|
Albert Gordo, Florent Perronnin, & Ernest Valveny. (2012). Document classification using multiple views. In 10th IAPR International Workshop on Document Analysis Systems (pp. 33–37). IEEE Computer Society Washington.
Abstract: The combination of multiple features or views when representing documents or other kinds of objects usually leads to improved results in classification (and retrieval) tasks. Most systems assume that those views will be available both at training and test time. However, some views may be too `expensive' to be available at test time. In this paper, we consider the use of Canonical Correlation Analysis to leverage `expensive' views that are available only at training time. Experimental results show that this information may significantly improve the results in a classification task.
|
|
|
Alicia Fornes, Josep Llados, Gemma Sanchez, & Horst Bunke. (2012). Writer Identification in Old Handwritten Music Scores. In Copnstantin Papaodysseus (Ed.), Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology (pp. 27–63). IGI-Global.
Abstract: The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores. Even though an important amount of compositions contains handwritten text in the music scores, the aim of our work is to use only music notation to determine the author. The steps of the system proposed are the following. First of all, the music sheet is preprocessed and normalized for obtaining a single binarized music line, without the staff lines. Afterwards, 100 features are extracted for every music line, which are subsequently used in a k-NN classifier that compares every feature vector with prototypes stored in a database. By applying feature selection and extraction methods on the original feature set, the performance is increased. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving a recognition rate of about 95%.
|
|
|
Alex Gomez-Villa, Bartlomiej Twardowski, Lu Yu, Andrew Bagdanov, & Joost Van de Weijer. (2022). Continually Learning Self-Supervised Representations With Projected Functional Regularization. In CVPR 2022 Workshop on Continual Learning (CLVision, 3rd Edition) (pp. 3866–3876).
Abstract: Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches. However, these methods are unable to acquire new knowledge incrementally – they are, in fact, mostly used only as a pre-training phase over IID data. In this work we investigate self-supervised methods in continual learning regimes without any replay
mechanism. We show that naive functional regularization,also known as feature distillation, leads to lower plasticity and limits continual learning performance. Instead, we propose Projected Functional Regularization in which a separate temporal projection network ensures that the newly learned feature space preserves information of the previous one, while at the same time allowing for the learning of new features. This prevents forgetting while maintaining the plasticity of the learner. Comparison with other incremental learning approaches applied to self-supervision demonstrates that our method obtains competitive performance in
different scenarios and on multiple datasets.
Keywords: Computer vision; Conferences; Self-supervised learning; Image representation; Pattern recognition
|
|
|
Kai Wang, Xialei Liu, Andrew Bagdanov, Luis Herranz, Shangling Jui, & Joost Van de Weijer. (2022). Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition. In CVPR 2022 Workshop on Continual Learning (CLVision, 3rd Edition) (pp. 3728–3738).
Abstract: In this paper we consider the problem of incremental meta-learning in which classes are presented incrementally in discrete tasks. We propose Episodic Replay Distillation (ERD), that mixes classes from the current task with exemplars from previous tasks when sampling episodes for meta-learning. To allow the training to benefit from a large as possible variety of classes, which leads to more gener-
alizable feature representations, we propose the cross-task meta loss. Furthermore, we propose episodic replay distillation that also exploits exemplars for improved knowledge distillation. Experiments on four datasets demonstrate that ERD surpasses the state-of-the-art. In particular, on the more challenging one-shot, long task sequence scenarios, we reduce the gap between Incremental Meta-Learning and
the joint-training upper bound from 3.5% / 10.1% / 13.4% / 11.7% with the current state-of-the-art to 2.6% / 2.9% / 5.0% / 0.2% with our method on Tiered-ImageNet / Mini-ImageNet / CIFAR100 / CUB, respectively.
Keywords: Training; Computer vision; Image recognition; Upper bound; Conferences; Pattern recognition; Task analysis
|
|
|
Bojana Gajic, Ariel Amato, Ramon Baldrich, Joost Van de Weijer, & Carlo Gatta. (2022). Area Under the ROC Curve Maximization for Metric Learning. In CVPR 2022 Workshop on Efficien Deep Learning for Computer Vision (ECV 2022, 5th Edition).
Abstract: Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification.
Keywords: Training; Computer vision; Conferences; Area measurement; Benchmark testing; Pattern recognition
|
|
|
Mohamed Ramzy Ibrahim, Robert Benavente, Felipe Lumbreras, & Daniel Ponsa. (2022). 3DRRDB: Super Resolution of Multiple Remote Sensing Images using 3D Residual in Residual Dense Blocks. In CVPR 2022 Workshop on IEEE Perception Beyond the Visible Spectrum workshop series (PBVS, 18th Edition).
Abstract: The rapid advancement of Deep Convolutional Neural Networks helped in solving many remote sensing problems, especially the problems of super-resolution. However, most state-of-the-art methods focus more on Single Image Super-Resolution neglecting Multi-Image Super-Resolution. In this work, a new proposed 3D Residual in Residual Dense Blocks model (3DRRDB) focuses on remote sensing Multi-Image Super-Resolution for two different single spectral bands. The proposed 3DRRDB model explores the idea of 3D convolution layers in deeply connected Dense Blocks and the effect of local and global residual connections with residual scaling in Multi-Image Super-Resolution. The model tested on the Proba-V challenge dataset shows a significant improvement above the current state-of-the-art models scoring a Corrected Peak Signal to Noise Ratio (cPSNR) of 48.79 dB and 50.83 dB for Near Infrared (NIR) and RED Bands respectively. Moreover, the proposed 3DRRDB model scores a Corrected Structural Similarity Index Measure (cSSIM) of 0.9865 and 0.9909 for NIR and RED bands respectively.
Keywords: Training; Solid modeling; Three-dimensional displays; PSNR; Convolution; Superresolution; Pattern recognition
|
|
|
Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L. Hayes, et al. (2021). Avalanche: an End-to-End Library for Continual Learning. In 34th IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 3595–3605).
Abstract: Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms.
|
|
|
Marc Masana, Tinne Tuytelaars, & Joost Van de Weijer. (2021). Ternary Feature Masks: zero-forgetting for task-incremental learning. In 34th IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 3565–3574).
Abstract: We propose an approach without any forgetting to continual learning for the task-aware regime, where at inference the task-label is known. By using ternary masks we can upgrade a model to new tasks, reusing knowledge from previous tasks while not forgetting anything about them. Using masks prevents both catastrophic forgetting and backward transfer. We argue -- and show experimentally -- that avoiding the former largely compensates for the lack of the latter, which is rarely observed in practice. In contrast to earlier works, our masks are applied to the features (activations) of each layer instead of the weights. This considerably reduces the number of mask parameters for each new task; with more than three orders of magnitude for most networks. The encoding of the ternary masks into two bits per feature creates very little overhead to the network, avoiding scalability issues. To allow already learned features to adapt to the current task without changing the behavior of these features for previous tasks, we introduce task-specific feature normalization. Extensive experiments on several finegrained datasets and ImageNet show that our method outperforms current state-of-the-art while reducing memory overhead in comparison to weight-based approaches.
|
|
|
Razieh Rastgoo, Kourosh Kiani, Sergio Escalera, & Mohammad Sabokrou. (2021). Sign Language Production: A Review. In Conference on Computer Vision and Pattern Recognition Workshops (pp. 3472–3481).
Abstract: Sign Language is the dominant yet non-primary form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundamental. To this end, sign language recognition and production are two necessary parts for making such a two-way system. Sign language recognition and production need to cope with some critical challenges. In this survey, we review recent advances in Sign Language Production (SLP) and related areas using deep learning. This survey aims to briefly summarize recent achievements in SLP, discussing their advantages, limitations, and future directions of research.
|
|
|
Sudeep Katakol, Luis Herranz, Fei Yang, & Marta Mrak. (2021). DANICE: Domain adaptation without forgetting in neural image compression. In Conference on Computer Vision and Pattern Recognition Workshops (pp. 1921–1925).
Abstract: Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility with previously encoded bitstreams. Addressing these problems, we propose Codec Adaptation without Forgetting (CAwF), a framework that can avoid these problems by adding a small amount of custom parameters, where the source codec remains embedded and unchanged during the adaptation process. Experiments demonstrate its effectiveness and provide useful insights on the characteristics of catastrophic interference in NIC.
|
|
|
Albert Clapes, Ozan Bilici, Dariia Temirova, Egils Avots, Gholamreza Anbarjafari, & Sergio Escalera. (2018). From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 2373–2382).
|
|
|
Dena Bazazian, Dimosthenis Karatzas, & Andrew Bagdanov. (2018). Word Spotting in Scene Images based on Character Recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 1872–1874).
Abstract: In this paper we address the problem of unconstrained Word Spotting in scene images. We train a Fully Convolutional Network to produce heatmaps of all the character classes. Then, we employ the Text Proposals approach and, via a rectangle classifier, detect the most likely rectangle for each query word based on the character attribute maps. We evaluate the proposed method on ICDAR2015 and show that it is capable of identifying and recognizing query words in natural scene images.
|
|
|
Bojana Gajic, & Ramon Baldrich. (2018). Cross-domain fashion image retrieval. In CVPR 2018 Workshop on Women in Computer Vision (WiCV 2018, 4th Edition) (pp. 19500–19502).
Abstract: Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image of a fashion item taken by users, to the images of the same item taken in controlled condition, usually by professional photographer. When facing this problem, we have different products
in train and test time, and we use triplet loss to train the network. We stress the importance of proper training of simple architecture, as well as adapting general models to the specific task.
|
|
|
Ilke Demir, Dena Bazazian, Adriana Romero, Viktoriia Sharmanska, & Lyne P. Tchapmi. (2018). WiCV 2018: The Fourth Women In Computer Vision Workshop. In 4th Women in Computer Vision Workshop (pp. 1941–19412).
Abstract: We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.
Keywords: Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning
|
|
|
Patricia Suarez, Angel Sappa, Boris X. Vintimilla, & Riad I. Hammoud. (2018). Deep Learning based Single Image Dehazing. In 31st IEEE Conference on Computer Vision and Pattern Recognition Workhsop (pp. 1250–12507).
Abstract: This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear
images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images.
Keywords: Gallium nitride; Atmospheric modeling; Generators; Generative adversarial networks; Convergence; Image color analysis
|
|