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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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Guillermo Torres, Debora Gil, Antoni Rosell, S. Mena, & Carles Sanchez. (2023). Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules – Intermediate Results of the RadioLung Project. IJCARS - International Journal of Computer Assisted Radiology and Surgery, .
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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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Guillermo Torres, Jan Rodríguez Dueñas, Sonia Baeza, Antoni Rosell, Carles Sanchez, & Debora Gil. (2023). Prediction of Malignancy in Lung Cancer using several strategies for the fusion of Multi-Channel Pyradiomics Images. In 7th Workshop on Digital Image Processing for Medical and Automotive Industry in the framework of SYNASC 2023.
Abstract: This study shows the generation process and the subsequent study of the representation space obtained by extracting GLCM texture features from computer-aided tomography (CT) scans of pulmonary nodules (PN). For this, data from 92 patients from the Germans Trias i Pujol University Hospital were used. The workflow focuses on feature extraction using Pyradiomics and the VGG16 Convolutional Neural Network (CNN). The aim of the study is to assess whether the data obtained have a positive impact on the diagnosis of lung cancer (LC). To design a machine learning (ML) model training method that allows generalization, we train SVM and neural network (NN) models, evaluating diagnosis performance using metrics defined at slice and nodule level.
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Hao Fang, Ajian Liu, Jun Wan, Sergio Escalera, Chenxu Zhao, Xu Zhang, et al. (2023). Surveillance Face Anti-spoofing.
Abstract: Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.
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Hao Fang, Ajian Liu, Jun Wan, Sergio Escalera, Hugo Jair Escalante, & Zhen Lei. (2023). Surveillance Face Presentation Attack Detection Challenge. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 6360–6370).
Abstract: Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, most of the studies lacked consideration of long-distance scenarios. Specifically, compared with FAS in traditional scenes such as phone unlocking, face payment, and self-service security inspection, FAS in long-distance such as station squares, parks, and self-service supermarkets are equally important, but it has not been sufficiently explored yet. In order to fill this gap in the FAS community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask). SuHiFiMask contains 10,195 videos from 101 subjects of different age groups, which are collected by 7 mainstream surveillance cameras. Based on this dataset and protocol-3 for evaluating the robustness of the algorithm under quality changes, we organized a face presentation attack detection challenge in surveillance scenarios. It attracted 180 teams for the development phase with a total of 37 teams qualifying for the final round. The organization team re-verified and re-ran the submitted code and used the results as the final ranking. In this paper, we present an overview of the challenge, including an introduction to the dataset used, the definition of the protocol, the evaluation metrics, and the announcement of the competition results. Finally, we present the top-ranked algorithms and the research ideas provided by the competition for attack detection in long-range surveillance scenarios.
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Hao Wu, Alejandro Ariza-Casabona, Bartłomiej Twardowski, & Tri Kurniawan Wijaya. (2023). MM-GEF: Multi-modal representation meet collaborative filtering.
Abstract: In modern e-commerce, item content features in various modalities offer accurate yet comprehensive information to recommender systems. The majority of previous work either focuses on learning effective item representation during modelling user-item interactions, or exploring item-item relationships by analysing multi-modal features. Those methods, however, fail to incorporate the collaborative item-user-item relationships into the multi-modal feature-based item structure. In this work, we propose a graph-based item structure enhancement method MM-GEF: Multi-Modal recommendation with Graph Early-Fusion, which effectively combines the latent item structure underlying multi-modal contents with the collaborative signals. Instead of processing the content feature in different modalities separately, we show that the early-fusion of multi-modal features provides significant improvement. MM-GEF learns refined item representations by injecting structural information obtained from both multi-modal and collaborative signals. Through extensive experiments on four publicly available datasets, we demonstrate systematical improvements of our method over state-of-the-art multi-modal recommendation methods.
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Hugo Bertiche, Niloy J Mitra, Kuldeep Kulkarni, Chun Hao Paul Huang, Tuanfeng Y Wang, Meysam Madadi, et al. (2023). Blowing in the Wind: CycleNet for Human Cinemagraphs from Still Images. In 36th IEEE Conference on Computer Vision and Pattern Recognition (pp. 459–468).
Abstract: Cinemagraphs are short looping videos created by adding subtle motions to a static image. This kind of media is popular and engaging. However, automatic generation of cinemagraphs is an underexplored area and current solutions require tedious low-level manual authoring by artists. In this paper, we present an automatic method that allows generating human cinemagraphs from single RGB images. We investigate the problem in the context of dressed humans under the wind. At the core of our method is a novel cyclic neural network that produces looping cinemagraphs for the target loop duration. To circumvent the problem of collecting real data, we demonstrate that it is possible, by working in the image normal space, to learn garment motion dynamics on synthetic data and generalize to real data. We evaluate our method on both synthetic and real data and demonstrate that it is possible to create compelling and plausible cinemagraphs from single RGB images.
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Iban Berganzo-Besga, Hector A. Orengo, Felipe Lumbreras, Aftab Alam, Rosie Campbell, Petrus J Gerrits, et al. (2023). Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan. ScR - Scientific Reports, 13, 11257.
Abstract: This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map.
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Javier Selva, Anders S. Johansen, Sergio Escalera, Kamal Nasrollahi, Thomas B. Moeslund, & Albert Clapes. (2023). Video transformers: A survey. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), 12922–12943.
Abstract: Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video. However, they lack inductive biases and scale quadratically with input length. These limitations are further exacerbated when dealing with the high dimensionality introduced by the temporal dimension. While there are surveys analyzing the advances of Transformers for vision, none focus on an in-depth analysis of video-specific designs. In this survey, we analyze the main contributions and trends of works leveraging Transformers to model video. Specifically, we delve into how videos are handled at the input level first. Then, we study the architectural changes made to deal with video more efficiently, reduce redundancy, re-introduce useful inductive biases, and capture long-term temporal dynamics. In addition, we provide an overview of different training regimes and explore effective self-supervised learning strategies for video. Finally, we conduct a performance comparison on the most common benchmark for Video Transformers (i.e., action classification), finding them to outperform 3D ConvNets even with less computational complexity.
Keywords: Artificial Intelligence; Computer Vision; Self-Attention; Transformers; Video Representations
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