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Akshita Gupta; Sanath Narayan; Salman Khan; Fahad Shahbaz Khan; Ling Shao; Joost Van de Weijer |
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Title |
Generative Multi-Label Zero-Shot Learning |
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Journal Article |
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Year |
2023 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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Volume |
45 |
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12 |
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14611-14624 |
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Generalized zero-shot learning; Multi-label classification; Zero-shot object detection; Feature synthesis |
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Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. The test samples can additionally contain seen categories in the generalized variant. Existing approaches rely on learning either shared or label-specific attention from the seen classes. Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge. In contrast, state-of-the-art single-label generative adversarial network (GAN) based approaches learn to directly synthesize the class-specific visual features from the corresponding class attribute embeddings. However, synthesizing multi-label features from GANs is still unexplored in the context of zero-shot setting. When multiple objects occur jointly in a single image, a critical question is how to effectively fuse multi-class information. In this work, we introduce different fusion approaches at the attribute-level, feature-level and cross-level (across attribute and feature-levels) for synthesizing multi-label features from their corresponding multi-label class embeddings. To the best of our knowledge, our work is the first to tackle the problem of multi-label feature synthesis in the (generalized) zero-shot setting. Our cross-level fusion-based generative approach outperforms the state-of-the-art on three zero-shot benchmarks: NUS-WIDE, Open Images and MS COCO. Furthermore, we show the generalization capabilities of our fusion approach in the zero-shot detection task on MS COCO, achieving favorable performance against existing methods. |
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December 2023 |
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LAMP; PID2021-128178OB-I00 |
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Admin @ si @ |
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3853 |
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Jose Elias Yauri; M. Lagos; H. Vega-Huerta; P. de-la-Cruz; G.L.E Maquen-Niño; E. Condor-Tinoco |
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Title |
Detection of Epileptic Seizures Based-on Channel Fusion and Transformer Network in EEG Recordings |
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Journal Article |
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2023 |
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International Journal of Advanced Computer Science and Applications |
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IJACSA |
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14 |
Issue |
5 |
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1067-1074 |
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Epilepsy; epilepsy detection; EEG; EEG channel fusion; convolutional neural network; self-attention |
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According to the World Health Organization, epilepsy affects more than 50 million people in the world, and specifically, 80% of them live in developing countries. Therefore, epilepsy has become among the major public issue for many governments and deserves to be engaged. Epilepsy is characterized by uncontrollable seizures in the subject due to a sudden abnormal functionality of the brain. Recurrence of epilepsy attacks change people’s lives and interferes with their daily activities. Although epilepsy has no cure, it could be mitigated with an appropriated diagnosis and medication. Usually, epilepsy diagnosis is based on the analysis of an electroencephalogram (EEG) of the patient. However, the process of searching for seizure patterns in a multichannel EEG recording is a visual demanding and time consuming task, even for experienced neurologists. Despite the recent progress in automatic recognition of epilepsy, the multichannel nature of EEG recordings still challenges current methods. In this work, a new method to detect epilepsy in multichannel EEG recordings is proposed. First, the method uses convolutions to perform channel fusion, and next, a self-attention network extracts temporal features to classify between interictal and ictal epilepsy states. The method was validated in the public CHB-MIT dataset using the k-fold cross-validation and achieved 99.74% of specificity and 99.15% of sensitivity, surpassing current approaches. |
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IAM |
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no |
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Admin @ si @ |
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3856 |
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M. Altillawi; S. Li; S.M. Prakhya; Z. Liu; Joan Serrat |
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Title |
Implicit Learning of Scene Geometry From Poses for Global Localization |
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Journal Article |
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2024 |
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IEEE Robotics and Automation Letters |
Abbreviated Journal |
ROBOTAUTOMLET |
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9 |
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2 |
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955-962 |
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Localization; Localization and mapping; Deep learning for visual perception; Visual learning |
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Global visual localization estimates the absolute pose of a camera using a single image, in a previously mapped area. Obtaining the pose from a single image enables many robotics and augmented/virtual reality applications. Inspired by latest advances in deep learning, many existing approaches directly learn and regress 6 DoF pose from an input image. However, these methods do not fully utilize the underlying scene geometry for pose regression. The challenge in monocular relocalization is the minimal availability of supervised training data, which is just the corresponding 6 DoF poses of the images. In this letter, we propose to utilize these minimal available labels (i.e., poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose. We present a learning method that uses these pose labels and rigid alignment to learn two 3D geometric representations ( X, Y, Z coordinates ) of the scene, one in camera coordinate frame and the other in global coordinate frame. Given a single image, it estimates these two 3D scene representations, which are then aligned to estimate a pose that matches the pose label. This formulation allows for the active inclusion of additional learning constraints to minimize 3D alignment errors between the two 3D scene representations, and 2D re-projection errors between the 3D global scene representation and 2D image pixels, resulting in improved localization accuracy. During inference, our model estimates the 3D scene geometry in camera and global frames and aligns them rigidly to obtain pose in real-time. We evaluate our work on three common visual localization datasets, conduct ablation studies, and show that our method exceeds state-of-the-art regression methods' pose accuracy on all datasets. |
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2377-3766 |
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ADAS |
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no |
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Admin @ si @ |
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3857 |
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Filip Szatkowski; Mateusz Pyla; Marcin Przewięzlikowski; Sebastian Cygert; Bartłomiej Twardowski; Tomasz Trzcinski |
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Title |
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-Free Continual Learning |
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Conference Article |
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2023 |
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Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
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3512-3517 |
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In this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting. KD-based methods are successfully used in CIL, but they often struggle to regularize the model without access to exemplars of the training data from previous tasks. Our analysis reveals that this issue originates from substantial representation shifts in the teacher network when dealing with out-of-distribution data. This causes large errors in the KD loss component, leading to performance degradation in CIL. Inspired by recent test-time adaptation methods, we introduce Teacher Adaptation (TA), a method that concurrently updates the teacher and the main model during incremental training. Our method seamlessly integrates with KD-based CIL approaches and allows for consistent enhancement of their performance across multiple exemplar-free CIL benchmarks. |
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Paris; France; October 2023 |
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ICCVW |
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LAMP |
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no |
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Admin @ si @ |
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3944 |
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Author |
J. Martinez |
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Title |
Automotive sector and Machine Vision. |
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Miscellaneous |
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2002 |
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European Integrated Machine Vision, 1: 2, Special Edition: Automative and Glass. |
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Admin @ si @ 37316 |
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270 |
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Oriol Martinez |
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Title |
Semantic Retrieval of Memory Color Content |
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2004 |
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CVC Technical Report #80 |
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CVC (UAB) |
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Admin @ si @ 38047 |
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508 |
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Author |
Carme Julia |
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Title |
Motion segmentation through factorization. Application to night driving assistance |
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Report |
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2004 |
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CVC Technical Report #79 |
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CVC (UAB) |
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Admin @ si @ 38169 |
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506 |
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Maria Alberich-Carramiñ; Guillem Alenya; Juan Andrade; E. Martinez; Carme Torras |
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Title |
Affine Epipolar Direction from Two Views of a Planar Contour |
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2006 |
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Proceedings of the Advanced Concepts for Intelligent Vision Systems Conference, LNCS 4179: 944–955 |
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Antwerp (Belgium) |
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Admin @ si @ AAA2006 |
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661 |
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Reza Azad; Maryam Asadi Aghbolaghi; Mahmood Fathy; Sergio Escalera |
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Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions |
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Conference Article |
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2019 |
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Visual Recognition for Medical Images workshop |
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406-415 |
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In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance. |
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Seul; Korea; October 2019 |
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ICCVW |
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HUPBA; no proj |
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no |
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Admin @ si @ AAF2019 |
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3324 |
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Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera |
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Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation |
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2020 |
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Bioimage computation workshop |
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Virtual; August 2020 |
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ECCVW |
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HUPBA |
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no |
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Admin @ si @ AAF2020 |
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3520 |
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Reza Azad; Maryam Asadi-Aghbolaghi; Shohreh Kasaei; Sergio Escalera |
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Title |
Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps |
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Journal Article |
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2019 |
Publication |
IEEE Transactions on Circuits and Systems for Video Technology |
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TCSVT |
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29 |
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6 |
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1729-1740 |
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Hand gesture recognition; Multilevel temporal sampling; Weighted depth motion map; Spatio-temporal description; VLAD encoding |
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Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient (HOG) and local binary pattern (LBP) are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D hand gesture recognition. We also achieve competitive results on NTU action dataset. |
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June 2019, |
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HUPBA; no proj |
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Admin @ si @ AAK2018 |
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3213 |
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Cristhian A. Aguilera-Carrasco; Cristhian Aguilera; Cristobal A. Navarro; Angel Sappa |
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Fast CNN Stereo Depth Estimation through Embedded GPU Devices |
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2020 |
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Sensors |
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SENS |
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20 |
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11 |
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3249 |
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stereo matching; deep learning; embedded GPU |
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Current CNN-based stereo depth estimation models can barely run under real-time constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art evaluations usually do not consider model optimization techniques, being that it is unknown what is the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models on three different embedded GPU devices, with and without optimization methods, presenting performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically augmenting the runtime speed of current models. In our experiments, we achieve real-time inference speed, in the range of 5–32 ms, for 1216 × 368 input stereo images on the Jetson TX2, Jetson Xavier, and Jetson Nano embedded devices. |
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MSIAU; 600.122 |
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Admin @ si @ AAN2020 |
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3428 |
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Cristhian A. Aguilera-Carrasco; C. Aguilera; Angel Sappa |
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Melamine Faced Panels Defect Classification beyond the Visible Spectrum |
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2018 |
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Sensors |
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SENS |
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18 |
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11 |
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1-10 |
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industrial application; infrared; machine learning |
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In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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MSIAU; 600.122 |
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Admin @ si @ AAS2018 |
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3191 |
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Author |
Reza Azad; Afshin Bozorgpour; Maryam Asadi-Aghbolaghi; Dorit Merhof; Sergio Escalera |
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Title |
Deep Frequency Re-Calibration U-Net for Medical Image Segmentation |
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Conference Article |
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2021 |
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IEEE/CVF International Conference on Computer Vision Workshops |
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3274-3283 |
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We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. |
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VIRTUAL; October 2021 |
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ICCVW |
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HUPBA; no proj |
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no |
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Admin @ si @ ABA2021 |
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3645 |
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Author |
Joan Arnedo-Moreno; D. Bañeres; Xavier Baro; S. Caballe; S. Guerrero; L. Porta; J. Prieto |
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Title |
Va-ID: A trust-based virtual assessment system |
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Conference Article |
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2014 |
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6th International Conference on Intelligent Networking and Collaborative Systems |
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328 - 335 |
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Even though online education is a very important pillar of lifelong education, institutions are still reluctant to wager for a fully online educational model. At the end, they keep relying on on-site assessment systems, mainly because fully virtual alternatives do not have the deserved social recognition or credibility. Thus, the design of virtual assessment systems that are able to provide effective proof of student authenticity and authorship and the integrity of the activities in a scalable and cost efficient manner would be very helpful. This paper presents ValID, a virtual assessment approach based on a continuous trust level evaluation between students and the institution. The current trust level serves as the main mechanism to dynamically decide which kind of controls a given student should be subjected to, across different courses in a degree. The main goal is providing a fair trade-off between security, scalability and cost, while maintaining the perceived quality of the educational model. |
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Salerna; Italy; September 2014 |
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978-1-4799-6386-7 |
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INCOS |
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Notes |
OR; HuPBA;MV |
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no |
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Admin @ si @ ABB2014 |
Serial |
2620 |
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