no code implementations • NAACL (TeachingNLP) 2021 • Rajkumar Saini, György Kovács, Mohamadreza Faridghasemnia, Hamam Mokayed, Oluwatosin Adewumi, Pedro Alonso, Sumit Rakesh, Marcus Liwicki
The ongoing COVID-19 pandemic has brought online education to the forefront of pedagogical discussions.
1 code implementation • 5 Jun 2024 • Richa Upadhyay, Ronald Phlypo, Rajkumar Saini, Marcus Liwicki
A detailed performance analysis of the LOMT models, in contrast to the conventional MTL models, reveals that the LOMT models outperform for most task combinations.
no code implementations • 1 Apr 2024 • Prakash Chandra Chhipa, Kanjar De, Meenakshi Subhash Chippa, Rajkumar Saini, Marcus Liwicki
The challenge of Out-Of-Distribution (OOD) robustness remains a critical hurdle towards deploying deep vision models.
no code implementations • 7 Mar 2024 • Prakash Chandra Chhipa, Meenakshi Subhash Chippa, Kanjar De, Rajkumar Saini, Marcus Liwicki, Mubarak Shah
In this work, we propose mitigating perspective distortion (MPD) by employing a fine-grained parameter control on a specific family of M\"obius transform to model real-world distortion without estimating camera intrinsic and extrinsic parameters and without the need for actual distorted data.
1 code implementation • 23 Aug 2023 • Richa Upadhyay, Ronald Phlypo, Rajkumar Saini, Marcus Liwicki
In this work, we introduce channel-wise l1/l2 group sparsity in the shared convolutional layers parameters (or weights) of the multi-task learning model.
no code implementations • 10 Aug 2023 • Rickard Brännvall, Henrik Forsgren, Fredrik Sandin, Marcus Liwicki
It is demonstrated that the novel gating mechanism can capture long-term dependencies for a standard synthetic sequence learning task while significantly reducing computational costs such that execution time is reduced by half on CPU and by one-third under encryption.
1 code implementation • 31 Jul 2023 • Prakash Chandra Chhipa, Johan Rodahl Holmgren, Kanjar De, Rajkumar Saini, Marcus Liwicki
Detailed experiments have been conducted to study the robustness of self-supervised learning methods on distribution shifts and image corruptions.
no code implementations • 23 Jun 2023 • Tahira Shehzadi, Khurram Azeem Hashmi, Didier Stricker, Marcus Liwicki, Muhammad Zeshan Afzal
Upon integrating query modifications in the DETR, we outperform prior works and achieve new state-of-the-art results with the mAP of 96. 9\%, 95. 7\% and 99. 3\% on TableBank, PubLaynet, PubTables, respectively.
Ranked #3 on Document Layout Analysis on PubLayNet val
no code implementations • 19 Jun 2023 • Holly Wilson, Scott Wellington, Foteini Simistira Liwicki, Vibha Gupta, Rajkumar Saini, Kanjar De, Nosheen Abid, Sumit Rakesh, Johan Eriksson, Oliver Watts, Xi Chen, Mohammad Golbabaee, Michael J. Proulx, Marcus Liwicki, Eamonn O'Neill, Benjamin Metcalfe
Decoding inner speech from the brain signal via hybridisation of fMRI and EEG data is explored to investigate the performance benefits over unimodal models.
no code implementations • 4 May 2023 • Tahira Shehzadi, Khurram Azeem Hashmi, Didier Stricker, Marcus Liwicki, Muhammad Zeshan Afzal
Table detection is the task of classifying and localizing table objects within document images.
no code implementations • 27 Apr 2023 • Hamam Mokayed, Palaiahnakote Shivakumara, Lama Alkhaled, Rajkumar Saini, Muhammad Zeshan Afzal, Yan Chai Hum, Marcus Liwicki
Vehicle detection in real-time scenarios is challenging because of the time constraints and the presence of multiple types of vehicles with different speeds, shapes, structures, etc.
no code implementations • 25 Apr 2023 • Sana Sabah Al-Azzawi, György Kovács, Filip Nilsson, Tosin Adewumi, Marcus Liwicki
In this paper, we propose a methodology for task 10 of SemEval23, focusing on detecting and classifying online sexism in social media posts.
1 code implementation • 20 Apr 2023 • Ekta Gupta, Varun Gupta, Muskaan Chopra, Prakash Chandra Chhipa, Marcus Liwicki
Most of the existing DR image classification methods are based on supervised learning which requires a lot of time-consuming and expensive medical domain experts-annotated data for training.
1 code implementation • 19 Apr 2023 • Muskaan Chopra, Prakash Chandra Chhipa, Gopal Mengi, Varun Gupta, Marcus Liwicki
The proposed approach investigates the knowledge transfer of selfsupervised representations across the distinct source and target data distributions in depth in the remote sensing data domain.
1 code implementation • 5 Apr 2023 • Gustav Grund Pihlgren, Fredrik Sandin, Marcus Liwicki
The concept of image similarity is ambiguous, and images can be similar in one context and not in another.
1 code implementation • 29 Mar 2023 • Konstantina Nikolaidou, George Retsinas, Vincent Christlein, Mathias Seuret, Giorgos Sfikas, Elisa Barney Smith, Hamam Mokayed, Marcus Liwicki
Our proposed method is able to generate realistic word image samples from different writer styles, by using class index styles and text content prompts without the need of adversarial training, writer recognition, or text recognition.
Ranked #1 on HTR on IAM
1 code implementation • 12 Mar 2023 • Prakash Chandra Chhipa, Muskaan Chopra, Gopal Mengi, Varun Gupta, Richa Upadhyay, Meenakshi Subhash Chippa, Kanjar De, Rajkumar Saini, Seiichi Uchida, Marcus Liwicki
This work investigates the unexplored usability of self-supervised representation learning in the direction of functional knowledge transfer.
1 code implementation • 4 Mar 2023 • Peyman Hosseini, Mehran Hosseini, Sana Sabah Al-Azzawi, Marcus Liwicki, Ignacio Castro, Matthew Purver
We study the influence of different activation functions in the output layer of deep neural network models for soft and hard label prediction in the learning with disagreement task.
1 code implementation • 8 Feb 2023 • Gustav Grund Pihlgren, Konstantina Nikolaidou, Prakash Chandra Chhipa, Nosheen Abid, Rajkumar Saini, Fredrik Sandin, Marcus Liwicki
Deep perceptual loss is a type of loss function in computer vision that aims to mimic human perception by using the deep features extracted from neural networks.
2 code implementations • 28 Jan 2023 • Tosin Adewumi, Isabella Södergren, Lama Alkhaled, Sana Sabah Sabry, Foteini Liwicki, Marcus Liwicki
Hence, we also contribute a new, large Swedish bias-labelled dataset (of 2 million samples), translated from the English version and train the SotA mT5 model on it.
1 code implementation • 18 Oct 2022 • Prakash Chandra Chhipa, Richa Upadhyay, Rajkumar Saini, Lars Lindqvist, Richard Nordenskjold, Seiichi Uchida, Marcus Liwicki
This work presents a novel self-supervised representation learning method to learn efficient representations without labels on images from a 3DPM sensor (3-Dimensional Particle Measurement; estimates the particle size distribution of material) utilizing RGB images and depth maps of mining material on the conveyor belt.
1 code implementation • 13 Oct 2022 • Richa Upadhyay, Prakash Chandra Chhipa, Ronald Phlypo, Rajkumar Saini, Marcus Liwicki
In particular, it focuses simultaneous learning of multiple tasks, an element of MTL and promptly adapting to new tasks, a quality of meta learning.
Ranked #99 on Semantic Segmentation on NYU Depth v2
no code implementations • 11 Oct 2022 • Tosin Adewumi, Sana Sabah Sabry, Nosheen Abid, Foteini Liwicki, Marcus Liwicki
Our motivation is to determine which of the recent SoTA models is best for automatic hate speech detection and what advantage methods like data augmentation and ensemble may have on the best model, if any.
1 code implementation • 6 Jul 2022 • Oskar Sjögren, Gustav Grund Pihlgren, Fredrik Sandin, Marcus Liwicki
This work investigates the most common DPS metric, where deep features are compared by spatial position, along with metrics comparing the averaged and sorted deep features.
no code implementations • 7 May 2022 • Tosin Adewumi, Foteini Liwicki, Marcus Liwicki
We experiment with three instances of the SoTA dialogue model, Dialogue Generative Pre-trained Transformer (DialoGPT), for conversation generation.
no code implementations • 5 May 2022 • Foteini Simistira Liwicki, Richa Upadhyay, Prakash Chandra Chhipa, Killian Murphy, Federico Visi, Stefan Östersjö, Marcus Liwicki
While this idea was proposed in a previous study, this paper introduces several novelties: (i) Presents a novel method to overcome the class imbalance challenge and make learning possible for co-existent gestures by batch balancing approach and spatial-temporal representations of gestures.
no code implementations • 2 May 2022 • Tosin Adewumi, Foteini Liwicki, Marcus Liwicki
Results of the survey show that progress has been made with recent SoTA conversational AI, but there are still persistent challenges that need to be solved, and the female gender is more common than the male for conversational AI.
1 code implementation • 28 Apr 2022 • Danish Nazir, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Depth completion involves recovering a dense depth map from a sparse map and an RGB image.
Ranked #1 on Depth Completion on KITTI Depth Completion
no code implementations • 17 Apr 2022 • Tosin Adewumi, Mofetoluwa Adeyemi, Aremu Anuoluwapo, Bukola Peters, Happy Buzaaba, Oyerinde Samuel, Amina Mardiyyah Rufai, Benjamin Ajibade, Tajudeen Gwadabe, Mory Moussou Koulibaly Traore, Tunde Ajayi, Shamsuddeen Muhammad, Ahmed Baruwa, Paul Owoicho, Tolulope Ogunremi, Phylis Ngigi, Orevaoghene Ahia, Ruqayya Nasir, Foteini Liwicki, Marcus Liwicki
The language with the most transferable properties is the Nigerian Pidgin English, with a human-likeness score of 78. 1%, of which 34. 4% are unanimous.
no code implementations • SemEval (NAACL) 2022 • Tosin Adewumi, Lama Alkhaled, Hamam Mokayed, Foteini Liwicki, Marcus Liwicki
This paper describes the system used by the Machine Learning Group of LTU in subtask 1 of the SemEval-2022 Task 4: Patronizing and Condescending Language (PCL) Detection.
no code implementations • 16 Mar 2022 • Konstantina Nikolaidou, Mathias Seuret, Hamam Mokayed, Marcus Liwicki
However, because of the very large variety of the actual data (e. g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task.
1 code implementation • 15 Mar 2022 • Prakash Chandra Chhipa, Richa Upadhyay, Gustav Grund Pihlgren, Rajkumar Saini, Seiichi Uchida, Marcus Liwicki
This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors.
Ranked #1 on Breast Cancer Histology Image Classification on BreakHis (Accuracy (Inter-Patient) metric)
Breast Cancer Histology Image Classification (20% labels) Classification Of Breast Cancer Histology Images +2
no code implementations • 11 Feb 2022 • Sana Sabah Sabry, Tosin Adewumi, Nosheen Abid, György Kovacs, Foteini Liwicki, Marcus Liwicki
We investigate the performance of a state-of-the art (SoTA) architecture T5 (available on the SuperGLUE) and compare with it 3 other previous SoTA architectures across 5 different tasks from 2 relatively diverse datasets.
no code implementations • 11 Dec 2021 • Karl Löwenmark, Cees Taal, Stephan Schnabel, Marcus Liwicki, Fredrik Sandin
In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainability, and workplace safety.
no code implementations • 23 Nov 2021 • Richa Upadhyay, Ronald Phlypo, Rajkumar Saini, Marcus Liwicki
However, this article reviews research studies that combine (two of) these learning algorithms.
no code implementations • 12 Oct 2021 • Tosin Adewumi, Rickard Brännvall, Nosheen Abid, Maryam Pahlavan, Sana Sabah Sabry, Foteini Liwicki, Marcus Liwicki
Perplexity score (an automated intrinsic language model metric) and surveys by human evaluation were used to assess the performances of the fine-tuned models, with results that indicate that the capacity for transfer learning can be exploited with considerable success.
no code implementations • 29 Apr 2021 • Khurram Azeem Hashmi, Marcus Liwicki, Didier Stricker, Muhammad Adnan Afzal, Muhammad Ahtsham Afzal, Muhammad Zeshan Afzal
Table understanding has substantially benefited from the recent breakthroughs in deep neural networks.
2 code implementations • LREC 2022 • Tosin P. Adewumi, Roshanak Vadoodi, Aparajita Tripathy, Konstantina Nikolaidou, Foteini Liwicki, Marcus Liwicki
The challenges with NLP systems with regards to tasks such as Machine Translation (MT), word sense disambiguation (WSD) and information retrieval make it imperative to have a labelled idioms dataset with classes such as it is in this work.
no code implementations • 21 Apr 2021 • Khurram Azeem Hashmi, Didier Stricker, Marcus Liwicki, Muhammad Noman Afzal, Muhammad Zeshan Afzal
Subsequently, these anchors are exploited to locate the rows and columns in tabular images.
1 code implementation • 15 Nov 2020 • Tosin P. Adewumi, Foteini Liwicki, Marcus Liwicki
The major contributions of this work include the empirical establishment of a better performance for Yoruba embeddings from undiacritized (normalized) dataset and provision of new analogy sets for evaluation.
no code implementations • 6 Nov 2020 • Tosin P. Adewumi, Foteini Liwicki, Marcus Liwicki
In this work, we show that the difference in performance of embeddings from differently sourced data for a given language can be due to other factors besides data size.
1 code implementation • 23 Jul 2020 • Tosin P. Adewumi, Foteini Liwicki, Marcus Liwicki
To achieve a good network performance in natural language processing (NLP) downstream tasks, several factors play important roles: dataset size, the right hyper-parameters, and well-trained embeddings.
2 code implementations • 23 Mar 2020 • Tosin P. Adewumi, Foteini Liwicki, Marcus Liwicki
However, wrong combination of hyper-parameters can produce poor quality vectors.
1 code implementation • 16 Mar 2020 • Gustav Grund Pihlgren, Fredrik Sandin, Marcus Liwicki
To evaluate this method we perform experiments on three standard publicly available datasets (LunarLander-v2, STL-10, and SVHN) and compare six different procedures for training image encoders (pixel-wise, perceptual similarity, and feature prediction losses; combined with two variations of image and feature encoding/decoding).
no code implementations • 3 Mar 2020 • Pedro Alonso, Kumar Shridhar, Denis Kleyko, Evgeny Osipov, Marcus Liwicki
The embedding achieved on par F1 scores while decreasing the time and memory requirements by several times compared to the conventional n-gram statistics, e. g., for one of the classifiers on a small dataset, the memory reduction was 6. 18 times; while train and test speed-ups were 4. 62 and 3. 84 times, respectively.
1 code implementation • 10 Jan 2020 • Gustav Grund Pihlgren, Fredrik Sandin, Marcus Liwicki
Autoencoders are trained to embed images from three different computer vision datasets using perceptual loss based on a pretrained model as well as pixel-wise loss.
2 code implementations • 12 Nov 2019 • Michele Alberti, Angela Botros, Narayan Schuez, Rolf Ingold, Marcus Liwicki, Mathias Seuret
In this work, we investigate the application of trainable and spectrally initializable matrix transformations on the feature maps produced by convolution operations.
no code implementations • 11 Jun 2019 • Michele Alberti, Vinaychandran Pondenkandath, Lars Vögtlin, Marcel Würsch, Rolf Ingold, Marcus Liwicki
The field of deep learning is experiencing a trend towards producing reproducible research.
2 code implementations • 11 Jun 2019 • Joel Niklaus, Michele Alberti, Vinaychandran Pondenkandath, Rolf Ingold, Marcus Liwicki
Jass is a very popular card game in Switzerland and is closely connected with Swiss culture.
1 code implementation • 11 Jun 2019 • Michele Alberti, Lars Vögtlin, Vinaychandran Pondenkandath, Mathias Seuret, Rolf Ingold, Marcus Liwicki
We measured the performance of our method on a recent dataset of challenging medieval manuscripts and surpassed state-of-the-art results by reducing the error by 80. 7%.
Ranked #1 on Text-Line Extraction on DIVA-HisDB
no code implementations • 22 May 2019 • Linda Studer, Michele Alberti, Vinaychandran Pondenkandath, Pinar Goktepe, Thomas Kolonko, Andreas Fischer, Marcus Liwicki, Rolf Ingold
Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated learning samples.
Ranked #7 on Image Classification on Kuzushiji-MNIST
1 code implementation • 8 Mar 2019 • Rajkumar Saini, Derek Dobson, Jon Morrey, Marcus Liwicki, Foteini Simistira Liwicki
We propose a Historical Document Reading Challenge on Large Chinese Structured Family Records, in short ICDAR2019 HDRC CHINESE.
no code implementations • 25 Feb 2019 • Ashutosh Mishra, Marcus Liwicki
The decoder model learns to extract descriptions for the image from scratch by decoding the joint representation of the object visual features and their object classes conditioned by the encoder component.
6 code implementations • 8 Jan 2019 • Kumar Shridhar, Felix Laumann, Marcus Liwicki
In this paper, Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference is proposed, that introduces probability distribution over the weights.
no code implementations • 5 Nov 2018 • Vinaychandran Pondenkandath, Michele Alberti, Sammer Puran, Rolf Ingold, Marcus Liwicki
We present a novel approach to leverage large unlabeled datasets by pre-training state-of-the-art deep neural networks on randomly-labeled datasets.
1 code implementation • 17 Oct 2018 • Paul Maergner, Vinaychandran Pondenkandath, Michele Alberti, Marcus Liwicki, Kaspar Riesen, Rolf Ingold, Andreas Fischer
Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures.
3 code implementations • 16 Oct 2018 • Kumar Shridhar, Ayushman Dash, Amit Sahu, Gustav Grund Pihlgren, Pedro Alonso, Vinaychandran Pondenkandath, Gyorgy Kovacs, Foteini Simistira, Marcus Liwicki
In this paper, we introduce the use of Semantic Hashing as embedding for the task of Intent Classification and achieve state-of-the-art performance on three frequently used benchmarks.
1 code implementation • 21 Aug 2018 • Michele Alberti, Vinaychandran Pondenkandath, Marcel Würsch, Manuel Bouillon, Mathias Seuret, Rolf Ingold, Marcus Liwicki
We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models.
5 code implementations • 15 Jun 2018 • Kumar Shridhar, Felix Laumann, Marcus Liwicki
On multiple datasets in supervised learning settings (MNIST, CIFAR-10, CIFAR-100), this variational inference method achieves performances equivalent to frequentist inference in identical architectures, while the two desiderata, a measure for uncertainty and regularization are incorporated naturally.
1 code implementation • 21 May 2018 • Sidney Pontes-Filho, Marcus Liwicki
A multilayer perceptron can behave as a generative classifier by applying bidirectional learning (BL).
12 code implementations • 23 Apr 2018 • Michele Alberti, Vinaychandran Pondenkandath, Marcel Würsch, Rolf Ingold, Marcus Liwicki
We introduce DeepDIVA: an infrastructure designed to enable quick and intuitive setup of reproducible experiments with a large range of useful analysis functionality.
no code implementations • 5 Apr 2018 • Vinaychandran Pondenkandath, Michele Alberti, Nicole Eichenberger, Rolf Ingold, Marcus Liwicki
Cross-depiction is the problem of identifying the same object even when it is depicted in a variety of manners.
no code implementations • 1 Apr 2018 • Andreas Kölsch, Ashutosh Mishra, Saurabh Varshneya, Muhammad Zeshan Afzal, Marcus Liwicki
This paper introduces a very challenging dataset of historic German documents and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate handwritten annotations of any kind in these documents.
no code implementations • 23 Nov 2017 • Michele Alberti, Mathias Seuret, Rolf Ingold, Marcus Liwicki
Experimental results suggest that in fact, there is no correlation between the reconstruction score and the quality of features for a classification task.
1 code implementation • 23 Nov 2017 • Michele Alberti, Manuel Bouillon, Rolf Ingold, Marcus Liwicki
This paper presents an open tool for standardizing the evaluation process of the layout analysis task of document images at pixel level.
no code implementations • 3 Nov 2017 • Andreas Kölsch, Muhammad Zeshan Afzal, Markus Ebbecke, Marcus Liwicki
This paper presents an approach for real-time training and testing for document image classification.
1 code implementation • 19 Oct 2017 • Michele Alberti, Mathias Seuret, Vinaychandran Pondenkandath, Rolf Ingold, Marcus Liwicki
In this paper, we present a novel approach to perform deep neural networks layer-wise weight initialization using Linear Discriminant Analysis (LDA).
5 code implementations • 11 Apr 2017 • Muhammad Zeshan Afzal, Andreas Kölsch, Sheraz Ahmed, Marcus Liwicki
We present an exhaustive investigation of recent Deep Learning architectures, algorithms, and strategies for the task of document image classification to finally reduce the error by more than half.
Ranked #27 on Document Image Classification on RVL-CDIP
4 code implementations • 19 Mar 2017 • Ayushman Dash, John Cristian Borges Gamboa, Sheraz Ahmed, Marcus Liwicki, Muhammad Zeshan Afzal
In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing images from their text descriptions.
no code implementations • 19 Mar 2017 • Andreas Kölsch, Muhammad Zeshan Afzal, Marcus Liwicki
In this work, we propose the combined usage of low- and high-level blocks of convolutional neural networks (CNNs) for improving object recognition.
no code implementations • 13 Mar 2017 • Michele Alberti, Mathias Seuret, Rolf Ingold, Marcus Liwicki
Experimental results suggest that in fact, there is no correlation between the reconstruction score and the quality of features for a classification task.
no code implementations • 1 Feb 2017 • Mathias Seuret, Michele Alberti, Rolf Ingold, Marcus Liwicki
In this paper, we present a novel approach for initializing deep neural networks, i. e., by turning PCA into neural layers.
no code implementations • 4 Jan 2017 • Monit Shah Singh, Vinaychandran Pondenkandath, Bo Zhou, Paul Lukowicz, Marcus Liwicki
Convolutional Neural Networks (CNNs) have become the state-of-the-art in various computer vision tasks, but they are still premature for most sensor data, especially in pervasive and wearable computing.
no code implementations • International Conference on Frontiers in Handwriting Recognition 2016 • Fotini Simistira, Mathias Seuret, Nicole Eichenberger, Angelika Garz, Marcus Liwicki, Rolf Ingold
Layout analysis results of several representative baseline technologies are also presented in order to help researchers evaluate their methods and advance the frontiers of complex historical manuscripts analysis.
no code implementations • 4 May 2016 • Sheraz Ahmed, Muhammad Imran Malik, Muhammad Zeshan Afzal, Koichi Kise, Masakazu Iwamura, Andreas Dengel, Marcus Liwicki
The method is generic, language independent and can be used for generation of labeled documents datasets (both scanned and cameracaptured) in any cursive and non-cursive language, e. g., English, Russian, Arabic, Urdu, etc.
no code implementations • 13 Nov 2015 • Federico Raue, Andreas Dengel, Thomas M. Breuel, Marcus Liwicki
We evaluated the proposed extension in the following scenarios: missing elements in one modality (visual or audio) and missing elements in both modalities (visual and sound).
3 code implementations • 17 Sep 2015 • Peter Burkert, Felix Trier, Muhammad Zeshan Afzal, Andreas Dengel, Marcus Liwicki
The proposed architecture achieves 99. 6% for CKP and 98. 63% for MMI, therefore performing better than the state of the art using CNNs.
Ranked #1 on Facial Expression Recognition (FER) on MMI
no code implementations • NeurIPS 2015 • Marijn F. Stollenga, Wonmin Byeon, Marcus Liwicki, Juergen Schmidhuber
In contrast, Multi-Dimensional Recurrent NNs (MD-RNNs) can perceive the entire spatio-temporal context of each pixel in a few sweeps through all pixels, especially when the RNN is a Long Short-Term Memory (LSTM).
no code implementations • CVPR 2015 • Wonmin Byeon, Thomas M. Breuel, Federico Raue, Marcus Liwicki
This paper addresses the problem of pixel-level segmentation and classification of scene images with an entirely learning-based approach using Long Short Term Memory (LSTM) recurrent neural networks, which are commonly used for sequence classification.
no code implementations • 23 Apr 2015 • Anguelos Nicolaou, Andrew D. Bagdanov, Marcus Liwicki, Dimosthenis Karatzas
In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification.
no code implementations • NeurIPS 2007 • Alex Graves, Marcus Liwicki, Horst Bunke, Jürgen Schmidhuber, Santiago Fernández
On-line handwriting recognition is unusual among sequence labelling tasks in that the underlying generator of the observed data, i. e. the movement of the pen, is recorded directly.