المؤلفون: Bousselham, Walid, Boggust, Angie, Chaybouti, Sofian, Strobelt, Hendrik, Kuehne, Hilde

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Vision Transformers (ViTs), with their ability to model long-range dependencies through self-attention mechanisms, have become a standard architecture in computer vision. However, the interpretability of these models remains a challenge. To address this, we propose LeGrad, an explainability method specifically designed for ViTs. LeGrad computes the gradient with respect to the attention maps of ViT layers, considering the gradient itself as the explainability signal. We aggregate the signal over all layers, combining the activations of the last as well as intermediate tokens to produce the merged explainability map. This makes LeGrad a conceptually simple and an easy-to-implement tool for enhancing the transparency of ViTs. We evaluate LeGrad in challenging segmentation, perturbation, and open-vocabulary settings, showcasing its versatility compared to other SotA explainability methods demonstrating its superior spatial fidelity and robustness to perturbations. A demo and the code is available at https://github.com/WalBouss/LeGradTest.
Comment: Code available at https://github.com/WalBouss/LeGradTest

الوصول الحر: http://arxiv.org/abs/2404.03214Test

المؤلفون: Bousselham, Walid, Petersen, Felix, Ferrari, Vittorio, Kuehne, Hilde

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence

الوصف: Vision-language foundation models have shown remarkable performance in various zero-shot settings such as image retrieval, classification, or captioning. But so far, those models seem to fall behind when it comes to zero-shot localization of referential expressions and objects in images. As a result, they need to be fine-tuned for this task. In this paper, we show that pretrained vision-language (VL) models allow for zero-shot open-vocabulary object localization without any fine-tuning. To leverage those capabilities, we propose a Grounding Everything Module (GEM) that generalizes the idea of value-value attention introduced by CLIPSurgery to a self-self attention path. We show that the concept of self-self attention corresponds to clustering, thus enforcing groups of tokens arising from the same object to be similar while preserving the alignment with the language space. To further guide the group formation, we propose a set of regularizations that allows the model to finally generalize across datasets and backbones. We evaluate the proposed GEM framework on various benchmark tasks and datasets for semantic segmentation. It shows that GEM not only outperforms other training-free open-vocabulary localization methods, but also achieves state-of-the-art results on the recently proposed OpenImagesV7 large-scale segmentation benchmark.
Comment: Code available at https://github.com/WalBouss/GEMTest

الوصول الحر: http://arxiv.org/abs/2312.00878Test

المؤلفون: Khan, Aisha Urooj, Kuehne, Hilde, Wu, Bo, Chheu, Kim, Bousselham, Walid, Gan, Chuang, Lobo, Niels, Shah, Mubarak

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Answering questions about complex situations in videos requires not only capturing the presence of actors, objects, and their relations but also the evolution of these relationships over time. A situation hyper-graph is a representation that describes situations as scene sub-graphs for video frames and hyper-edges for connected sub-graphs and has been proposed to capture all such information in a compact structured form. In this work, we propose an architecture for Video Question Answering (VQA) that enables answering questions related to video content by predicting situation hyper-graphs, coined Situation Hyper-Graph based Video Question Answering (SHG-VQA). To this end, we train a situation hyper-graph decoder to implicitly identify graph representations with actions and object/human-object relationships from the input video clip. and to use cross-attention between the predicted situation hyper-graphs and the question embedding to predict the correct answer. The proposed method is trained in an end-to-end manner and optimized by a VQA loss with the cross-entropy function and a Hungarian matching loss for the situation graph prediction. The effectiveness of the proposed architecture is extensively evaluated on two challenging benchmarks: AGQA and STAR. Our results show that learning the underlying situation hyper-graphs helps the system to significantly improve its performance for novel challenges of video question-answering tasks.

الوصول الحر: http://arxiv.org/abs/2304.08682Test

المؤلفون: Bousselham, Walid, Thibault, Guillaume, Pagano, Lucas, Machireddy, Archana, Gray, Joe, Chang, Young Hwan, Song, Xubo

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Ensemble of predictions is known to perform better than individual predictions taken separately. However, for tasks that require heavy computational resources, e.g. semantic segmentation, creating an ensemble of learners that needs to be trained separately is hardly tractable. In this work, we propose to leverage the performance boost offered by ensemble methods to enhance the semantic segmentation, while avoiding the traditional heavy training cost of the ensemble. Our self-ensemble approach takes advantage of the multi-scale features set produced by feature pyramid network methods to feed independent decoders, thus creating an ensemble within a single model. Similar to the ensemble, the final prediction is the aggregation of the prediction made by each learner. In contrast to previous works, our model can be trained end-to-end, alleviating the traditional cumbersome multi-stage training of ensembles. Our self-ensemble approach outperforms the current state-of-the-art on the benchmark datasets Pascal Context and COCO-Stuff-10K for semantic segmentation and is competitive on ADE20K and Cityscapes. Code is publicly available at github.com/WalBouss/SenFormer.
Comment: Code available at https://github.com/WalBouss/SenFormerTest

الوصول الحر: http://arxiv.org/abs/2111.13280Test

المؤلفون: Pagano, Lucas, Thibault, Guillaume, Bousselham, Walid, Riesterer, Jessica L., Song, Xubo, Gray, Joe W.

المصدر: Frontiers in Bioinformatics ; volume 3 ; ISSN 2673-7647

الوصف: Electron microscopy (EM) enables imaging at a resolution of nanometers and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task.However, analyzing them is now a bottleneck, as manual structure identification is very time-consuming and can take up to several months for a single sample. Deep learning approaches offer a suitable solution to speed up the analysis. In this work, we present a study of several state-of-the-art deep learning models for the task of segmenting nuclei and nucleoli in volumes from tumor biopsies. We compared previous results obtained with the ResUNet architecture to the more recent UNet++, FracTALResNet, SenFormer, and CEECNet models. In addition, we explored the utilization of unlabeled images through semi-supervised learning with Cross Pseudo Supervision. We have trained and evaluated all of the models on sparse manual labels from three fully annotated in-house datasets that we have made available on demand, demonstrating improvements in terms of 3D Dice score. From the analysis of these results, we drew conclusions on the relative gains of using more complex models, and semi-supervised learning as well as the next steps for the mitigation of the manual segmentation bottleneck.

الإتاحة: https://doi.org/10.3389/fbinf.2023.1308707Test

المؤلفون: Potortì, Francesco, Park, Sangjoon, Crivello, Antonino, Palumbo, Filippo, GIROLAMI, MICHELE, Barsocchi, Paolo, Lee, Soyeon, Torres-Sospedra, Joaquín, Jimenez Ruiz, Antonio Ramon, Perez-Navarro, Antoni, Mendoza-Silva, Germán Martín, Seco, Fernando, ortiz, miguel, PERUL, Johan, Renaudin, Valerie, Kang, Hyunwoong, Park, Soyoung, Lee, Jae Hong, Park, Chan Gook, Ha, Jisu, Han, JaeSeung, Park, Changjun, Kim, Keunhye, Lee, Yonghyun, Gye, Seunghun, lee, keumryeol, Kim, Eunjee, Choi, Jeongsik, choi, Yang-Seok, Talwar, Shilpa, Cho, Seong Yun, Ben-Moshe, Boaz, Scherbakov, Alex, Antsfeld, Leonid, Sansano-Sansano, Emilio, Chidlovskii, Boris, Kronenwett, Nikolai, Prophet, Silvia, Landay, Yael, Marbel, Revital, Zheng, Lingxiang, Peng, Ao, Lin, Zhichao, Wu, Bang, Ma, Chengqi, Poslad, Stefan, Selviah, David, Wu, Wei, Ma, Zixiang, Zhang, Wenchao, Wei, Dongyan, Yuan, Hong, Jiang, Jun-Bang, Huang, Shao-Yung, Liu, Jing-Wen, Su, Kuan-Wu, Leu, Jenq-Shiou, Nishiguchi, Kazuki, BOUSSELHAM, Walid, Uchiyama, Hideaki, Thomas, Diego, Shimada, Atsushi, Taniguchi, Rin-ichiro, Cortés Puschel, Vicente, Lungenstrass, Tomás, Ashraf, Imran, Lee, Chanseok, Ali, Muhammad Usman, Im, Yeongjun, Kim, Gunzung, Eom, Jeongsook, Hur, Soojung, Park, Yongwan, Opiela, Miroslav, Moreira, Adriano, Nicolau, Maria João, Pendão, Cristiano, Silva, Ivo, Meneses, Filipe, Costa, António, Trogh, Jens, Plets, David, Chien, Ying-Ren, Chang, Tzu-Yu, Fang, Shih-Hau, Tsao, Yu

مصطلحات موضوعية: indoor localisation, indoor navigation, competition, benchmarking, smartphone-based positioning, foot-mounted pedestrian dead reckoning, Wi-Fi fingerprinting, magnetic field, camera-based positioning, inertial-based positioning, sensor fusion, Kalman filter, particle filter, stakeholders, atmospheric measurements, particle measurements, real-time systems, wireless fidelity, sensor systems

الوصف: IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks.

وصف الملف: application/pdf

العلاقة: Development of human enhancement fire helmet and fire suppression support system; Basic Science Research Program; ICT Research and Development Program of MSIP/IITP (Development of Precise Positioning Technology for the Enhancement of Pedestrian Position/Spatial Cognition and Sports Competition Analysis); MICROCEBUS; REPNIN PLUS; TECHNOFUSION(III)CM; Development of wireless communication tracking-based location information system in disaster scene for fire-fighters and person who requested rescue; Strategic Priority Research Program; IEEE Access, Vol. 8 (2020); F. Potortì et al., "The IPIN 2019 Indoor Localisation Competition—Description and Results," in IEEE Access, vol. 8, pp. 206674-206718, 2020, doi:10.1109/ACCESS.2020.3037221.; http://hdl.handle.net/10234/200929Test

الإتاحة: https://doi.org/10.1109/ACCESS.2020.3037221Test
http://hdl.handle.net/10234/200929Test

المؤلفون: Potorti, Francesco, Park, Sangjoon, Crivello, Antonino, Palumbo, Filippo, Girolami, Michele, Barsocchi, Paolo, Lee, Soyeon, Torres-Sospedra, Joaquin, Ruiz, Antonio Ramon Jimenez, Perez-Navarro, Antoni, Mendoza-Silva, German Martin, Seco, Fernando, Ortiz, Miguel, Perul, Johan, Renaudin, Valerie, Kang, Hyunwoong, Park, Soyoung, Lee, Jae Hong, Park, Chan Gook, Ha, Jisu, Han, Jaeseung, Park, Changjun, Kim, Keunhye, Lee, Yonghyun, Gye, Seunghun, Lee, Keumryeol, Kim, Eunjee, Choi, Jeong-Sik, Choi, Yang-Seok, Talwar, Shilpa, Cho, Seong Yun, Ben-Moshe, Boaz, Scherbakov, Alex, Antsfeld, Leonid, Sansano-Sansano, Emilio, Chidlovskii, Boris, Kronenwett, Nikolai, Prophet, Silvia, Landay, Yael, Marbel, Revital, Zheng, Lingxiang, Peng, Ao, Lin, Zhichao, Wu, Bang, Ma, Chengqi, Poslad, Stefan, Selviah, David R., Wu, Wei, Ma, Zixiang, Zhang, Wenchao, Wei, Dongyan, Yuan, Hong, Jiang, Jun-Bang, Huang, Shao-Yung, Liu, Jing-Wen, Su, Kuan-Wu, Leu, Jenq-Shiou, Nishiguchi, Kazuki, Bousselham, Walid, Uchiyama, Hideaki, Thomas, Diego, Shimada, Atsushi, Taniguchi, Rin-Ichiro, Puschel, Vicente Cortes, Poulsen, Tomas Lungenstrass, Ashraf, Imran, Lee, Chanseok, Ali, Muhammad Usman, Im, Yeongjun, Kim, Gunzung, Eom, Jeongsook, Hur, Soojung, Park, Yongwan, Opiela, Miroslav, Moreira, Adriano, Nicolau, Maria Joao, Pendao, Cristiano, Silva, Ivo, Meneses, Filipe, Costa, Antonio, Trogh, Jens, Plets, David, Chien, Ying-Ren, Chang, Tzu-Yu, Fang, Shih-Hau, Tsao, Yu

المصدر: IEEE access, 8, 206674–206718 ; ISSN: 2169-3536

مصطلحات موضوعية: ddc:620, Engineering & allied operations, info:eu-repo/classification/ddc/620

الوصف: PIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks.

وصف الملف: application/pdf

العلاقة: info:eu-repo/semantics/altIdentifier/wos/000594446900001; info:eu-repo/semantics/altIdentifier/issn/2169-3536; https://publikationen.bibliothek.kit.edu/1000130123Test; https://publikationen.bibliothek.kit.edu/1000130123/104023450Test; https://doi.org/10.5445/IR/1000130123Test

الإتاحة: https://doi.org/10.5445/IR/1000130123Test
https://doi.org/10.1109/ACCESS.2020.3037221Test
https://publikationen.bibliothek.kit.edu/1000130123Test
https://publikationen.bibliothek.kit.edu/1000130123/104023450Test

المؤلفون: Potorti, Francesco, Park, Sangjoon, Crivello, Antonino, Palumbo, Filippo, Girolami, Michele, Barsocchi, Paolo, Lee, Soyeon, Torres-Sospedra, Joaquín, Ruiz, Antonio Ramon Jimenez, Perez-Navarro, Antoni, Mendoza-Silva, German Martin, Seco, Fernando, Ortiz, Miguel, Perul, Johan, Renaudin, Valerie, Kang, Hyunwoong, Park, Soyoung, Lee, Jae Hong, Park, Chan Gook, Ha, Jisu, Han, Jaeseung, Park, Changjun, Kim, Keunhye, Lee, Yonghyun, Gye, Seunghun, Lee, Keumryeol, Kim, Eunjee, Choi, Jeong Sik, Choi, Yang Seok, Talwar, Shilpa, Cho, Seong Yun, Ben-Moshe, Boaz, Scherbakov, Alex, Antsfeld, Leonid, Sansano-Sansano, Emilio, Chidlovskii, Boris, Kronenwett, Nikolai, Prophet, Silvia, Landay, Yael, Marbel, Revital, Zheng, Lingxiang, Peng, Ao, Lin, Zhichao, Wu, Bang, Ma, Chengqi, Poslad, Stefan, Selviah, David R., Wu, Wei, Ma, Zixiang, Zhang, Wenchao, Wei, Dongyan, Yuan, Hong, Jiang, Jun-Bang, Huang, Shao-Yung, Liu, Jing-Wen, Su, Kuan-Wu, Nishiguchi, Kazuki, Bousselham, Walid, Uchiyama, Hideaki, Thomas, Diego, Shimada, Atsushi, Taniguchi, Rin-Ichiro, Cortes Puschel, Vicente, Lungenstrass Poulsen, Tomas, Ashraf, Imran, Lee, Chanseok, Ali, Muhammad Usman, Im, Yeongjun, Kim, Gunzung, Eom, Jeongsook, Hur, Soojung, Park, Yongwan, Opiela, Miroslav, Moreira, Adriano, Nicolau, Maria João, Pendão, Cristiano Gonçalves, Silva, Ivo Miguel Menezes, Meneses, Filipe, Costa, António, Trogh, Jens, Plets, David, Chien, Ying-Ren, Chang, Tzu-Yu, Fang, Shih-Hau, Tsao, Yu

مصطلحات موضوعية: benchmarking, camera-based positioning, competition, foot-mounted pedestrian dead reckoning, Indoor localisation, indoor navigation, inertial-based positioning, Kalman filter, magnetic field, particle filter, sensor fusion, smartphone-based positioning, Wi-Fi fingerprinting, Sensors, Wireless fidelity, Sensor systems, Real-time systems, Particle measurements, Atmospheric measurements, Stakeholders, Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática, Science & Technology

الوصف: IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m2 outdoors and and 6000 m2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks. ; The authors would like to thank Siemens, the Electronic and Telecommunications Research Institute (ETRI), the Korean Institute of Communications and Information Sciences (KICS), Ganko Food, J-Power Systems, and the other sponsors who provided prizes for the winners of the competition (see http://ipin-conference.org/2019/awards.htmlTest for a complete list).

وصف الملف: application/pdf

العلاقة: https://ieeexplore.ieee.org/document/9253514Test; F. Potortì et al., "The IPIN 2019 Indoor Localisation Competition—Description and Results," in IEEE Access, vol. 8, pp. 206674-206718, 2020, doi:10.1109/ACCESS.2020.3037221; http://hdl.handle.net/1822/70552Test

الإتاحة: https://doi.org/10.1109/ACCESS.2020.3037221Test
http://hdl.handle.net/1822/70552Test

المؤلفون: BOUSSELHAM WALID

المساهمون: STATISTICS & APPLIED PROBABILITY, Jialiang Li

مصطلحات موضوعية: Deep Learning, Machine Learning, healthcare, gastroenterology

الوصف: Master's ; MASTER OF SCIENCE (RSH-FOS)

العلاقة: BOUSSELHAM WALID (2020-12-21). Deep Learning For Automated Real-Time Detection And Segmentation Of Intestinal Lesions In Colonoscopies. ScholarBank@NUS Repository.; https://scholarbank.nus.edu.sg/handle/10635/185986Test; orcid:0000-0002-9684-523X

الإتاحة: https://scholarbank.nus.edu.sg/handle/10635/185986Test

المؤلفون: Potorti, Francesco, Park, Sangjoon, Crivello, Antonino, Palumbo, Filippo, Girolami, Michele, Barsocchi, Paolo, Lee, Soyeon, Torres-Sospedra, Joaquin, Ruiz, Antonio Ramon Jimenez, Perez-Navarro, Antoni, Mendoza-Silva, German Martin, Seco, Fernando, Ortiz, Miguel, Perul, Johan, Renaudin, Valerie, Kang, Hyunwoong, Park, Soyoung, Lee, Jae Hong, Park, Chan Gook, Ha, Jisu, Han, Jaeseung, Park, Changjun, Kim, Keunhye, Lee, Yonghyun, Gye, Seunghun, Lee, Keumryeol, Kim, Eunjee, Choi, Jeong-Sik, Choi, Yang-Seok, Talwar, Shilpa, Cho, Seong Yun, Ben-Moshe, Boaz, Scherbakov, Alex, Antsfeld, Leonid, Sansano-Sansano, Emilio, Chidlovskii, Boris, Kronenwett, Nikolai, Prophet, Silvia, Landay, Yael, Marbel, Revital, Zheng, Lingxiang, Peng, Ao, Lin, Zhichao, Wu, Bang, Ma, Chengqi, Poslad, Stefan, Selviah, David R., Wu, Wei, Ma, Zixiang, Zhang, Wenchao, Wei, Dongyan, Yuan, Hong, Jiang, Jun-Bang, Huang, Shao-Yung, Liu, Jing-Wen, Su, Kuan-Wu, Leu, Jenq-Shiou, Nishiguchi, Kazuki, Bousselham, Walid, Uchiyama, Hideaki, Thomas, Diego, Shimada, Atsushi, Taniguchi, Rin-Ichiro, Puschel, Vicente Cortes, Poulsen, Tomas Lungenstrass, Ashraf, Imran, Lee, Chanseok, Ali, Muhammad Usman, Im, Yeongjun, Kim, Gunzung, Eom, Jeongsook, Hur, Soojung, Park, Yongwan, Opiela, Miroslav, Moreira, Adriano, Nicolau, Maria Joao, Pendao, Cristiano, Silva, Ivo, Meneses, Filipe, Costa, Antonio, Trogh, Jens, Plets, David, Chien, Ying-Ren, Chang, Tzu-Yu, Fang, Shih-Hau, Tsao, Yu

مصطلحات موضوعية: 4. Education

الوصف: PIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::426efda1e20144e2838544fd6f6c0f30Test