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Author | Khanh Nguyen; Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas | ||||
Title | Show, Interpret and Tell: Entity-Aware Contextualised Image Captioning in Wikipedia | Type | Conference Article | ||
Year | 2023 | Publication | Proceedings of the 37th AAAI Conference on Artificial Intelligence | Abbreviated Journal | |
Volume | 37 | Issue | 2 | Pages | 1940-1948 |
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Abstract | Humans exploit prior knowledge to describe images, and are able to adapt their explanation to specific contextual information given, even to the extent of inventing plausible explanations when contextual information and images do not match. In this work, we propose the novel task of captioning Wikipedia images by integrating contextual knowledge. Specifically, we produce models that jointly reason over Wikipedia articles, Wikimedia images and their associated descriptions to produce contextualized captions. The same Wikimedia image can be used to illustrate different articles, and the produced caption needs to be adapted to the specific context allowing us to explore the limits of the model to adjust captions to different contextual information. Dealing with out-of-dictionary words and Named Entities is a challenging task in this domain. To address this, we propose a pre-training objective, Masked Named Entity Modeling (MNEM), and show that this pretext task results to significantly improved models. Furthermore, we verify that a model pre-trained in Wikipedia generalizes well to News Captioning datasets. We further define two different test splits according to the difficulty of the captioning task. We offer insights on the role and the importance of each modality and highlight the limitations of our model. | ||||
Address | Washington; USA; February 2023 | ||||
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Area | Expedition | Conference | AAAI | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ NBM2023 | Serial | 3860 | ||
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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 | Pages | 108997 | |
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Abstract | 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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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ BPF2023 | Serial | 3861 | ||
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Author | Wenjuan Gong; Yue Zhang; Wei Wang; Peng Cheng; Jordi Gonzalez | ||||
Title | Meta-MMFNet: Meta-learning-based Multi-model Fusion Network for Micro-expression Recognition | Type | Journal Article | ||
Year | 2023 | Publication | ACM Transactions on Multimedia Computing, Communications, and Applications | Abbreviated Journal | TMCCA |
Volume | 20 | Issue | 2 | Pages | 1–20 |
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Abstract | Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning-based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. | ||||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ GZW2023 | Serial | 3862 | ||
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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 | Abbreviated Journal | |
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Abstract | 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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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ SBS2023 | Serial | 3863 | ||
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Author | Wenwen Fu; Zhihong An; Wendong Huang; Haoran Sun; Wenjuan Gong; Jordi Gonzalez | ||||
Title | A Spatio-Temporal Spotting Network with Sliding Windows for Micro-Expression Detection | Type | Journal Article | ||
Year | 2023 | Publication | Electronics | Abbreviated Journal | ELEC |
Volume | 12 | Issue | 18 | Pages | 3947 |
Keywords | micro-expression spotting; sliding window; key frame extraction | ||||
Abstract | Micro-expressions reveal underlying emotions and are widely applied in political psychology, lie detection, law enforcement and medical care. Micro-expression spotting aims to detect the temporal locations of facial expressions from video sequences and is a crucial task in micro-expression recognition. In this study, the problem of micro-expression spotting is formulated as micro-expression classification per frame. We propose an effective spotting model with sliding windows called the spatio-temporal spotting network. The method involves a sliding window detection mechanism, combines the spatial features from the local key frames and the global temporal features and performs micro-expression spotting. The experiments are conducted on the CAS(ME)2 database and the SAMM Long Videos database, and the results demonstrate that the proposed method outperforms the state-of-the-art method by 30.58% for the CAS(ME)2 and 23.98% for the SAMM Long Videos according to overall F-scores. | ||||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ FAH2023 | Serial | 3864 | ||
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Author | Luca Ginanni Corradini; Simone Balocco; Luciano Maresca; Silvio Vitale; Matteo Stefanini | ||||
Title | Anatomical Modifications After Stent Implantation: A Comparative Analysis Between CGuard, Wallstent, and Roadsaver Carotid Stents | Type | Journal Article | ||
Year | 2023 | Publication | Journal of Endovascular Therapy | Abbreviated Journal | |
Volume | 30 | Issue | 1 | Pages | 18-24 |
Keywords | Ginanni Corradini L, Balocco S, Maresca L, Vitale S, Stefanini M. | ||||
Abstract | Abstract
Purpose: Carotid revascularization can be associated with modifications of the vascular geometry, which may lead to complications. The changes on the vessel angulation before and after a carotid WallStent (WS) implantation are compared against 2 new dual-layer devices, CGuard (CG) and RoadSaver (RS). Materials and Methods: The study prospectively recruited 217 consecutive patients (112 GC, 73 WS, and 32 RS, respectively). Angiography projections were explored and the one having a higher arterial angle was selected as a basal view. After stent implantation, a stent control angiography was performed selecting the projection having the maximal angle. The same procedure is followed in all the 3 stent types to guarantee comparable conditions. The angulation changes on the stented segments were quantified from both angiographies. The statistical analysis quantitatively compared the pre-and post-angles for the 3 stent types. The results are qualitatively illustrated using boxplots. Finally, the relation between pre- and post-angles measurements is analyzed using linear regression. Results: For CG, no statistical difference in the axial vessel geometry between the basal and postprocedural angles was found. For WS and RS, statistical difference was found between pre- and post-angles. The regression analysis shows that CG induces lower changes from the original curvature with respect to WS and RS. Conclusion: Based on our results, CG determines minor changes over the basal morphology than WS and RS stents. Hence, CG respects better the native vessel anatomy than the other stents. Level of Evidence: Level 4, Case Series. |
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Notes | xxx | Approved | no | ||
Call Number | Admin @ si @ GBM2023 | Serial | 4006 | ||
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Author | Maciej Wielgosz; Antonio Lopez; Muhamad Naveed Riaz | ||||
Title | CARLA-BSP: a simulated dataset with pedestrians | Type | Miscellaneous | ||
Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | 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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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ WLN2023 | Serial | 3866 | ||
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Author | Akhil Gurram; Antonio Lopez | ||||
Title | On the Metrics for Evaluating Monocular Depth Estimation | Type | Miscellaneous | ||
Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | 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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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ GuL2023 | Serial | 3867 | ||
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Author | David Pujol Perich; Albert Clapes; Sergio Escalera | ||||
Title | SADA: Semantic adversarial unsupervised domain adaptation for Temporal Action Localization | Type | Miscellaneous | ||
Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | 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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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ PCE2023 | Serial | 4014 | ||
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Author | Hao Fang; Ajian Liu; Jun Wan; Sergio Escalera; Chenxu Zhao; Xu Zhang; Stan Z Li; Zhen Lei | ||||
Title | Surveillance Face Anti-spoofing | Type | Journal Article | ||
Year | 2024 | Publication | IEEE Transactions on Information Forensics and Security | Abbreviated Journal | TIFS |
Volume | 19 | Issue | Pages | 1535-1546 | |
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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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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ FLW2024 | Serial | 3869 | ||
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Author | 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 | Type | Miscellaneous | ||
Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | 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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Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ LWH2023 | Serial | 3870 | ||
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Author | 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 | Abbreviated Journal | |
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Abstract | 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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Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ CCL2023 | Serial | 3871 | ||
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Author | Marcos V Conde; Javier Vazquez; Michael S Brown; Radu TImofte | ||||
Title | NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement | Type | Conference Article | ||
Year | 2024 | Publication | 38th AAAI Conference on Artificial Intelligence | Abbreviated Journal | |
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Abstract | 3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipeline. Cameras typically provide multiple options for picture styles, where each style is usually obtained by applying a unique handcrafted 3D LUT. Current approaches for learning and applying 3D LUTs are notably fast, yet not so memory-efficient, as storing multiple 3D LUTs is required. For this reason and other implementation limitations, their use on mobile devices is less popular. In this work, we propose a Neural Implicit LUT (NILUT), an implicitly defined continuous 3D color transformation parameterized by a neural network. We show that NILUTs are capable of accurately emulating real 3D LUTs. Moreover, a NILUT can be extended to incorporate multiple styles into a single network with the ability to blend styles implicitly. Our novel approach is memory-efficient, controllable and can complement previous methods, including learned ISPs. | ||||
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Area | Expedition | Conference | AAAI | ||
Notes | CIC; MACO | Approved | no | ||
Call Number | Admin @ si @ CVB2024 | Serial | 3872 | ||
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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 | Type | Journal Article | ||
Year | 2023 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 234 | Issue | 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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Notes | LAMP; MACO | Approved | no | ||
Call Number | Admin @ si @ YWH2023 | Serial | 3874 | ||
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Author | Chengyi Zou; Shuai Wan; Tiannan Ji; Marc Gorriz Blanch; Marta Mrak; Luis Herranz | ||||
Title | Chroma Intra Prediction with Lightweight Attention-Based Neural Networks | Type | Journal Article | ||
Year | 2023 | Publication | IEEE Transactions on Circuits and Systems for Video Technology | Abbreviated Journal | TCSVT |
Volume | 34 | Issue | 1 | Pages | 549 - 560 |
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Abstract | Neural networks can be successfully used for cross-component prediction in video coding. In particular, attention-based architectures are suitable for chroma intra prediction using luma information because of their capability to model relations between difierent channels. However, the complexity of such methods is still very high and should be further reduced, especially for decoding. In this paper, a cost-effective attention-based neural network is designed for chroma intra prediction. Moreover, with the goal of further improving coding performance, a novel approach is introduced to utilize more boundary information effectively. In addition to improving prediction, a simplification methodology is also proposed to reduce inference complexity by simplifying convolutions. The proposed schemes are integrated into H.266/Versatile Video Coding (VVC) pipeline, and only one additional binary block-level syntax flag is introduced to indicate whether a given block makes use of the proposed method. Experimental results demonstrate that the proposed scheme achieves up to −0.46%/−2.29%/−2.17% BD-rate reduction on Y/Cb/Cr components, respectively, compared with H.266/VVC anchor. Reductions in the encoding and decoding complexity of up to 22% and 61%, respectively, are achieved by the proposed scheme with respect to the previous attention-based chroma intra prediction method while maintaining coding performance. | ||||
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Notes | MACO; LAMP | Approved | no | ||
Call Number | Admin @ si @ ZWJ2023 | Serial | 3875 | ||
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