| Records |
| Author |
Cristhian A. Aguilera-Carrasco; Luis Felipe Gonzalez-Böhme; Francisco Valdes; Francisco Javier Quitral Zapata; Bogdan Raducanu |
| Title |
A Hand-Drawn Language for Human–Robot Collaboration in Wood Stereotomy |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
11 |
Issue  |
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Pages |
100975 - 100985 |
| Keywords |
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| Abstract |
This study introduces a novel, hand-drawn language designed to foster human-robot collaboration in wood stereotomy, central to carpentry and joinery professions. Based on skilled carpenters’ line and symbol etchings on timber, this language signifies the location, geometry of woodworking joints, and timber placement within a framework. A proof-of-concept prototype has been developed, integrating object detectors, keypoint regression, and traditional computer vision techniques to interpret this language and enable an extensive repertoire of actions. Empirical data attests to the language’s efficacy, with the successful identification of a specific set of symbols on various wood species’ sawn surfaces, achieving a mean average precision (mAP) exceeding 90%. Concurrently, the system can accurately pinpoint critical positions that facilitate robotic comprehension of carpenter-indicated woodworking joint geometry. The positioning error, approximately 3 pixels, meets industry standards. |
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LAMP |
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no |
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Admin @ si @ AGV2023 |
Serial |
3969 |
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| Author |
Mohamed Ali Souibgui; Pau Torras; Jialuo Chen; Alicia Fornes |
| Title |
An Evaluation of Handwritten Text Recognition Methods for Historical Ciphered Manuscripts |
Type |
Conference Article |
| Year |
2023 |
Publication |
7th International Workshop on Historical Document Imaging and Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
7-12 |
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| Abstract |
This paper investigates the effectiveness of different deep learning HTR families, including LSTM, Seq2Seq, and transformer-based approaches with self-supervised pretraining, in recognizing ciphered manuscripts from different historical periods and cultures. The goal is to identify the most suitable method or training techniques for recognizing ciphered manuscripts and to provide insights into the challenges and opportunities in this field of research. We evaluate the performance of these models on several datasets of ciphered manuscripts and discuss their results. This study contributes to the development of more accurate and efficient methods for recognizing historical manuscripts for the preservation and dissemination of our cultural heritage. |
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HIP |
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DAG |
Approved |
no |
| Call Number |
Admin @ si @ STC2023 |
Serial |
3849 |
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| Author |
Pau Torras; Mohamed Ali Souibgui; Sanket Biswas; Alicia Fornes |
| Title |
Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images |
Type |
Conference Article |
| Year |
2023 |
Publication |
Document Analysis and Recognition – ICDAR 2023 Workshops |
Abbreviated Journal |
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| Volume |
14193 |
Issue |
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Pages |
83-93 |
| Keywords |
Historical Manuscripts; Symbol Alignment |
| Abstract |
Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system. |
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ICDAR |
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DAG |
Approved |
no |
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Admin @ si @ TSS2023 |
Serial |
3850 |
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| Author |
Marwa Dhiaf; Mohamed Ali Souibgui; Kai Wang; Yuyang Liu; Yousri Kessentini; Alicia Fornes; Ahmed Cheikh Rouhou |
| Title |
CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition |
Type |
Miscellaneous |
| Year |
2023 |
Publication |
Arxiv |
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Self-supervised learning has recently emerged as a strong alternative in document analysis. These approaches are now capable of learning high-quality image representations and overcoming the limitations of supervised methods, which require a large amount of labeled data. However, these methods are unable to capture new knowledge in an incremental fashion, where data is presented to the model sequentially, which is closer to the realistic scenario. In this paper, we explore the potential of continual self-supervised learning to alleviate the catastrophic forgetting problem in handwritten text recognition, as an example of sequence recognition. Our method consists in adding intermediate layers called adapters for each task, and efficiently distilling knowledge from the previous model while learning the current task. Our proposed framework is efficient in both computation and memory complexity. To demonstrate its effectiveness, we evaluate our method by transferring the learned model to diverse text recognition downstream tasks, including Latin and non-Latin scripts. As far as we know, this is the first application of continual self-supervised learning for handwritten text recognition. We attain state-of-the-art performance on English, Italian and Russian scripts, whilst adding only a few parameters per task. The code and trained models will be publicly available. |
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DAG |
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no |
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Admin @ si @ DSW2023 |
Serial |
3851 |
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| Author |
JW Xiao; CB Zhang; J. Feng; Xialei Liu; Joost Van de Weijer; MM Cheng |
| Title |
Endpoints Weight Fusion for Class Incremental Semantic Segmentation |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition |
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Issue |
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Pages |
7204-7213 |
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Class incremental semantic segmentation (CISS) focuses on alleviating catastrophic forgetting to improve discrimination. Previous work mainly exploit regularization (e.g., knowledge distillation) to maintain previous knowledge in the current model. However, distillation alone often yields limited gain to the model since only the representations of old and new models are restricted to be consistent. In this paper, we propose a simple yet effective method to obtain a model with strong memory of old knowledge, named Endpoints Weight Fusion (EWF). In our method, the model containing old knowledge is fused with the model retaining new knowledge in a dynamic fusion manner, strengthening the memory of old classes in ever-changing distributions. In addition, we analyze the relation between our fusion strategy and a popular moving average technique EMA, which reveals why our method is more suitable for class-incremental learning. To facilitate parameter fusion with closer distance in the parameter space, we use distillation to enhance the optimization process. Furthermore, we conduct experiments on two widely used datasets, achieving the state-of-the-art performance. |
| Address |
Vancouver; Canada; June 2023 |
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Conference |
CVPR |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ XZF2023 |
Serial |
3854 |
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| Author |
Patricia Suarez; Dario Carpio; Angel Sappa |
| Title |
A Deep Learning Based Approach for Synthesizing Realistic Depth Maps |
Type |
Conference Article |
| Year |
2023 |
Publication |
22nd International Conference on Image Analysis and Processing |
Abbreviated Journal |
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| Volume |
14234 |
Issue |
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Pages |
369–380 |
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| Abstract |
This paper presents a novel cycle generative adversarial network (CycleGAN) architecture for synthesizing high-quality depth maps from a given monocular image. The proposed architecture uses multiple loss functions, including cycle consistency, contrastive, identity, and least square losses, to enable the generation of realistic and high-fidelity depth maps. The proposed approach addresses this challenge by synthesizing depth maps from RGB images without requiring paired training data. Comparisons with several state-of-the-art approaches are provided showing the proposed approach overcome other approaches both in terms of quantitative metrics and visual quality. |
| Address |
Udine; Italia; Setember 2023 |
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ICIAP |
| Notes |
MSIAU |
Approved |
no |
| Call Number |
Admin @ si @ SCS2023a |
Serial |
3968 |
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| Author |
Francesc Net; Marc Folia; Pep Casals; Lluis Gomez |
| Title |
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections |
Type |
Conference Article |
| Year |
2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
14191 |
Issue |
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Pages |
3-17 |
| Keywords |
Image deduplication; Near-duplicate images detection; Transductive Learning; Photographic Archives; Deep Learning |
| Abstract |
This paper presents a comparative study of near-duplicate image detection techniques in a real-world use case scenario, where a document management company is commissioned to manually annotate a collection of scanned photographs. Detecting duplicate and near-duplicate photographs can reduce the time spent on manual annotation by archivists. This real use case differs from laboratory settings as the deployment dataset is available in advance, allowing the use of transductive learning. We propose a transductive learning approach that leverages state-of-the-art deep learning architectures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). Our approach involves pre-training a deep neural network on a large dataset and then fine-tuning the network on the unlabeled target collection with self-supervised learning. The results show that the proposed approach outperforms the baseline methods in the task of near-duplicate image detection in the UKBench and an in-house private dataset. |
| Address |
San Jose; CA; USA; August 2023 |
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LNCS |
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ICDAR |
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DAG |
Approved |
no |
| Call Number |
Admin @ si @ NFC2023 |
Serial |
3859 |
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| Author |
Parichehr Behjati; Pau Rodriguez; Carles Fernandez; Isabelle Hupont; Armin Mehri; Jordi Gonzalez |
| Title |
Single image super-resolution based on directional variance attention network |
Type |
Journal Article |
| Year |
2023 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
133 |
Issue |
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Pages |
108997 |
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Recent advances in single image super-resolution (SISR) explore the power of deep convolutional neural networks (CNNs) to achieve better performance. However, most of the progress has been made by scaling CNN architectures, which usually raise computational demands and memory consumption. This makes modern architectures less applicable in practice. In addition, most CNN-based SR methods do not fully utilize the informative hierarchical features that are helpful for final image recovery. In order to address these issues, we propose a directional variance attention network (DiVANet), a computationally efficient yet accurate network for SISR. Specifically, we introduce a novel directional variance attention (DiVA) mechanism to capture long-range spatial dependencies and exploit inter-channel dependencies simultaneously for more discriminative representations. Furthermore, we propose a residual attention feature group (RAFG) for parallelizing attention and residual block computation. The output of each residual block is linearly fused at the RAFG output to provide access to the whole feature hierarchy. In parallel, DiVA extracts most relevant features from the network for improving the final output and preventing information loss along the successive operations inside the network. Experimental results demonstrate the superiority of DiVANet over the state of the art in several datasets, while maintaining relatively low computation and memory footprint. The code is available at https://github.com/pbehjatii/DiVANet. |
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ISE |
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no |
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Admin @ si @ BPF2023 |
Serial |
3861 |
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| Author |
Bonifaz Stuhr; Jurgen Brauer; Bernhard Schick; Jordi Gonzalez |
| Title |
Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation |
Type |
Miscellaneous |
| Year |
2023 |
Publication |
Arxiv |
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A common goal of unpaired image-to-image translation is to preserve content consistency between source images and translated images while mimicking the style of the target domain. Due to biases between the datasets of both domains, many methods suffer from inconsistencies caused by the translation process. Most approaches introduced to mitigate these inconsistencies do not constrain the discriminator, leading to an even more ill-posed training setup. Moreover, none of these approaches is designed for larger crop sizes. In this work, we show that masking the inputs of a global discriminator for both domains with a content-based mask is sufficient to reduce content inconsistencies significantly. However, this strategy leads to artifacts that can be traced back to the masking process. To reduce these artifacts, we introduce a local discriminator that operates on pairs of small crops selected with a similarity sampling strategy. Furthermore, we apply this sampling strategy to sample global input crops from the source and target dataset. In addition, we propose feature-attentive denormalization to selectively incorporate content-based statistics into the generator stream. In our experiments, we show that our method achieves state-of-the-art performance in photorealistic sim-to-real translation and weather translation and also performs well in day-to-night translation. Additionally, we propose the cKVD metric, which builds on the sKVD metric and enables the examination of translation quality at the class or category level. |
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ISE |
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no |
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Admin @ si @ SBS2023 |
Serial |
3863 |
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| Author |
Maciej Wielgosz; Antonio Lopez; Muhamad Naveed Riaz |
| Title |
CARLA-BSP: a simulated dataset with pedestrians |
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Miscellaneous |
| Year |
2023 |
Publication |
Arxiv |
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We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0.9.13). We provide use cases for pedestrian detection, autoencoding, pose estimation, and pose lifting. We also showcase baseline results. |
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ADAS |
Approved |
no |
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Admin @ si @ WLN2023 |
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3866 |
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| Author |
Akhil Gurram; Antonio Lopez |
| Title |
On the Metrics for Evaluating Monocular Depth Estimation |
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Miscellaneous |
| Year |
2023 |
Publication |
Arxiv |
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Monocular Depth Estimation (MDE) is performed to produce 3D information that can be used in downstream tasks such as those related to on-board perception for Autonomous Vehicles (AVs) or driver assistance. Therefore, a relevant arising question is whether the standard metrics for MDE assessment are a good indicator of the accuracy of future MDE-based driving-related perception tasks. We address this question in this paper. In particular, we take the task of 3D object detection on point clouds as a proxy of on-board perception. We train and test state-of-the-art 3D object detectors using 3D point clouds coming from MDE models. We confront the ranking of object detection results with the ranking given by the depth estimation metrics of the MDE models. We conclude that, indeed, MDE evaluation metrics give rise to a ranking of methods that reflects relatively well the 3D object detection results we may expect. Among the different metrics, the absolute relative (abs-rel) error seems to be the best for that purpose. |
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ADAS |
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no |
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Admin @ si @ GuL2023 |
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3867 |
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| Author |
David Pujol Perich; Albert Clapes; Sergio Escalera |
| Title |
SADA: Semantic adversarial unsupervised domain adaptation for Temporal Action Localization |
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Miscellaneous |
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2023 |
Publication |
Arxiv |
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Temporal Action Localization (TAL) is a complex task that poses relevant challenges, particularly when attempting to generalize on new -- unseen -- domains in real-world applications. These scenarios, despite realistic, are often neglected in the literature, exposing these solutions to important performance degradation. In this work, we tackle this issue by introducing, for the first time, an approach for Unsupervised Domain Adaptation (UDA) in sparse TAL, which we refer to as Semantic Adversarial unsupervised Domain Adaptation (SADA). Our contributions are threefold: (1) we pioneer the development of a domain adaptation model that operates on realistic sparse action detection benchmarks; (2) we tackle the limitations of global-distribution alignment techniques by introducing a novel adversarial loss that is sensitive to local class distributions, ensuring finer-grained adaptation; and (3) we present a novel set of benchmarks based on EpicKitchens100 and CharadesEgo, that evaluate multiple domain shifts in a comprehensive manner. Our experiments indicate that SADA improves the adaptation across domains when compared to fully supervised state-of-the-art and alternative UDA methods, attaining a performance boost of up to 6.14% mAP. |
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HUPBA |
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no |
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Admin @ si @ PCE2023 |
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4014 |
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Senmao Li; Joost van de Weijer; Taihang Hu; Fahad Shahbaz Khan; Qibin Hou; Yaxing Wang; Jian Yang |
| Title |
StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing |
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Miscellaneous |
| Year |
2023 |
Publication |
Arxiv |
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A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images. They either finetune the model, or invert the image in the latent space of the pretrained model. However, they suffer from two problems: (1) Unsatisfying results for selected regions, and unexpected changes in nonselected regions. (2) They require careful text prompt editing where the prompt should include all visual objects in the input image. To address this, we propose two improvements: (1) Only optimizing the input of the value linear network in the cross-attention layers, is sufficiently powerful to reconstruct a real image. (2) We propose attention regularization to preserve the object-like attention maps after editing, enabling us to obtain accurate style editing without invoking significant structural changes. We further improve the editing technique which is used for the unconditional branch of classifier-free guidance, as well as the conditional one as used by P2P. Extensive experimental prompt-editing results on a variety of images, demonstrate qualitatively and quantitatively that our method has superior editing capabilities than existing and concurrent works. |
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LAMP |
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no |
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Admin @ si @ LWH2023 |
Serial |
3870 |
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Antonio Carta; Andrea Cossu; Vincenzo Lomonaco; Davide Bacciu; Joost Van de Weijer |
| Title |
Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning |
Type |
Miscellaneous |
| Year |
2023 |
Publication |
Arxiv |
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Distributed learning on the edge often comprises self-centered devices (SCD) which learn local tasks independently and are unwilling to contribute to the performance of other SDCs. How do we achieve forward transfer at zero cost for the single SCDs? We formalize this problem as a Distributed Continual Learning scenario, where SCD adapt to local tasks and a CL model consolidates the knowledge from the resulting stream of models without looking at the SCD's private data. Unfortunately, current CL methods are not directly applicable to this scenario. We propose Data-Agnostic Consolidation (DAC), a novel double knowledge distillation method that consolidates the stream of SC models without using the original data. DAC performs distillation in the latent space via a novel Projected Latent Distillation loss. Experimental results show that DAC enables forward transfer between SCDs and reaches state-of-the-art accuracy on Split CIFAR100, CORe50 and Split TinyImageNet, both in reharsal-free and distributed CL scenarios. Somewhat surprisingly, even a single out-of-distribution image is sufficient as the only source of data during consolidation. |
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LAMP |
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no |
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Admin @ si @ CCL2023 |
Serial |
3871 |
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| Author |
Shiqi Yang; Yaxing Wang; Luis Herranz; Shangling Jui; Joost Van de Weijer |
| Title |
Casting a BAIT for offline and online source-free domain adaptation |
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Journal Article |
| Year |
2023 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
| Volume |
234 |
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Pages |
103747 |
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| Abstract |
We address the source-free domain adaptation (SFDA) problem, where only the source model is available during adaptation to the target domain. We consider two settings: the offline setting where all target data can be visited multiple times (epochs) to arrive at a prediction for each target sample, and the online setting where the target data needs to be directly classified upon arrival. Inspired by diverse classifier based domain adaptation methods, in this paper we introduce a second classifier, but with another classifier head fixed. When adapting to the target domain, the additional classifier initialized from source classifier is expected to find misclassified features. Next, when updating the feature extractor, those features will be pushed towards the right side of the source decision boundary, thus achieving source-free domain adaptation. Experimental results show that the proposed method achieves competitive results for offline SFDA on several benchmark datasets compared with existing DA and SFDA methods, and our method surpasses by a large margin other SFDA methods under online source-free domain adaptation setting. |
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Admin @ si @ YWH2023 |
Serial |
3874 |
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