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Albert Clapes; Ozan Bilici; Dariia Temirova; Egils Avots; Gholamreza Anbarjafari; Sergio Escalera |
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Title |
From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation |
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Conference Article |
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2018 |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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2373-2382 |
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Salt Lake City; USA; June 2018 |
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CVPRW |
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HUPBA |
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no |
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Admin @ si @ |
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3116 |
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Mohammad A. Haque; Ruben B. Bautista; Kamal Nasrollahi; Sergio Escalera; Christian B. Laursen; Ramin Irani; Ole K. Andersen; Erika G. Spaich; Kaustubh Kulkarni; Thomas B. Moeslund; Marco Bellantonio; Golamreza Anbarjafari; Fatemeh Noroozi |
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Title |
Deep Multimodal Pain Recognition: A Database and Comparision of Spatio-Temporal Visual Modalities, Faces and Gestures |
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Conference Article |
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Year |
2018 |
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13th IEEE Conference on Automatic Face and Gesture Recognition |
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250 - 257 |
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Pain is a symptom of many disorders associated with actual or potential tissue damage in human body. Managing pain is not only a duty but also highly cost prone. The most primitive state of pain management is the assessment of pain. Traditionally it was accomplished by self-report or visual inspection by experts. However, automatic pain assessment systems from facial videos are also rapidly evolving due to the need of managing pain in a robust and cost effective way. Among different challenges of automatic pain assessment from facial video data two issues are increasingly prevalent: first, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data on shallow learning scenarios. However, employing deep learning techniques for spatio-temporal analysis considering Depth (D) and Thermal (T) along with RGB has high potential in this area. In this paper, we present the first state-of-the-art publicly available database, 'Multimodal Intensity Pain (MIntPAIN)' database, for RGBDT pain level recognition in sequences. We provide a first baseline results including 5 pain levels recognition by analyzing independent visual modalities and their fusion with CNN and LSTM models. From the experimental evaluation we observe that fusion of modalities helps to enhance recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate. |
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Xian; China; May 2018 |
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HUPBA; no proj |
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no |
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Admin @ si @ HBN2018 |
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3117 |
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Rain Eric Haamer; Kaustubh Kulkarni; Nasrin Imanpour; Mohammad Ahsanul Haque; Egils Avots; Michelle Breisch; Kamal Nasrollahi; Sergio Escalera; Cagri Ozcinar; Xavier Baro; Ahmad R. Naghsh-Nilchi; Thomas B. Moeslund; Gholamreza Anbarjafari |
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Title |
Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification |
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Conference Article |
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2018 |
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8th International Workshop on Human Behavior Understanding |
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Facial dynamics can be considered as unique signatures for discrimination between people. These have started to become important topic since many devices have the possibility of unlocking using face recognition or verification. In this work, we evaluate the efficacy of the transition frames of video in emotion as compared to the peak emotion frames for identification. For experiments with transition frames we extract features from each frame of the video from a fine-tuned VGG-Face Convolutional Neural Network (CNN) and geometric features from facial landmark points. To model the temporal context of the transition frames we train a Long-Short Term Memory (LSTM) on the geometric and the CNN features. Furthermore, we employ two fusion strategies: first, an early fusion, in which the geometric and the CNN features are stacked and fed to the LSTM. Second, a late fusion, in which the prediction of the LSTMs, trained independently on the two features, are stacked and used with a Support Vector Machine (SVM). Experimental results show that the late fusion strategy gives the best results and the transition frames give better identification results as compared to the peak emotion frames. |
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Xian; China; May 2018 |
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FGW |
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HUPBA; no proj |
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no |
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Call Number |
Admin @ si @ HKI2018 |
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3118 |
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Author |
Mohammad N. S. Jahromi; Morten Bojesen Bonderup; Maryam Asadi-Aghbolaghi; Egils Avots; Kamal Nasrollahi; Sergio Escalera; Shohreh Kasaei; Thomas B. Moeslund; Gholamreza Anbarjafari |
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Title |
Automatic Access Control Based on Face and Hand Biometrics in a Non-cooperative Context |
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Conference Article |
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2018 |
Publication |
IEEE Winter Applications of Computer Vision Workshops |
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28-36 |
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IEEE Winter Applications of Computer Vision Workshops |
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Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users. |
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Lake Tahoe; USA; March 2018 |
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WACVW |
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HUPBA; 602.133 |
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no |
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Call Number |
Admin @ si @ JBA2018 |
Serial |
3121 |
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Author |
Hassan Ahmed Sial; S. Sancho; Ramon Baldrich; Robert Benavente; Maria Vanrell |
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Title |
Color-based data augmentation for Reflectance Estimation |
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Conference Article |
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Year |
2018 |
Publication |
26th Color Imaging Conference |
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284-289 |
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Deep convolutional architectures have shown to be successful frameworks to solve generic computer vision problems. The estimation of intrinsic reflectance from single image is not a solved problem yet. Encoder-Decoder architectures are a perfect approach for pixel-wise reflectance estimation, although it usually suffers from the lack of large datasets. Lack of data can be partially solved with data augmentation, however usual techniques focus on geometric changes which does not help for reflectance estimation. In this paper we propose a color-based data augmentation technique that extends the training data by increasing the variability of chromaticity. Rotation on the red-green blue-yellow plane of an opponent space enable to increase the training set in a coherent and sound way that improves network generalization capability for reflectance estimation. We perform some experiments on the Sintel dataset showing that our color-based augmentation increase performance and overcomes one of the state-of-the-art methods. |
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Vancouver; November 2018 |
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CIC |
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CIC |
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no |
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Call Number |
Admin @ si @ SSB2018a |
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3129 |
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Author |
Yaxing Wang; Chenshen Wu; Luis Herranz; Joost Van de Weijer; Abel Gonzalez-Garcia; Bogdan Raducanu |
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Title |
Transferring GANs: generating images from limited data |
Type |
Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision |
Abbreviated Journal |
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Volume |
11210 |
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Pages |
220-236 |
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Keywords |
Generative adversarial networks; Transfer learning; Domain adaptation; Image generation |
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Abstract |
ransferring knowledge of pre-trained networks to new domains by means of fine-tuning is a widely used practice for applications based on discriminative models. To the best of our knowledge this practice has not been studied within the context of generative deep networks. Therefore, we study domain adaptation applied to image generation with generative adversarial networks. We evaluate several aspects of domain adaptation, including the impact of target domain size, the relative distance between source and target domain, and the initialization of conditional GANs. Our results show that using knowledge from pre-trained networks can shorten the convergence time and can significantly improve the quality of the generated images, especially when target data is limited. We show that these conclusions can also be drawn for conditional GANs even when the pre-trained model was trained without conditioning. Our results also suggest that density is more important than diversity and a dataset with one or few densely sampled classes is a better source model than more diverse datasets such as ImageNet or Places. |
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Munich; September 2018 |
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ECCV |
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LAMP; 600.109; 600.106; 600.120 |
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no |
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Admin @ si @ WWH2018a |
Serial |
3130 |
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Author |
Yaxing Wang; Joost Van de Weijer; Luis Herranz |
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Title |
Mix and match networks: encoder-decoder alignment for zero-pair image translation |
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Conference Article |
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Year |
2018 |
Publication |
31st IEEE Conference on Computer Vision and Pattern Recognition |
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5467 - 5476 |
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We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform unseen image translation tasks between domains or modalities for which explicit paired samples were not seen during training. We study the impact of autoencoders, side information and losses in improving the alignment and transferability of trained pairwise translation models to unseen translations. We show our approach is scalable and can perform colorization and style transfer between unseen combinations of domains. We evaluate our system in a challenging cross-modal setting where semantic segmentation is estimated from depth images, without explicit access to any depth-semantic segmentation training pairs. Our model outperforms baselines based on pix2pix and CycleGAN models. |
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Salt Lake City; USA; June 2018 |
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CVPR |
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LAMP; 600.109; 600.106; 600.120 |
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no |
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Admin @ si @ WWH2018b |
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3131 |
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Author |
Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
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Title |
Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy |
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Conference Article |
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2018 |
Publication |
OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis |
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11041 |
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Biopsy guiding; Bronchoscopy; Lung biopsy; Intervention guiding; Airway codification |
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Abstract |
Bronchoscopy examinations allow biopsy of pulmonary nodules with minimum risk for the patient. Even for experienced bronchoscopists, it is difficult to guide the bronchoscope to most distal lesions and obtain an accurate diagnosis. This paper presents an image-based codification of the bronchial anatomy for bronchoscopy biopsy guiding. The 3D anatomy of each patient is codified as a binary tree with nodes representing bronchial levels and edges labeled using their position on images projecting the 3D anatomy from a set of branching points. The paths from the root to leaves provide a codification of navigation routes with spatially consistent labels according to the anatomy observes in video bronchoscopy explorations. We evaluate our labeling approach as a guiding system in terms of the number of bronchial levels correctly codified, also in the number of labels-based instructions correctly supplied, using generalized mixed models and computer-generated data. Results obtained for three independent observers prove the consistency and reproducibility of our guiding system. We trust that our codification based on viewer’s projection might be used as a foundation for the navigation process in Virtual Bronchoscopy systems. |
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Granada; September 2018 |
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MICCAIW |
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IAM; 600.096; 600.075; 601.323; 600.145 |
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no |
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Admin @ si @ RSB2018b |
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3137 |
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Author |
Pau Rodriguez; Josep M. Gonfaus; Guillem Cucurull; Xavier Roca; Jordi Gonzalez |
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Title |
Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery |
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Conference Article |
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2018 |
Publication |
15th European Conference on Computer Vision |
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11212 |
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357-372 |
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Deep Learning; Convolutional Neural Networks; Attention |
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We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. It learns to attend to lower-level feature activations without requiring part annotations and uses these activations to update and rectify the output likelihood distribution. In contrast to other approaches, the proposed mechanism is modular, architecture-independent and efficient both in terms of parameters and computation required. Experiments show that networks augmented with our approach systematically improve their classification accuracy and become more robust to clutter. As a result, Wide Residual Networks augmented with our proposal surpasses the state of the art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford dogs, and UEC Food-100. |
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Munich; September 2018 |
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ECCV |
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ISE; 600.098; 602.121; 600.119 |
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no |
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Admin @ si @ RGC2018 |
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3139 |
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Author |
Boris N. Oreshkin; Pau Rodriguez; Alexandre Lacoste |
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TADAM: Task dependent adaptive metric for improved few-shot learning |
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Conference Article |
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2018 |
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32nd Annual Conference on Neural Information Processing Systems |
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Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space. Moreover, we propose and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100. |
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Montreal; Canada; December 2018 |
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NIPS |
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ISE; 600.098; 600.119 |
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no |
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Admin @ si @ ORL2018 |
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3140 |
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Author |
Lluis Gomez; Andres Mafla; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Single Shot Scene Text Retrieval |
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Conference Article |
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2018 |
Publication |
15th European Conference on Computer Vision |
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11218 |
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728-744 |
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Image retrieval; Scene text; Word spotting; Convolutional Neural Networks; Region Proposals Networks; PHOC |
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Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this paper we address the problem of scene text retrieval: given a text query, the system must return all images containing the queried text. The novelty of the proposed model consists in the usage of a single shot CNN architecture that predicts at the same time bounding boxes and a compact text representation of the words in them. In this way, the text based image retrieval task can be casted as a simple nearest neighbor search of the query text representation over the outputs of the CNN over the entire image
database. Our experiments demonstrate that the proposed architecture
outperforms previous state-of-the-art while it offers a significant increase
in processing speed. |
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Munich; September 2018 |
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ECCV |
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DAG; 600.084; 601.338; 600.121; 600.129 |
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no |
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Admin @ si @ GMR2018 |
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3143 |
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Author |
Mohammed Al Rawi; Dimosthenis Karatzas |
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Title |
On the Labeling Correctness in Computer Vision Datasets |
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Conference Article |
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2018 |
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Proceedings of the Workshop on Interactive Adaptive Learning, co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases |
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Image datasets have heavily been used to build computer vision systems.
These datasets are either manually or automatically labeled, which is a
problem as both labeling methods are prone to errors. To investigate this problem, we use a majority voting ensemble that combines the results from several Convolutional Neural Networks (CNNs). Majority voting ensembles not only enhance the overall performance, but can also be used to estimate the confidence level of each sample. We also examined Softmax as another form to estimate posterior probability. We have designed various experiments with a range of different ensembles built from one or different, or temporal/snapshot CNNs, which have been trained multiple times stochastically. We analyzed CIFAR10, CIFAR100, EMNIST, and SVHN datasets and we found quite a few incorrect
labels, both in the training and testing sets. We also present detailed confidence analysis on these datasets and we found that the ensemble is better than the Softmax when used estimate the per-sample confidence. This work thus proposes an approach that can be used to scrutinize and verify the labeling of computer vision datasets, which can later be applied to weakly/semi-supervised learning. We propose a measure, based on the Odds-Ratio, to quantify how many of these incorrectly classified labels are actually incorrectly labeled and how many of these are confusing. The proposed methods are easily scalable to larger datasets, like ImageNet, LSUN and SUN, as each CNN instance is trained for 60 epochs; or even faster, by implementing a temporal (snapshot) ensemble. |
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ECML-PKDDW |
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DAG; 600.121; 600.129 |
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Admin @ si @ RaK2018 |
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3144 |
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Author |
Adrian Galdran; Aitor Alvarez-Gila; Alessandro Bria; Javier Vazquez; Marcelo Bertalmio |
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On the Duality Between Retinex and Image Dehazing |
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2018 |
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31st IEEE Conference on Computer Vision and Pattern Recognition |
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8212–8221 |
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Image color analysis; Task analysis; Atmospheric modeling; Computer vision; Computational modeling; Lighting |
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Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem. |
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Salt Lake City; USA; June 2018 |
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CVPR |
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LAMP; 600.120 |
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Admin @ si @ GAB2018 |
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3146 |
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Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados |
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Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework |
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2018 |
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14th Asian Conference on Computer Vision |
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In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset. |
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Perth; Australia; December 2018 |
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ACCV |
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DAG; 600.097; 600.121; 600.129 |
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Admin @ si @ DDG2018a |
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3151 |
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Author |
Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados; Umapada Pal |
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Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch |
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2018 |
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24th International Conference on Pattern Recognition |
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916 - 921 |
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In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets. |
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Beijing; China; August 2018 |
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DAG; 602.167; 602.168; 600.097; 600.084; 600.121; 600.129 |
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Admin @ si @ DDG2018b |
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3152 |
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