub]

3. Visual Question Answering Goal: predict the answer of a given question related to an image

4. Motivation New Turing test? How to evaluate AI’s image understanding?

6. The 7W WHAT WHERE WHEN WHO WHY HOW WHICH Questions: multi-choice 4 candidates, only one correct

7. Grounding: image-text correspondences Exploit the relation between image regions and nouns in the questions

8. The new answer is... Question-Answer types: ● Telling questions: the answer is text ● Pointing questions: a new type of QA that they introduce where the answers are image regions

9. Related work

10. Common approach Who is under the umbrella? Extract visual features Embedding Merge Predict answer Two women

11. The Dataset

12. Visual7W Dataset Characteristics: ● 47.300 images from COCO dataset ● 327.939 QA pairs ● 561.459 object bounding boxes spread across 36.579 categories

13. Creating the Dataset Procedure: ● Write QA pairs ● 3 AMT workers evaluate as good or bad each pair ● Only the ones with at least 2 good evaluations are considered ● Write the 3 wrong answers (having the right one) ● Extract object names and draw bounding boxes for each one

15. Attention-based model Pointing questions model

16. Experiments & Results

17. Experiments Different experiments have been conducted depending on the information given to the subject: ● Only the question ● Question + Image Subjects/models: ● Human ● Logistic regression ● LSTM ● LSTM + attention model

19. Conclusions

20. Conclusions ● Visual QA model has been presented ● Attention model to focus on local regions of the image ● Dataset created with goundings