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Albin Soutif; Antonio Carta; Joost Van de Weijer |
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Title |
Improving Online Continual Learning Performance and Stability with Temporal Ensembles |
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2023 |
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2nd Conference on Lifelong Learning Agents |
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Neural networks are very effective when trained on large datasets for a large number of iterations. However, when they are trained on non-stationary streams of data and in an online fashion, their performance is reduced (1) by the online setup, which limits the availability of data, (2) due to catastrophic forgetting because of the non-stationary nature of the data. Furthermore, several recent works (Caccia et al., 2022; Lange et al., 2023) arXiv:2205.13452 showed that replay methods used in continual learning suffer from the stability gap, encountered when evaluating the model continually (rather than only on task boundaries). In this article, we study the effect of model ensembling as a way to improve performance and stability in online continual learning. We notice that naively ensembling models coming from a variety of training tasks increases the performance in online continual learning considerably. Starting from this observation, and drawing inspirations from semi-supervised learning ensembling methods, we use a lightweight temporal ensemble that computes the exponential moving average of the weights (EMA) at test time, and show that it can drastically increase the performance and stability when used in combination with several methods from the literature. |
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Montreal; Canada; August 2023 |
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COLLAS |
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LAMP |
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Admin @ si @ SCW2023 |
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3922 |
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B. Zhou; Agata Lapedriza; J. Xiao; A. Torralba; A. Oliva |
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Learning Deep Features for Scene Recognition using Places Database |
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2014 |
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28th Annual Conference on Neural Information Processing Systems |
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487-495 |
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Montreal; Canada; December 2014 |
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OR;MV |
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Admin @ si @ ZLX2014 |
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2621 |
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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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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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ISE; 600.098; 600.119 |
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Admin @ si @ ORL2018 |
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3140 |
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Abel Gonzalez-Garcia; Joost Van de Weijer; Yoshua Bengio |
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Image-to-image translation for cross-domain disentanglement |
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2018 |
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32nd Annual Conference on Neural Information Processing Systems |
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Montreal; Canada; December 2018 |
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LAMP; 600.120 |
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Admin @ si @ GWB2018 |
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3155 |
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Chenshen Wu; Luis Herranz; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu |
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Title |
Memory Replay GANs: Learning to Generate New Categories without Forgetting |
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2018 |
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32nd Annual Conference on Neural Information Processing Systems |
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5966-5976 |
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Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion. We first show that sequential fine tuning renders the network unable to properly generate images from previous categories (ie forgetting). Addressing this problem, we propose Memory Replay GANs (MeRGANs), a conditional GAN framework that integrates a memory replay generator. We study two methods to prevent forgetting by leveraging these replays, namely joint training with replay and replay alignment. Qualitative and quantitative experimental results in MNIST, SVHN and LSUN datasets show that our memory replay approach can generate competitive images while significantly mitigating the forgetting of previous categories. |
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Montreal; Canada; December 2018 |
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LAMP; 600.106; 600.109; 602.200; 600.120 |
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Admin @ si @ WHL2018 |
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3249 |
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Author |
Fernando Vilariño |
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Library Living Lab, Numérisation 3D des chapiteaux du cloître de Saint-Cugat : des citoyens co- créant le nouveau patrimoine culturel numérique |
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2019 |
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Intersectorialité et approche Living Labs. Entretiens Jacques-Cartier |
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Montreal; Canada; December 2019 |
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MV; DAG; 600.140; 600.121;SIAI |
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Admin @ si @ Vil2019a |
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3457 |
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Author |
Jiaolong Xu; Peng Wang; Heng Yang; Antonio Lopez |
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Title |
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving |
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Conference Article |
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2019 |
Publication |
IEEE International Conference on Robotics and Automation |
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2379-2384 |
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Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent deep convolutional neural network based object detectors have achieved state-of-the-art accuracy. However, such models are trained with numerous parameters and their high computational costs and large storage prohibit the deployment to memory and computation resource limited systems. Low-precision neural networks are popular techniques for reducing the computation requirements and memory footprint. Among them, binary weight neural network (BWN) is the extreme case which quantizes the float-point into just bit. BWNs are difficult to train and suffer from accuracy deprecation due to the extreme low-bit representation. To address this problem, we propose a knowledge transfer (KT) method to aid the training of BWN using a full-precision teacher network. We built DarkNet-and MobileNet-based binary weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car, pedestrian and cyclist detection. The experimental results show that the proposed method maintains high detection accuracy while reducing the model size of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB. |
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Montreal; Canada; May 2019 |
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ICRA |
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ADAS; 600.124; 600.116; 600.118 |
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Admin @ si @ XWY2018 |
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3182 |
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Author |
Xavier Soria; Angel Sappa; Arash Akbarinia |
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Multispectral Single-Sensor RGB-NIR Imaging: New Challenges and Opportunities |
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Conference Article |
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2017 |
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7th International Conference on Image Processing Theory, Tools & Applications |
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Color restoration; Neural networks; Singlesensor cameras; Multispectral images; RGB-NIR dataset |
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Multispectral images captured with a single sensor camera have become an attractive alternative for numerous computer vision applications. However, in order to fully exploit their potentials, the color restoration problem (RGB representation) should be addressed. This problem is more evident in outdoor scenarios containing vegetation, living beings, or specular materials. The problem of color distortion emerges from the sensitivity of sensors due to the overlap of visible and near infrared spectral bands. This paper empirically evaluates the variability of the near infrared (NIR) information with respect to the changes of light throughout the day. A tiny neural network is proposed to restore the RGB color representation from the given RGBN (Red, Green, Blue, NIR) images. In order to evaluate the proposed algorithm, different experiments on a RGBN outdoor dataset are conducted, which include various challenging cases. The obtained result shows the challenge and the importance of addressing color restoration in single sensor multispectral images. |
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Montreal; Canada; November 2017 |
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IPTA |
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NEUROBIT; MSIAU; 600.122 |
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Admin @ si @ SSA2017 |
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3074 |
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Carlos Boned Riera; Oriol Ramos Terrades |
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Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph |
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2022 |
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26th International Conference on Pattern Recognition |
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2186-2191 |
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Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition |
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Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks. |
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Montreal; Quebec; Canada; August 2022 |
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ICPR |
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DAG; 600.121; 600.162 |
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Admin @ si @ BoR2022 |
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3741 |
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Vacit Oguz Yazici; Joost Van de Weijer; Longlong Yu |
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Visual Transformers with Primal Object Queries for Multi-Label Image Classification |
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2022 |
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26th International Conference on Pattern Recognition |
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Multi-label image classification is about predicting a set of class labels that can be considered as orderless sequential data. Transformers process the sequential data as a whole, therefore they are inherently good at set prediction. The first vision-based transformer model, which was proposed for the object detection task introduced the concept of object queries. Object queries are learnable positional encodings that are used by attention modules in decoder layers to decode the object classes or bounding boxes using the region of interests in an image. However, inputting the same set of object queries to different decoder layers hinders the training: it results in lower performance and delays convergence. In this paper, we propose the usage of primal object queries that are only provided at the start of the transformer decoder stack. In addition, we improve the mixup technique proposed for multi-label classification. The proposed transformer model with primal object queries improves the state-of-the-art class wise F1 metric by 2.1% and 1.8%; and speeds up the convergence by 79.0% and 38.6% on MS-COCO and NUS-WIDE datasets respectively. |
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Montreal; Quebec; Canada; August 2022 |
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LAMP; 600.147; 601.309 |
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Admin @ si @ YWY2022 |
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3786 |
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Ayan Banerjee; Palaiahnakote Shivakumara; Parikshit Acharya; Umapada Pal; Josep Llados |
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TWD: A New Deep E2E Model for Text Watermark Detection in Video Images |
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2022 |
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26th International Conference on Pattern Recognition |
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Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection |
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Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multi-type text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge |
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Montreal; Quebec; Canada; August 2022 |
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DAG; |
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Admin @ si @ BSA2022 |
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3788 |
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Mikhail Mozerov; Ariel Amato; Xavier Roca |
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Occlusion Handling in Trinocular Stereo using Composite Disparity Space Image |
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2009 |
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19th International Conference on Computer Graphics and Vision |
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69–73 |
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In this paper we propose a method that smartly improves occlusion handling in stereo matching using trinocular stereo. The main idea is based on the assumption that any occluded region in a matched stereo pair (middle-left images) in general is not occluded in the opposite matched pair (middle-right images). Then two disparity space images (DSI) can be merged in one composite DSI. The proposed integration differs from the known approach that uses a cumulative cost. A dense disparity map is obtained with a global optimization algorithm using the proposed composite DSI. The experimental results are evaluated on the Middlebury data set, showing high performance of the proposed algorithm especially in the occluded regions. One of the top positions in the rank of the Middlebury website confirms the performance of our method to be competitive with the best stereo matching. |
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Moscow (Russia) |
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978-5-317-02975-3 |
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GRAPHICON |
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ISE |
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ISE @ ise @ MAR2009b |
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1207 |
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Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun |
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CARLA: An Open Urban Driving Simulator |
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Conference Article |
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2017 |
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1st Annual Conference on Robot Learning. Proceedings of Machine Learning |
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78 |
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1-16 |
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Autonomous driving; sensorimotor control; simulation |
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We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an endto-end
model trained via imitation learning, and an end-to-end model trained via
reinforcement learning. The approaches are evaluated in controlled scenarios of
increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research. |
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Mountain View; CA; USA; November 2017 |
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ADAS; 600.085; 600.118 |
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Admin @ si @ DRC2017 |
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2988 |
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Javier Varona; Juan J. Villanueva |
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NeuroFilters: Neural Networks for image Processing. |
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1997 |
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Vision Systems: New image Processing Techniques and Applications Algorithms, Methods, and Components. Proceedings of the SPIE. |
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ISE @ ise @ VaV1997a |
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207 |
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Oriol Pujol; Oriol Rodriguez-Leor; J. Mauri; E. Fernandez; V. Valle; Petia Radeva |
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Automatic segmentation and characterization of IVUS images by texture analysis |
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2004 |
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European Society of Cardiology Congress 2004 |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ PRM2004 |
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478 |
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