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| Author | Ruben Ballester; Xavier Arnal Clemente; Carles Casacuberta; Meysam Madadi; Ciprian Corneanu | ||||
| Title | Towards explaining the generalization gap in neural networks using topological data analysis | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Understanding how neural networks generalize on unseen data is crucial for designing more robust and reliable models. In this paper, we study the generalization gap of neural networks using methods from topological data analysis. For this purpose, we compute homological persistence diagrams of weighted graphs constructed from neuron activation correlations after a training phase, aiming to capture patterns that are linked to the generalization capacity of the network. We compare the usefulness of different numerical summaries from persistence diagrams and show that a combination of some of them can accurately predict and partially explain the generalization gap without the need of a test set. Evaluation on two computer vision recognition tasks (CIFAR10 and SVHN) shows competitive generalization gap prediction when compared against state-of-the-art methods. | ||||
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| Notes | HUPBA; no menciona | Approved  |
no | ||
| Call Number | Admin @ si @ BAC2022 | Serial | 3821 | ||
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| Author | Arya Farkhondeh; Cristina Palmero; Simone Scardapane; Sergio Escalera | ||||
| Title | Towards Self-Supervised Gaze Estimation | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Recent joint embedding-based self-supervised methods have surpassed standard supervised approaches on various image recognition tasks such as image classification. These self-supervised methods aim at maximizing agreement between features extracted from two differently transformed views of the same image, which results in learning an invariant representation with respect to appearance and geometric image transformations. However, the effectiveness of these approaches remains unclear in the context of gaze estimation, a structured regression task that requires equivariance under geometric transformations (e.g., rotations, horizontal flip). In this work, we propose SwAT, an equivariant version of the online clustering-based self-supervised approach SwAV, to learn more informative representations for gaze estimation. We demonstrate that SwAT, with ResNet-50 and supported with uncurated unlabeled face images, outperforms state-of-the-art gaze estimation methods and supervised baselines in various experiments. In particular, we achieve up to 57% and 25% improvements in cross-dataset and within-dataset evaluation tasks on existing benchmarks (ETH-XGaze, Gaze360, and MPIIFaceGaze). | ||||
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| Notes | HUPBA; no menciona | Approved |
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| Call Number | Admin @ si @ FPS2022 | Serial | 3822 | ||
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| Author | Javier Selva; Anders S. Johansen; Sergio Escalera; Kamal Nasrollahi; Thomas B. Moeslund; Albert Clapes | ||||
| Title | Video transformers: A survey | Type | Journal Article | ||
| Year | 2023 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 45 | Issue | 11 | Pages | 12922-12943 |
| Keywords | Artificial Intelligence; Computer Vision; Self-Attention; Transformers; Video Representations | ||||
| 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. | ||||
| Address | 1 Nov. 2023 | ||||
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| Notes | HUPBA; no menciona | Approved |
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| Call Number | Admin @ si @ SJE2023 | Serial | 3823 | ||
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| Author | Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
| Title | Word separation in continuous sign language using isolated signs and post-processing | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Continuous Sign Language Recognition (CSLR) is a long challenging task in Computer Vision due to the difficulties in detecting the explicit boundaries between the words in a sign sentence. To deal with this challenge, we propose a two-stage model. In the first stage, the predictor model, which includes a combination of CNN, SVD, and LSTM, is trained with the isolated signs. In the second stage, we apply a post-processing algorithm to the Softmax outputs obtained from the first part of the model in order to separate the isolated signs in the continuous signs. Due to the lack of a large dataset, including both the sign sequences and the corresponding isolated signs, two public datasets in Isolated Sign Language Recognition (ISLR), RKS-PERSIANSIGN and ASLVID, are used for evaluation. Results of the continuous sign videos confirm the efficiency of the proposed model to deal with isolated sign boundaries detection. | ||||
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| Notes | HUPBA; no menciona | Approved |
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| Call Number | Admin @ si @ RKE2022b | Serial | 3824 | ||
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| Author | Ruben Tito; Dimosthenis Karatzas; Ernest Valveny | ||||
| Title | Hierarchical multimodal transformers for Multi-Page DocVQA | Type | Journal Article | ||
| Year | 2023 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 144 | Issue | Pages | 109834 | |
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| Abstract | Document Visual Question Answering (DocVQA) refers to the task of answering questions from document images. Existing work on DocVQA only considers single-page documents. However, in real scenarios documents are mostly composed of multiple pages that should be processed altogether. In this work we extend DocVQA to the multi-page scenario. For that, we first create a new dataset, MP-DocVQA, where questions are posed over multi-page documents instead of single pages. Second, we propose a new hierarchical method, Hi-VT5, based on the T5 architecture, that overcomes the limitations of current methods to process long multi-page documents. The proposed method is based on a hierarchical transformer architecture where the encoder summarizes the most relevant information of every page and then, the decoder takes this summarized information to generate the final answer. Through extensive experimentation, we demonstrate that our method is able, in a single stage, to answer the questions and provide the page that contains the relevant information to find the answer, which can be used as a kind of explainability measure. | ||||
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| ISSN | ISSN 0031-3203 | ISBN | Medium | ||
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| Notes | DAG; 600.155; 600.121 | Approved |
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| Call Number | Admin @ si @ TKV2023 | Serial | 3825 | ||
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| Author | Souhail Bakkali; Zuheng Ming; Mickael Coustaty; Marçal Rusiñol; Oriol Ramos Terrades | ||||
| Title | VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification | Type | Journal Article | ||
| Year | 2023 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 139 | Issue | Pages | 109419 | |
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| Abstract | Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream approach. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues, considering intra- and inter-modality relationships. Instead of merging features from different modalities into a common representation space, the proposed method exploits high-level interactions and learns relevant semantic information from effective attention flows within and across modalities. The proposed learning objective is devised between intra- and inter-modality alignment tasks, where the similarity distribution per task is computed by contracting positive sample pairs while simultaneously contrasting negative ones in the common feature representation space}. Extensive experiments on public document classification datasets demonstrate the effectiveness and the generalization capacity of our model on both low-scale and large-scale datasets. | ||||
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| ISSN | ISSN 0031-3203 | ISBN | Medium | ||
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| Notes | DAG; 600.140; 600.121 | Approved |
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| Call Number | Admin @ si @ BMC2023 | Serial | 3826 | ||
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| Author | Marco Cotogni; Fei Yang; Claudio Cusano; Andrew Bagdanov; Joost Van de Weijer | ||||
| Title | Gated Class-Attention with Cascaded Feature Drift Compensation for Exemplar-free Continual Learning of Vision Transformers | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Marco Cotogni, Fei Yang, Claudio Cusano, Andrew D. Bagdanov, Joost van de Weijer | ||||
| Abstract | We propose a new method for exemplar-free class incremental training of ViTs. The main challenge of exemplar-free continual learning is maintaining plasticity of the learner without causing catastrophic forgetting of previously learned tasks. This is often achieved via exemplar replay which can help recalibrate previous task classifiers to the feature drift which occurs when learning new tasks. Exemplar replay, however, comes at the cost of retaining samples from previous tasks which for many applications may not be possible. To address the problem of continual ViT training, we first propose gated class-attention to minimize the drift in the final ViT transformer block. This mask-based gating is applied to class-attention mechanism of the last transformer block and strongly regulates the weights crucial for previous tasks. Importantly, gated class-attention does not require the task-ID during inference, which distinguishes it from other parameter isolation methods. Secondly, we propose a new method of feature drift compensation that accommodates feature drift in the backbone when learning new tasks. The combination of gated class-attention and cascaded feature drift compensation allows for plasticity towards new tasks while limiting forgetting of previous ones. Extensive experiments performed on CIFAR-100, Tiny-ImageNet and ImageNet100 demonstrate that our exemplar-free method obtains competitive results when compared to rehearsal based ViT methods. | ||||
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| Notes | LAMP; no proj | Approved |
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| Call Number | Admin @ si @ CYC2022 | Serial | 3827 | ||
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| Author | Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
| Title | A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Continuous Hand Gesture Recognition (CHGR) has been extensively studied by researchers in the last few decades. Recently, one model has been presented to deal with the challenge of the boundary detection of isolated gestures in a continuous gesture video [17]. To enhance the model performance and also replace the handcrafted feature extractor in the presented model in [17], we propose a GCN model and combine it with the stacked Bi-LSTM and Attention modules to push the temporal information in the video stream. Considering the breakthroughs of GCN models for skeleton modality, we propose a two-layer GCN model to empower the 3D hand skeleton features. Finally, the class probabilities of each isolated gesture are fed to the post-processing module, borrowed from [17]. Furthermore, we replace the anatomical graph structure with some non-anatomical graph structures. Due to the lack of a large dataset, including both the continuous gesture sequences and the corresponding isolated gestures, three public datasets in Dynamic Hand Gesture Recognition (DHGR), RKS-PERSIANSIGN, and ASLVID, are used for evaluation. Experimental results show the superiority of the proposed model in dealing with isolated gesture boundaries detection in continuous gesture sequences | ||||
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| Notes | HuPBA; no menciona | Approved |
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| Call Number | Admin @ si @ RKE2022d | Serial | 3828 | ||
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| Author | German Barquero; Sergio Escalera; Cristina Palmero | ||||
| Title | BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction | Type | Conference Article | ||
| Year | 2023 | Publication | IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 2317-2327 | ||
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| Abstract | Stochastic human motion prediction (HMP) has generally been tackled with generative adversarial networks and variational autoencoders. Most prior works aim at predicting highly diverse movements in terms of the skeleton joints’ dispersion. This has led to methods predicting fast and motion-divergent movements, which are often unrealistic and incoherent with past motion. Such methods also neglect contexts that need to anticipate diverse low-range behaviors, or actions, with subtle joint displacements. To address these issues, we present BeLFusion, a model that, for the first time, leverages latent diffusion models in HMP to sample from a latent space where behavior is disentangled from pose and motion. As a result, diversity is encouraged from a behavioral perspective. Thanks to our behavior
coupler’s ability to transfer sampled behavior to ongoing motion, BeLFusion’s predictions display a variety of behaviors that are significantly more realistic than the state of the art. To support it, we introduce two metrics, the Area of the Cumulative Motion Distribution, and the Average Pairwise Distance Error, which are correlated to our definition of realism according to a qualitative study with 126 participants. Finally, we prove BeLFusion’s generalization power in a new cross-dataset scenario for stochastic HMP. |
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| Address | 2-6 October 2023. Paris (France) | ||||
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| Area | Expedition | Conference | ICCV | ||
| Notes | HUPBA; no menciona | Approved |
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| Call Number | Admin @ si @ BEP2023 | Serial | 3829 | ||
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| Author | Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil | ||||
| Title | A benchmark for the evaluation of computational methods for bronchoscopic navigation | Type | Journal Article | ||
| Year | 2022 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal | IJCARS |
| Volume | 17 | Issue | 1 | Pages | |
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| Notes | IAM | Approved |
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| Call Number | Admin @ si @ BSC2022 | Serial | 3832 | ||
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| Author | Antoni Rosell; Sonia Baeza; S. Garcia-Reina; JL. Mate; Ignasi Guasch; I. Nogueira; I. Garcia-Olive; Guillermo Torres; Carles Sanchez; Debora Gil | ||||
| Title | EP01.05-001 Radiomics to Increase the Effectiveness of Lung Cancer Screening Programs. Radiolung Preliminary Results | Type | Journal Article | ||
| Year | 2022 | Publication | Journal of Thoracic Oncology | Abbreviated Journal | JTO |
| Volume | 17 | Issue | 9 | Pages | S182 |
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| Call Number | Admin @ si @ RBG2022b | Serial | 3834 | ||
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| Author | Antoni Rosell; Sonia Baeza; S. Garcia-Reina; JL. Mate; Ignasi Guasch; I. Nogueira; I. Garcia-Olive; Guillermo Torres; Carles Sanchez; Debora Gil | ||||
| Title | Radiomics to increase the effectiveness of lung cancer screening programs. Radiolung preliminary results. | Type | Journal Article | ||
| Year | 2022 | Publication | European Respiratory Journal | Abbreviated Journal | ERJ |
| Volume | 60 | Issue | 66 | Pages | |
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| Notes | IAM | Approved |
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| Call Number | Admin @ si @ RBG2022c | Serial | 3835 | ||
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| Author | Ruben Tito; Dimosthenis Karatzas; Ernest Valveny | ||||
| Title | Hierarchical multimodal transformers for Multipage DocVQA | Type | Journal Article | ||
| Year | 2023 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 144 | Issue | 109834 | Pages | |
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| Abstract | Existing work on DocVQA only considers single-page documents. However, in real applications documents are mostly composed of multiple pages that should be processed altogether. In this work, we propose a new multimodal hierarchical method Hi-VT5, that overcomes the limitations of current methods to process long multipage documents. In contrast to previous hierarchical methods that focus on different semantic granularity (He et al., 2021) or different subtasks (Zhou et al., 2022) used in image classification. Our method is a hierarchical transformer architecture where the encoder learns to summarize the most relevant information of every page and then, the decoder uses this summarized representation to generate the final answer, following a bottom-up approach. Moreover, due to the lack of multipage DocVQA datasets, we also introduce MP-DocVQA, an extension of SP-DocVQA where questions are posed over multipage documents instead of single pages. Through extensive experimentation, we demonstrate that Hi-VT5 is able, in a single stage, to answer the questions and provide the page that contains the answer, which can be used as a kind of explainability measure. | ||||
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| Notes | DAG | Approved |
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| Call Number | Admin @ si @ TKV2023 | Serial | 3836 | ||
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| Author | Yael Tudela; Ana Garcia Rodriguez; Gloria Fernandez Esparrach; Jorge Bernal | ||||
| Title | Towards Fine-Grained Polyp Segmentation and Classification | Type | Conference Article | ||
| Year | 2023 | Publication | Workshop on Clinical Image-Based Procedures | Abbreviated Journal | |
| Volume | 14242 | Issue | Pages | 32-42 | |
| Keywords | Medical image segmentation; Colorectal Cancer; Vision Transformer; Classification | ||||
| Abstract | Colorectal cancer is one of the main causes of cancer death worldwide. Colonoscopy is the gold standard screening tool as it allows lesion detection and removal during the same procedure. During the last decades, several efforts have been made to develop CAD systems to assist clinicians in lesion detection and classification. Regarding the latter, and in order to be used in the exploration room as part of resect and discard or leave-in-situ strategies, these systems must identify correctly all different lesion types. This is a challenging task, as the data used to train these systems presents great inter-class similarity, high class imbalance, and low representation of clinically relevant histology classes such as serrated sessile adenomas.
In this paper, a new polyp segmentation and classification method, Swin-Expand, is introduced. Based on Swin-Transformer, it uses a simple and lightweight decoder. The performance of this method has been assessed on a novel dataset, comprising 1126 high-definition images representing the three main histological classes. Results show a clear improvement in both segmentation and classification performance, also achieving competitive results when tested in public datasets. These results confirm that both the method and the data are important to obtain more accurate polyp representations. |
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| Address | Vancouver; October 2023 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | MICCAIW | ||
| Notes | ISE | Approved |
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| Call Number | Admin @ si @ TGF2023 | Serial | 3837 | ||
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| Author | Benjia Zhou; Zhigang Chen; Albert Clapes; Jun Wan; Yanyan Liang; Sergio Escalera; Zhen Lei; Du Zhang | ||||
| Title | Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining | Type | Conference Article | ||
| Year | 2023 | Publication | IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
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| Abstract | Sign Language Translation (SLT) is a challenging task due to its cross-domain nature, involving the translation of visual-gestural language to text. Many previous methods employ an intermediate representation, i.e., gloss sequences, to facilitate SLT, thus transforming it into a two-stage task of sign language recognition (SLR) followed by sign language translation (SLT). However, the scarcity of gloss-annotated sign language data, combined with the information bottleneck in the mid-level gloss representation, has hindered the further development of the SLT task. To address this challenge, we propose a novel Gloss-Free SLT based on Visual-Language Pretraining (GFSLT-VLP), which improves SLT by inheriting language-oriented prior knowledge from pre-trained models, without any gloss annotation assistance. Our approach involves two stages: (i) integrating Contrastive Language-Image Pre-training (CLIP) with masked self-supervised learning to create pre-tasks that bridge the semantic gap between visual and textual representations and restore masked sentences, and (ii) constructing an end-to-end architecture with an encoder-decoder-like structure that inherits the parameters of the pre-trained Visual Encoder and Text Decoder from the first stage. The seamless combination of these novel designs forms a robust sign language representation and significantly improves gloss-free sign language translation. In particular, we have achieved unprecedented improvements in terms of BLEU-4 score on the PHOENIX14T dataset (>+5) and the CSL-Daily dataset (>+3) compared to state-of-the-art gloss-free SLT methods. Furthermore, our approach also achieves competitive results on the PHOENIX14T dataset when compared with most of the gloss-based methods. | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA; | Approved |
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| Call Number | Admin @ si @ ZCC2023 | Serial | 3839 | ||
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