Dawid Rymarczyk, Joost van de Weijer, Bartosz Zielinski, & Bartlomiej Twardowski. (2023). ICICLE: Interpretable Class Incremental Continual Learning. Dawid Rymarczyk. In 20th IEEE International Conference on Computer Vision (pp. 1887–1898).
Abstract: Continual learning enables incremental learning of new tasks without forgetting those previously learned, resulting in positive knowledge transfer that can enhance performance on both new and old tasks. However, continual learning poses new challenges for interpretability, as the rationale behind model predictions may change over time, leading to interpretability concept drift. We address this problem by proposing Interpretable Class-InCremental LEarning (ICICLE), an exemplar-free approach that adopts a prototypical part-based approach. It consists of three crucial novelties: interpretability regularization that distills previously learned concepts while preserving user-friendly positive reasoning; proximity-based prototype initialization strategy dedicated to the fine-grained setting; and task-recency bias compensation devoted to prototypical parts. Our experimental results demonstrate that ICICLE reduces the interpretability concept drift and outperforms the existing exemplar-free methods of common class-incremental learning when applied to concept-based models.
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Jordy Van Landeghem, Ruben Tito, Lukasz Borchmann, Michal Pietruszka, Pawel Joziak, Rafal Powalski, et al. (2023). Document Understanding Dataset and Evaluation (DUDE). In 20th IEEE International Conference on Computer Vision (pp. 19528–19540).
Abstract: We call on the Document AI (DocAI) community to re-evaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks. Document Understanding Dataset and Evaluation (DUDE) seeks to remediate the halted research progress in understanding visually-rich documents (VRDs). We present a new dataset with novelties related to types of questions, answers, and document layouts based on multi-industry, multi-domain, and multi-page VRDs of various origins and dates. Moreover, we are pushing the boundaries of current methods by creating multi-task and multi-domain evaluation setups that more accurately simulate real-world situations where powerful generalization and adaptation under low-resource settings are desired. DUDE aims to set a new standard as a more practical, long-standing benchmark for the community, and we hope that it will lead to future extensions and contributions that address real-world challenges. Finally, our work illustrates the importance of finding more efficient ways to model language, images, and layout in DocAI.
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Yuyang Liu, Yang Cong, Dipam Goswami, Xialei Liu, & Joost Van de Weijer. (2023). Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection. In 20th IEEE International Conference on Computer Vision (pp. 11367–11377).
Abstract: In incremental learning, replaying stored samples from previous tasks together with current task samples is one of the most efficient approaches to address catastrophic forgetting. However, unlike incremental classification, image replay has not been successfully applied to incremental object detection (IOD). In this paper, we identify the overlooked problem of foreground shift as the main reason for this. Foreground shift only occurs when replaying images of previous tasks and refers to the fact that their background might contain foreground objects of the current task. To overcome this problem, a novel and efficient Augmented Box Replay (ABR) method is developed that only stores and replays foreground objects and thereby circumvents the foreground shift problem. In addition, we propose an innovative Attentive RoI Distillation loss that uses spatial attention from region-of-interest (RoI) features to constrain current model to focus on the most important information from old model. ABR significantly reduces forgetting of previous classes while maintaining high plasticity in current classes. Moreover, it considerably reduces the storage requirements when compared to standard image replay. Comprehensive experiments on Pascal-VOC and COCO datasets support the state-of-the-art performance of our model.
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Guillermo Torres, Debora Gil, Antoni Rosell, S. Mena, & Carles Sanchez. (2023). Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules. In 37th International Congress and Exhibition is organized by Computer Assisted Radiology and Surgery.
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Sonia Baeza, Debora Gil, Carles Sanchez, Guillermo Torres, Ignasi Garcia Olive, Ignasi Guasch, et al. (2023). Biopsia virtual radiomica para el diagnóstico histológico de nódulos pulmonares – Resultados intermedios del proyecto Radiolung. In SEPAR.
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Debora Gil, Guillermo Torres, & Carles Sanchez. (2023). Transforming radiomic features into radiological words. In IEEE International Symposium on Biomedical Imaging.
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Pau Cano, Debora Gil, & Eva Musulen. (2023). Towards automatic detection of helicobacter pylori in histological samples of gastric tissue. In IEEE International Symposium on Biomedical Imaging.
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Guillermo Torres, Debora Gil, Antonio Rosell, Sonia Baeza, & Carles Sanchez. (2023). A radiomic biopsy for virtual histology of pulmonary nodules. In IEEE International Symposium on Biomedical Imaging.
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Artur Xarles, Sergio Escalera, Thomas B. Moeslund, & Albert Clapes. (2023). ASTRA: An Action Spotting TRAnsformer for Soccer Videos. In Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports (93–102).
Abstract: In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches. ASTRA addresses several challenges inherent in the task and dataset, including the requirement for precise action localization, the presence of a long-tail data distribution, non-visibility in certain actions, and inherent label noise. To do so, ASTRA incorporates (a) a Transformer encoder-decoder architecture to achieve the desired output temporal resolution and to produce precise predictions, (b) a balanced mixup strategy to handle the long-tail distribution of the data, (c) an uncertainty-aware displacement head to capture the label variability, and (d) input audio signal to enhance detection of non-visible actions. Results demonstrate the effectiveness of ASTRA, achieving a tight Average-mAP of 66.82 on the test set. Moreover, in the SoccerNet 2023 Action Spotting challenge, we secure the 3rd position with an Average-mAP of 70.21 on the challenge set.
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Eduardo Aguilar, Bogdan Raducanu, Petia Radeva, & Joost Van de Weijer. (2023). Continual Evidential Deep Learning for Out-of-Distribution Detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (pp. 3444–3454).
Abstract: Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-ofdistribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method 1, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95.
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Lei Kang, Lichao Zhang, & Dazhi Jiang. (2023). Learning Robust Self-Attention Features for Speech Emotion Recognition with Label-Adaptive Mixup. In IEEE International Conference on Acoustics, Speech and Signal Processing.
Abstract: Speech Emotion Recognition (SER) is to recognize human emotions in a natural verbal interaction scenario with machines, which is considered as a challenging problem due to the ambiguous human emotions. Despite the recent progress in SER, state-of-the-art models struggle to achieve a satisfactory performance. We propose a self-attention based method with combined use of label-adaptive mixup and center loss. By adapting label probabilities in mixup and fitting center loss to the mixup training scheme, our proposed method achieves a superior performance to the state-of-the-art methods.
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Subhajit Maity, Sanket Biswas, Siladittya Manna, Ayan Banerjee, Josep Llados, Saumik Bhattacharya, et al. (2023). SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation. In 17th International Conference on Doccument Analysis and Recognition (Vol. 14187, 342–360).
Abstract: Document layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, etc. However, most of the existing works have ignored the crucial fact regarding the scarcity of labeled data. With growing internet connectivity to personal life, an enormous amount of documents had been available in the public domain and thus making data annotation a tedious task. We address this challenge using self-supervision and unlike, the few existing self-supervised document segmentation approaches which use text mining and textual labels, we use a complete vision-based approach in pre-training without any ground-truth label or its derivative. Instead, we generate pseudo-layouts from the document images to pre-train an image encoder to learn the document object representation and localization in a self-supervised framework before fine-tuning it with an object detection model. We show that our pipeline sets a new benchmark in this context and performs at par with the existing methods and the supervised counterparts, if not outperforms. The code is made publicly available at: this https URL
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Mohamed Ramzy Ibrahim, Robert Benavente, Daniel Ponsa, & Felipe Lumbreras. (2023). Unveiling the Influence of Image Super-Resolution on Aerial Scene Classification. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (Vol. 14469, 214–228). LNCS.
Abstract: Deep learning has made significant advances in recent years, and as a result, it is now in a stage where it can achieve outstanding results in tasks requiring visual understanding of scenes. However, its performance tends to decline when dealing with low-quality images. The advent of super-resolution (SR) techniques has started to have an impact on the field of remote sensing by enabling the restoration of fine details and enhancing image quality, which could help to increase performance in other vision tasks. However, in previous works, contradictory results for scene visual understanding were achieved when SR techniques were applied. In this paper, we present an experimental study on the impact of SR on enhancing aerial scene classification. Through the analysis of different state-of-the-art SR algorithms, including traditional methods and deep learning-based approaches, we unveil the transformative potential of SR in overcoming the limitations of low-resolution (LR) aerial imagery. By enhancing spatial resolution, more fine details are captured, opening the door for an improvement in scene understanding. We also discuss the effect of different image scales on the quality of SR and its effect on aerial scene classification. Our experimental work demonstrates the significant impact of SR on enhancing aerial scene classification compared to LR images, opening new avenues for improved remote sensing applications.
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Patricia Suarez, Dario Carpio, & Angel Sappa. (2023). Depth Map Estimation from a Single 2D Image. In 17th International Conference on Signal-Image Technology & Internet-Based Systems (pp. 347–353).
Abstract: This paper presents an innovative architecture based on a Cycle Generative Adversarial Network (CycleGAN) for the synthesis of high-quality depth maps from monocular images. The proposed architecture leverages a diverse set of loss functions, including cycle consistency, contrastive, identity, and least square losses, to facilitate the generation of depth maps that exhibit realism and high fidelity. A notable feature of the approach is its ability to synthesize depth maps from grayscale images without the need for paired training data. Extensive comparisons with different state-of-the-art methods show the superiority of the proposed approach in both quantitative metrics and visual quality. This work addresses the challenge of depth map synthesis and offers significant advancements in the field.
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Rafael E. Rivadeneira, Henry Velesaca, & Angel Sappa. (2023). Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach. In 17th International Conference on Signal-Image Technology & Internet-Based Systems.
Abstract: This work proposes a novel approach that integrates super-resolution techniques with off-the-shelf object detection methods to tackle the problem of handling very low-resolution thermal images. The suggested approach begins by enhancing the low-resolution (LR) thermal images through a guided super-resolution strategy, leveraging a high-resolution (HR) visible spectrum image. Subsequently, object detection is performed on the high-resolution thermal image. The experimental results demonstrate tremendous improvements in comparison with both scenarios: when object detection is performed on the LR thermal image alone, as well as when object detection is conducted on the up-sampled LR thermal image. Moreover, the proposed approach proves highly valuable in camouflaged scenarios where objects might remain undetected in visible spectrum images.
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