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"Connected to the people": Social Inclusion & Cohesion in Action through a Cultural Heritage Digital Tool

Proceedings of the ACM on Human-computer Interaction 2023年第CSCW2期7卷 1-37页

作者： Nisi, Valentina Bala, Paulo Cesário, Vanessa James, Stuart Del Bue, Alessio Nunes, Nuno Jardim Instituto Superior Técnico U. Lisbon Lisbon Portugal Visual Geometry and Modelling Lab Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy

Current cultural policies are evolving from social inclusion (removing barriers and promoting equality for participation in culture) to social cohesion (fostering solid bonds between groups despite their differences). Digital interventions can create spaces that promote social inclusion and cohesion. In this paper, we report on the design and evaluation of a cultural heritage and digital storytelling application supporting a participatory approach to culture and hosting society. We evaluate our intervention in three marginalized communities with different social-cultural contexts: migrant women in Barcelona, a community living in a priority neighbourhood in Paris and second and third-generation migrants in Lisbon. Through an analysis of their application use, our findings point at their needs and desires, highlighting how the app can support social inclusion as the first step towards cohesion, but that these are heterogeneous concepts susceptible to nuanced appropriations by the different communities. © 2023 ACM.

关键词： Digital devices

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Self-Supervised Representation Learning for Knee Injury Diagnosis From Magnetic Resonance Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1613-1623页

作者： Manna, Siladittya Bhattacharya, Saumik Pal, Umapada Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata700108 India Indian Institute of Technology Kharagpur Department of Electronics and Electrical Communication Engineering Kharagpur721302 India

In medical image analysis, the cost of acquiring high-quality data and annotation by experts is a barrier in many medical applications. Most of the techniques used are based on a supervised learning framework and require a large amount of annotated data to achieve satisfactory performance. As an alternative, in this article, we propose a self-supervised learning approach for learning the spatial anatomical representations from the frames of magnetic resonance (MR) video clips for the diagnosis of knee medical conditions. The pretext model learns meaningful context-invariant spatial representations. The downstream task in our article is a class-imbalanced multilabel classification. Different experiments show that the features learned by the pretext model provide competitive performance in the downstream task. Moreover, the efficiency and reliability of the proposed pretext model in learning representations of minority classes without applying any strategy toward imbalance in the dataset can be seen from the results. To the best of our knowledge, this work is the first of its kind in showing the effectiveness and reliability of self-supervised learning algorithms in imbalanced multilabel classification tasks on MR scans. © 2020 IEEE.

关键词： Supervised learning

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A new multimodal sentiment analysis for images containing textual information

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Multimedia Tools and Applications 2024年 1-30页

作者： Ahuja, Garvit Alaei, Alireza Pal, Umapada School of Computer Science and Engineering Manipal University Jaipur Jaipur India Faculty of Science and Engineering Southern Cross University Gold Coast Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Multimodal sentiment analysis on images with textual content is a research area aiming to understand the sentiment conveyed by visual and textual elements in the images. While multimodal sentiment analysis on images and text (reviews) has its own challenges, the combination of textual and visual content in the form of images presents new challenges as well as opportunities. In this research work, we proposed a multimodal sentiment analysis method that works on images incorporating textual elements. In the textual sentiment analysis model, we initially employed a recognition system to extract textual data from input images. Our proposed multimodal method is based on transfer learning, considering two pre-trained deep learning models, Xception, and RoBERTa, to extract features from both visual and textual content from multimedia images. We then implemented a fusion strategy to combine these two modalities (Visual Sentiment Analysis (VSA) and Textual Sentiment Analysis (TSA)) to enhance the accuracy of the proposed method and to provide a more comprehensive understanding of sentiment in multimedia content. In addition, we curated a custom dataset comprising images with associated text labels and sentiments. To ensure accurate labels, we conducted human evaluations involving thirty annotators. Our dataset includes images labeled with negative, neutral, and positive sentiments. Experimental results demonstrated the effectiveness of combining visual and textual features for sentiment analysis. The findings from this research hold promising implications for real-world applications, such as sentiment analysis in social media, product reviews, and marketing campaigns, where both images and text play a significant role in conveying emotional context. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Contrastive Learning

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Blind Image Inpainting via Omni-dimensional Gated Attention and Wavelet Queries

Blind Image Inpainting via Omni-dimensional Gated Attention ...

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2023 IEEE/CVF Conference on computer vision and pattern Recognition Workshops, CVPRW 2023

作者： Phutke, Shruti S. Kulkarni, Ashutosh Vipparthi, Santosh Kumar Murala, Subrahmanyam Indian Institute of Technology Ropar Computer Vision and Pattern Recognition Lab Punjab Rupnagar India

ISBN: (纸本)9798350302493

Blind image inpainting is a crucial restoration task that does not demand additional mask information to restore the corrupted regions. Yet, it is a very less explored research area due to the difficulty in discriminating between corrupted and valid regions. There exist very few approaches for blind image inpainting which sometimes fail at producing plausible inpainted images. Since they follow a common practice of predicting the corrupted regions and then inpaint them. To skip the corrupted region prediction step and obtain better results, in this work, we propose a novel end-to-end architecture for blind image inpainting consisting of wavelet query multi-head attention transformer block and the omni-dimensional gated attention. The proposed wavelet query multi-head attention in the transformer block provides encoder features via processed wavelet coefficients as query to the multi-head attention. Further, the proposed omni-dimensional gated attention effectively provides all dimensional attentive features from the encoder to the respective decoder. Our proposed approach is compared numerically and visually with existing state-of-the-art methods for blind image inpainting on different standard datasets. The comparative and ablation studies prove the effectiveness of the proposed approach for blind image inpainting. The testing code is available at : https://***/shrutiphutke/Blind-Omni-Wav-Net © 2023 IEEE.

关键词： Restoration

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HR_BMNet: A boundary perception and multiresolution fusion network for semantic segmentation of remote sensing

HR_BMNet: A boundary perception and multiresolution fusion n...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Chen, Yan Wang, Mengyuan Jiang, Wenxiang Kang, Menglei Wang, Xiaofeng Collaborative Innovation Centre for Computer Vision and Pattern Recognition School of Artificial Intelligence and Big Data Hefei University Hefei230601 China

ISBN: (纸本)9781510681514

The conventional approach for semantic segmentation of remote sensing imagery using encoder-decoder convolutional neural networks relies on the output of prior feature maps sequentially without considering the interactions between neighboring contextual feature maps with multiple resolutions. While the standard HRNet proposal has successfully improved multi-resolution semantic and spatial features to address the aforementioned issues, its lack of emphasis on boundary perception often results in inadequate target segmentation. Furthermore, a frequent occurrence of multiresolution contextual interaction in HRNet leads to the addition of a significant quantity of redundant information and amplifies the complexity of the model. Hence, to tackle the abovementioned issues, we propose a semantic segmentation network identified as HR-BMNet, which incorporates boundary sensitivity and multiple-resolution learning. The idea associated with standard HRNet is adopted as the foundational architecture. We extend novel boundary perception and multi-resolution fusion attention modules, integrating channel attention mechanisms. The strategy provides an ex-tensive optimization of edges and the efficient capture of crucial multi-scale features. During the feature combination stage, the boundary insights are employed to augment the semantic information, thereby mitigating the spatial details loss, enhancing the intra-class semantic consistency, and achieving superior segmentation. The efficacy of the proposed method is validated through comparison and ablation experiments conducted on the ISPRS Vaihingen and CSRSD datasets. Among the experiments conducted, the best ones attained a mean Intersection over Union (mIoU) of 72.11% on the Vaihingen dataset and 89.28% on the CSRSD dataset, respectively. © 2024 SPIE.

关键词： Semantic Segmentation

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Three-dimensional Target Detection Algorithm for Dangerous Goods in CT Security Inspection

Three-dimensional Target Detection Algorithm for Dangerous G...

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2023 International Conference on Algorithm, Imaging Processing, and Machine vision, AIPMV 2023

作者： He, Jingze Guo, Yao Song, Qing Department of Computer Science and Technology Tsinghua University Beijing China Pattern Recognition and Intelligent Vision Laboratory Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781510672444

In this paper, a 3D dangerous goods detection method based on RetinaNet is proposed. This method uses the bidirectional feature pyramid network structure of RetinaNet to extract multi-scale features from point cloud data and trains the system using Focal Loss function to achieve fast and accurate detection of dangerous goods. In addition, in order to improve the detection accuracy, this paper also introduces the 3D region proposal network (3D RPN) and non-maximum suppression (NMS) algorithm. The experimental results show that the proposed method performs well on our self-built CT dataset, with high accuracy and low false positive rate, and is suitable for dangerous goods detection tasks in practical scenarios. © 2024 SPIE.

关键词： computerized tomography

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Re-identification of Saimaa Ringed Seals from Image Sequences 23rd

Re-identification of Saimaa Ringed Seals from Image Sequen...

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22nd Scandinavian Conference on Image Analysis, SCIA 2023

作者： Nepovinnykh, Ekaterina Vilkman, Antti Eerola, Tuomas Kälviäinen, Heikki Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland

ISBN: (纸本)9783031314346

Automatic game cameras are commonly used for monitoring wildlife as they allow to document of the activity of animals in a non-invasive manner. By utilizing a large number of cameras and identifying individual animals from the images, it is possible to, for example, estimate the population size and study the migration patterns of the animals. Large image volumes produced by the cameras call for automated methods for the analysis. Re-identification of animals has commonly been implemented through one-to-one matching, where images are processed individually and the best match is searched from the database of known individuals one by one. Game cameras can be configured to produce a sequence of images that allows capturing the animal from multiple angles potentially improving the re-identification accuracy. In this work, the re-identification of the endangered Saimaa ringed seal (pusa hispida saimensis) from image sequences is studied. The individual identification is realized through Saimaa ringed seal’s unique pelage pattern. The proposed one-to-many and many-to-many matching methods aggregate the pelage pattern features over the whole sequence providing better embeddings for the re-identification tasks. We show that the proposed aggregation method outperforms traditional one-to-one matching based re-identification by a large margin. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Animals

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UformPose: A U-Shaped Hierarchical Multi-Scale Keypoint-Aware Framework for Human Pose Estimation

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IEEE Transactions on Circuits and Systems for Video Technology 2023年第4期33卷 1697-1709页

作者： Wang, You-Jie Luo, Yan-Min Bai, Gui-Hu Guo, Jing-Ming Huaqiao University Key Laboratory for Computer Vision and Pattern Recognition The College of Computer Science and Technology Xiamen361021 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei10607 Taiwan

Human pose estimation is a fundamental yet challenging task in computer vision. However, difficult scenarios such as invisible keypoints, occlusions and small-scale persons are still not well-handed. In this paper, we present a novel pose estimation framework named UformPose which targets to relieve these issues. UformPose has two core designs: Shared Feature Pyramid Stem (SFPS) and U-shaped hierarchical Multi-scale Keypoint-aware Attention Module (U-MKAM). SFPS is a feature pyramid stem with shared mechanism to learn stronger low-level features at the initial stage, and the shared mechanism can facilitate cross-resolution commonality learning. Our U-MKAM attempts to generate high-quality high-resolution representations by integrating all levels of feature representation of the backbone layer by layer. More importantly, we utilize the flexibility of attention operations for keypoint-aware modeling, which explicitly captures and trades-offs the dependencies between keypoints. We empirically demonstrate the effectiveness of our framework through the competitive pose estimation results on the COCO dataset. Extensive experiments and visual analysis on CrowdPose demonstrate the robustness of our model in crowd scenes. © 1991-2012 IEEE.

关键词： computer vision

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Online Evaluation of Textile Yarn Evenness Using Fast Exposure Imaging and Image Processing Techniques 7

Online Evaluation of Textile Yarn Evenness Using Fast Exposu...

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7th International Conference on Automation, Control and Robots, ICACR 2023

作者： Haleem, Noman Bustreo, Matteo Del Bue, Alessio University of Groningen Campus Fryslan Leeuwarden Netherlands Istituto Italiano di Tecnologia Pattern Analysis & Computer Vision Genova Italy

ISBN: (纸本)9798350302882

Evenness is regarded as the most important attribute of textile yarn quality as irregular yarns reduce efficiency of spinning process and deteriorate quality of resultant fabrics. In traditional yarn spinning process, evenness is tested offline using laboratory scale testers through sampling from a whole lot of yarns. The lag associated with this approach limits agility in responding to a variety of process variations whereas limited sampling restricts achieving fine grained yarn quality insights. These factors together result in limited control on yarn spinning process and eventually compromise quality of the yarn *** aim to address this problem by developing a new yarn evenness testing system, which can be deployed on an individual spinning position to evaluate yarn quality in an online manner. The proposed system, which is based on a combination of fast exposure imaging and image processing techniques, was first validated using a control set of yarn specimens and later tested in normal yarn production through comparison with an existing offline yarn evenness tester. The results demonstrate promising potential of the proposed system for wider application in yarn spinning industry. © 2023 IEEE.

关键词： Textile industry

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Low Resource Degraded Quality Document Image Binarization – Domain Adaptation is the Way 22

Low Resource Degraded Quality Document Image Binarization –...

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Proceedings of the Thirteenth Indian Conference on computer vision, Graphics and Image Processing

作者： Ahana Kundu Ujjwal Bhattacharya Computer Vision and Pattern Recognition Indian Statistical Institute IN Computer Vision and Pattern Recognition ISI Kolkata IN

ISBN: (纸本)9781450398220

Usually, image binarization plays a crucial role in automatic analysis of degraded documents from their captured images. However, this binarization task is often difficult due to a number of reasons including the high similarity between noisy background and faded foreground pixels. The study presented here is particularly focused on binarization of images of low-resource degraded quality documents based on a set of recently collected image samples of several rare, ancient and severely degraded quality printed documents of Bangla, the 2nd and 5th most popular script of India and the world respectively. This new collection of degraded document image samples will henceforth be referred as ’ISIDDI2’ and it consists of 139 images of Bangla old document pages. Samples of ’ISIDDI’, another existing database of degraded Bangla document image samples, have also been used in the present study. A novel deep architecture based on attention UNET++ with dilated convolution operation is proposed for this binarization task. The model is optimized using human vision perceptible distance reciprocal distortion (DRD) loss. Since the binarization ground truth of samples of both ’ISIDDI2’ and ’ISIDDI’ are not available, the proposed network has been trained using samples of DIBCO and H-DIBCO datasets and an unsupervised domain adaptation (DA) module is employed for adaptation of the proposed architecture to the degradation patterns of ’ISIDDI2’ or ’ISIDDI’ samples. The proposed binarization strategy includes certain post-processing operation based on a modified k-neighbourhood based approach for recovery of broken characters. Results of our extensive experimentation show that the proposed binarization strategy has improved the binarization output of state-of-the-art methods on both ISIDDI2 and ISIDDI datasets. Also, its performance on well-known DIBCO samples is satisfactory.

关键词： UNET++ degraded document image domain adaptation Image binarization attention

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