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| Author | Yaxing Wang; Salman Khan; Abel Gonzalez-Garcia; Joost Van de Weijer; Fahad Shahbaz Khan | ||||
| Title | Semi-supervised Learning for Few-shot Image-to-Image Translation | Type | Conference Article | ||
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2020 | Publication | 33rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
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| Abstract | In the last few years, unpaired image-to-image translation has witnessed remarkable progress. Although the latest methods are able to generate realistic images, they crucially rely on a large number of labeled images. Recently, some methods have tackled the challenging setting of few-shot image-to-image translation, reducing the labeled data requirements for the target domain during inference. In this work, we go one step further and reduce the amount of required labeled data also from the source domain during training. To do so, we propose applying semi-supervised learning via a noise-tolerant pseudo-labeling procedure. We also apply a cycle consistency constraint to further exploit the information from unlabeled images, either from the same dataset or external. Additionally, we propose several structural modifications to facilitate the image translation task under these circumstances. Our semi-supervised method for few-shot image translation, called SEMIT, achieves excellent results on four different datasets using as little as 10% of the source labels, and matches the performance of the main fully-supervised competitor using only 20% labeled data. Our code and models are made public at: this https URL. | ||||
| Address | Virtual; June 2020 | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ WKG2020 | Serial | 3486 | ||
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| Author | Yi Xiao; Felipe Codevilla; Christopher Pal; Antonio Lopez | ||||
| Title | Action-Based Representation Learning for Autonomous Driving | Type | Conference Article | ||
| Year |
2020 | Publication | Conference on Robot Learning | Abbreviated Journal | |
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| Abstract | Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creating end-to-end driving models that map sensor data directly into driving actions are problematic in terms of interpretability, and typically have significant difficulty dealing with spurious correlations. Alternatively, we propose to use this kind of action-based driving data for learning representations. Our experiments show that an affordance-based driving model pre-trained with this approach can leverage a relatively small amount of weakly annotated imagery and outperform pure end-to-end driving models, while being more interpretable. Further, we demonstrate how this strategy outperforms previous methods based on learning inverse dynamics models as well as other methods based on heavy human supervision (ImageNet). | ||||
| Address | virtual; November 2020 | ||||
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| Area | Expedition | Conference | CORL | ||
| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ XCP2020 | Serial | 3487 | ||
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| Author | Gabriel Villalonga; Antonio Lopez | ||||
| Title | Co-Training for On-Board Deep Object Detection | Type | Journal Article | ||
| Year |
2020 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
| Volume | Issue | Pages | 194441 - 194456 | ||
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| Abstract | Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation. | ||||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ ViL2020 | Serial | 3488 | ||
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| Author | Hannes Mueller; Andre Groger; Jonathan Hersh; Andrea Matranga; Joan Serrat | ||||
| Title | Monitoring War Destruction from Space: A Machine Learning Approach | Type | Miscellaneous | ||
| Year |
2020 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep learning techniques combined with data augmentation to expand training samples. We apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. The approach allows generating destruction data with unprecedented scope, resolution, and frequency – only limited by the available satellite imagery – which can alleviate data limitations decisively. | ||||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ MGH2020 | Serial | 3489 | ||
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| Author | Yi Xiao; Felipe Codevilla; Akhil Gurram; Onay Urfalioglu; Antonio Lopez | ||||
| Title | Multimodal end-to-end autonomous driving | Type | Journal Article | ||
| Year |
2020 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
| Volume | Issue | Pages | 1-11 | ||
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| Abstract | A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressing the development of AI drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are less demanding in terms of sensor data annotation. This paper focuses on end-to-end autonomous driving. So far, most proposals relying on this paradigm assume RGB images as input sensor data. However, AVs will not be equipped only with cameras, but also with active sensors providing accurate depth information (e.g., LiDARs). Accordingly, this paper analyses whether combining RGB and depth modalities, i.e. using RGBD data, produces better end-to-end AI drivers than relying on a single modality. We consider multimodality based on early, mid and late fusion schemes, both in multisensory and single-sensor (monocular depth estimation) settings. Using the CARLA simulator and conditional imitation learning (CIL), we show how, indeed, early fusion multimodality outperforms single-modality. | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ XCG2020 | Serial | 3490 | ||
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| Author | Lluis Gomez; Anguelos Nicolaou; Marçal Rusiñol; Dimosthenis Karatzas | ||||
| Title | 12 years of ICDAR Robust Reading Competitions: The evolution of reading systems for unconstrained text understanding | Type | Book Chapter | ||
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2020 | Publication | Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis | Abbreviated Journal | |
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| Publisher | Springer | Place of Publication | Editor | K. Alahari; C.V. Jawahar | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Series on Advances in Computer Vision and Pattern Recognition | Abbreviated Series Title | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | GNR2020 | Serial | 3494 | ||
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| Author | Lluis Gomez; Dena Bazazian; Dimosthenis Karatzas | ||||
| Title | Historical review of scene text detection research | Type | Book Chapter | ||
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2020 | Publication | Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis | Abbreviated Journal | |
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| Publisher | Springer | Place of Publication | Editor | K. Alahari; C.V. Jawahar | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Series on Advances in Computer Vision and Pattern Recognition | Abbreviated Series Title | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GBK2020 | Serial | 3495 | ||
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| Author | Jon Almazan; Lluis Gomez; Suman Ghosh; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | WATTS: A common representation of word images and strings using embedded attributes for text recognition and retrieval | Type | Book Chapter | ||
| Year |
2020 | Publication | Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis | Abbreviated Journal | |
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| Publisher | Springer | Place of Publication | Editor | Analysis”, K. Alahari; C.V. Jawahar | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Series on Advances in Computer Vision and Pattern Recognition | Abbreviated Series Title | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ AGG2020 | Serial | 3496 | ||
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| Author | Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe | ||||
| Title | Retrieval Guided Unsupervised Multi-domain Image to Image Translation | Type | Conference Article | ||
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2020 | Publication | 28th ACM International Conference on Multimedia | Abbreviated Journal | |
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| Abstract | Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data. | ||||
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| Area | Expedition | Conference | ACM | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GLN2020 | Serial | 3497 | ||
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| Author | Tomas Sixta; Julio C. S. Jacques Junior; Pau Buch Cardona; Eduard Vazquez; Sergio Escalera | ||||
| Title | FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition | Type | Conference Article | ||
| Year |
2020 | Publication | ECCV Workshops | Abbreviated Journal | |
| Volume | 12540 | Issue | Pages | 463-481 | |
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| Abstract | This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results. The aim of the challenge was to evaluate accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face verification in the presence of other confounding attributes. Participants were evaluated using an in-the-wild dataset based on reannotated IJB-C, further enriched 12.5K new images and additional labels. The dataset is not balanced, which simulates a real world scenario where AI-based models supposed to present fair outcomes are trained and evaluated on imbalanced data. The challenge attracted 151 participants, who made more 1.8K submissions in total. The final phase of the challenge attracted 36 active teams out of which 10 exceeded 0.999 AUC-ROC while achieving very low scores in the proposed bias metrics. Common strategies by the participants were face pre-processing, homogenization of data distributions, the use of bias aware loss functions and ensemble models. The analysis of top-10 teams shows higher false positive rates (and lower false negative rates) for females with dark skin tone as well as the potential of eyeglasses and young age to increase the false positive rates too. | ||||
| Address | Virtual; August 2020 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ SJB2020 | Serial | 3499 | ||
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| Author | Zhengying Liu; Zhen Xu; Shangeth Rajaa; Meysam Madadi; Julio C. S. Jacques Junior; Sergio Escalera; Adrien Pavao; Sebastien Treguer; Wei-Wei Tu; Isabelle Guyon | ||||
| Title | Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019 | Type | Conference Article | ||
| Year |
2020 | Publication | Proceedings of Machine Learning Research | Abbreviated Journal | |
| Volume | 123 | Issue | Pages | 242-252 | |
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| Abstract | We present the design and results of recent competitions in Automated Deep Learning (AutoDL). In the AutoDL challenge series 2019, we organized 5 machine learning challenges: AutoCV, AutoCV2, AutoNLP, AutoSpeech and AutoDL. The first 4 challenges concern each a specific application domain, such as computer vision, natural language processing and speech recognition. At the time of March 2020, the last challenge AutoDL is still on-going and we only present its design. Some highlights of this work include: (1) a benchmark suite of baseline AutoML solutions, with emphasis on domains for which Deep Learning methods have had prior success (image, video, text, speech, etc); (2) a novel any-time learning framework, which opens doors for further theoretical consideration; (3) a repository of around 100 datasets (from all above domains) over half of which are released as public datasets to enable research on meta-learning; (4) analyses revealing that winning solutions generalize to new unseen datasets, validating progress towards universal AutoML solution; (5) open-sourcing of the challenge platform, the starting kit, the dataset formatting toolkit, and all winning solutions (All information available at {autodl.chalearn.org}). | ||||
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| Area | Expedition | Conference | NEURIPS | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ LXR2020 | Serial | 3500 | ||
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| Author | Albert Clapes; Julio C. S. Jacques Junior; Carla Morral; Sergio Escalera | ||||
| Title | ChaLearn LAP 2020 Challenge on Identity-preserved Human Detection: Dataset and Results | Type | Conference Article | ||
| Year |
2020 | Publication | 15th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 801-808 | ||
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| Abstract | This paper summarizes the ChaLearn Looking at People 2020 Challenge on Identity-preserved Human Detection (IPHD). For the purpose, we released a large novel dataset containing more than 112K pairs of spatiotemporally aligned depth and thermal frames (and 175K instances of humans) sampled from 780 sequences. The sequences contain hundreds of non-identifiable people appearing in a mix of in-the-wild and scripted scenarios recorded in public and private places. The competition was divided into three tracks depending on the modalities exploited for the detection: (1) depth, (2) thermal, and (3) depth-thermal fusion. Color was also captured but only used to facilitate the groundtruth annotation. Still the temporal synchronization of three sensory devices is challenging, so bad temporal matches across modalities can occur. Hence, the labels provided should considered “weak”, although test frames were carefully selected to minimize this effect and ensure the fairest comparison of the participants’ results. Despite this added difficulty, the results got by the participants demonstrate current fully-supervised methods can deal with that and achieve outstanding detection performance when measured in terms of AP@0.50. | ||||
| Address | Virtual; November 2020 | ||||
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| Area | Expedition | Conference | FG | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ CJM2020 | Serial | 3501 | ||
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| Author | Zhengying Liu; Adrien Pavao; Zhen Xu; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Sebastien Treguer | ||||
| Title | How far are we from true AutoML: reflection from winning solutions and results of AutoDL challenge | Type | Conference Article | ||
| Year |
2020 | Publication | 7th ICML Workshop on Automated Machine Learning | Abbreviated Journal | |
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| Abstract | Following the completion of the AutoDL challenge (the final challenge in the ChaLearn
AutoDL challenge series 2019), we investigate winning solutions and challenge results to answer an important motivational question: how far are we from achieving true AutoML? On one hand, the winning solutions achieve good (accurate and fast) classification performance on unseen datasets. On the other hand, all winning solutions still contain a considerable amount of hard-coded knowledge on the domain (or modality) such as image, video, text, speech and tabular. This form of ad-hoc meta-learning could be replaced by more automated forms of meta-learning in the future. Organizing a meta-learning challenge could help forging AutoML solutions that generalize to new unseen domains (e.g. new types of sensor data) as well as gaining insights on the AutoML problem from a more fundamental point of view. The datasets of the AutoDL challenge are a resource that can be used for further benchmarks and the code of the winners has been outsourced, which is a big step towards “democratizing” Deep Learning. |
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| Address | Virtual; July 2020 | ||||
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| Area | Expedition | Conference | ICML | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ LPX2020 | Serial | 3502 | ||
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| Author | Aymen Azaza; Joost Van de Weijer; Ali Douik; Javad Zolfaghari Bengar; Marc Masana | ||||
| Title | Saliency from High-Level Semantic Image Features | Type | Journal | ||
| Year |
2020 | Publication | SN Computer Science | Abbreviated Journal | SN |
| Volume | 1 | Issue | 4 | Pages | 1-12 |
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| Abstract | Top-down semantic information is known to play an important role in assigning saliency. Recently, large strides have been made in improving state-of-the-art semantic image understanding in the fields of object detection and semantic segmentation. Therefore, since these methods have now reached a high-level of maturity, evaluation of the impact of high-level image understanding on saliency estimation is now feasible. We propose several saliency features which are computed from object detection and semantic segmentation results. We combine these features with a standard baseline method for saliency detection to evaluate their importance. Experiments demonstrate that the proposed features derived from object detection and semantic segmentation improve saliency estimation significantly. Moreover, they show that our method obtains state-of-the-art results on (FT, ImgSal, and SOD datasets) and obtains competitive results on four other datasets (ECSSD, PASCAL-S, MSRA-B, and HKU-IS). | ||||
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| Notes | LAMP; 600.120; 600.109; 600.106 | Approved | no | ||
| Call Number | Admin @ si @ AWD2020 | Serial | 3503 | ||
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| Author | Rahma Kalboussi; Aymen Azaza; Joost Van de Weijer; Mehrez Abdellaoui; Ali Douik | ||||
| Title | Object proposals for salient object segmentation in videos | Type | Journal Article | ||
| Year |
2020 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 79 | Issue | 13 | Pages | 8677-8693 |
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| Abstract | Salient object segmentation in videos is generally broken up in a video segmentation part and a saliency assignment part. Recently, object proposals, which are used to segment the image, have had significant impact on many computer vision applications, including image segmentation, object detection, and recently saliency detection in still images. However, their usage has not yet been evaluated for salient object segmentation in videos. Therefore, in this paper, we investigate the application of object proposals to salient object segmentation in videos. In addition, we propose a new motion feature derived from the optical flow structure tensor for video saliency detection. Experiments on two standard benchmark datasets for video saliency show that the proposed motion feature improves saliency estimation results, and that object proposals are an efficient method for salient object segmentation. Results on the challenging SegTrack v2 and Fukuchi benchmark data sets show that we significantly outperform the state-of-the-art. | ||||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | KAW2020 | Serial | 3504 | ||
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