Zobrazeno 1 - 10
of 24 510
pro vyhledávání: '"Cigdem, A."'
Autor:
Appiani, Andrea, Beyan, Cigdem
Voice Activity Detection (VAD) is the process of automatically determining whether a person is speaking and identifying the timing of their speech in an audiovisual data. Traditionally, this task has been tackled by processing either audio signals or
Externí odkaz:
http://arxiv.org/abs/2410.14509
Gaze target detection aims at determining the image location where a person is looking. While existing studies have made significant progress in this area by regressing accurate gaze heatmaps, these achievements have largely relied on access to exten
Externí odkaz:
http://arxiv.org/abs/2409.18561
Gaze Target Detection (GTD), i.e., determining where a person is looking within a scene from an external viewpoint, is a challenging task, particularly in 3D space. Existing approaches heavily rely on analyzing the person's appearance, primarily focu
Externí odkaz:
http://arxiv.org/abs/2409.17886
Autor:
Tur, Anil Osman, Conti, Alessandro, Beyan, Cigdem, Boscaini, Davide, Larcher, Roberto, Messelodi, Stefano, Poiesi, Fabio, Ricci, Elisa
In smart retail applications, the large number of products and their frequent turnover necessitate reliable zero-shot object classification methods. The zero-shot assumption is essential to avoid the need for re-training the classifier every time a n
Externí odkaz:
http://arxiv.org/abs/2409.14963
Segmenting multiple objects (e.g., organs) in medical images often requires an understanding of their topology, which simultaneously quantifies the shape of the objects and their positions relative to each other. This understanding is important for s
Externí odkaz:
http://arxiv.org/abs/2408.08038
The introduction of the Transformer neural network, along with techniques like self-supervised pre-training and transfer learning, has paved the way for advanced models like BERT. Despite BERT's impressive performance, opportunities for further enhan
Externí odkaz:
http://arxiv.org/abs/2407.00648
Autor:
Zhang, Chaojie, Chen, Shengjia, Cigdem, Ozkan, Rajamohan, Haresh Rengaraj, Cho, Kyunghyun, Kijowski, Richard, Deniz, Cem M.
A transformer-based deep learning model, MR-Transformer, was developed for total knee replacement (TKR) prediction using magnetic resonance imaging (MRI). The model incorporates the ImageNet pre-training and captures three-dimensional (3D) spatial co
Externí odkaz:
http://arxiv.org/abs/2405.02784
Autor:
Cigdem, Ozkan, Chen, Shengjia, Zhang, Chaojie, Cho, Kyunghyun, Kijowski, Richard, Deniz, Cem M.
A survival analysis model for predicting time-to-total knee replacement (TKR) was developed using features from medical images and clinical measurements. Supervised and self-supervised deep learning approaches were utilized to extract features from r
Externí odkaz:
http://arxiv.org/abs/2405.00069
Autor:
Vidgen, Bertie, Agrawal, Adarsh, Ahmed, Ahmed M., Akinwande, Victor, Al-Nuaimi, Namir, Alfaraj, Najla, Alhajjar, Elie, Aroyo, Lora, Bavalatti, Trupti, Bartolo, Max, Blili-Hamelin, Borhane, Bollacker, Kurt, Bomassani, Rishi, Boston, Marisa Ferrara, Campos, Siméon, Chakra, Kal, Chen, Canyu, Coleman, Cody, Coudert, Zacharie Delpierre, Derczynski, Leon, Dutta, Debojyoti, Eisenberg, Ian, Ezick, James, Frase, Heather, Fuller, Brian, Gandikota, Ram, Gangavarapu, Agasthya, Gangavarapu, Ananya, Gealy, James, Ghosh, Rajat, Goel, James, Gohar, Usman, Goswami, Sujata, Hale, Scott A., Hutiri, Wiebke, Imperial, Joseph Marvin, Jandial, Surgan, Judd, Nick, Juefei-Xu, Felix, Khomh, Foutse, Kailkhura, Bhavya, Kirk, Hannah Rose, Klyman, Kevin, Knotz, Chris, Kuchnik, Michael, Kumar, Shachi H., Kumar, Srijan, Lengerich, Chris, Li, Bo, Liao, Zeyi, Long, Eileen Peters, Lu, Victor, Luger, Sarah, Mai, Yifan, Mammen, Priyanka Mary, Manyeki, Kelvin, McGregor, Sean, Mehta, Virendra, Mohammed, Shafee, Moss, Emanuel, Nachman, Lama, Naganna, Dinesh Jinenhally, Nikanjam, Amin, Nushi, Besmira, Oala, Luis, Orr, Iftach, Parrish, Alicia, Patlak, Cigdem, Pietri, William, Poursabzi-Sangdeh, Forough, Presani, Eleonora, Puletti, Fabrizio, Röttger, Paul, Sahay, Saurav, Santos, Tim, Scherrer, Nino, Sebag, Alice Schoenauer, Schramowski, Patrick, Shahbazi, Abolfazl, Sharma, Vin, Shen, Xudong, Sistla, Vamsi, Tang, Leonard, Testuggine, Davide, Thangarasa, Vithursan, Watkins, Elizabeth Anne, Weiss, Rebecca, Welty, Chris, Wilbers, Tyler, Williams, Adina, Wu, Carole-Jean, Yadav, Poonam, Yang, Xianjun, Zeng, Yi, Zhang, Wenhui, Zhdanov, Fedor, Zhu, Jiacheng, Liang, Percy, Mattson, Peter, Vanschoren, Joaquin
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introdu
Externí odkaz:
http://arxiv.org/abs/2404.12241
Autor:
Alameda-Pineda, Xavier, Addlesee, Angus, García, Daniel Hernández, Reinke, Chris, Arias, Soraya, Arrigoni, Federica, Auternaud, Alex, Blavette, Lauriane, Beyan, Cigdem, Camara, Luis Gomez, Cohen, Ohad, Conti, Alessandro, Dacunha, Sébastien, Dondrup, Christian, Ellinson, Yoav, Ferro, Francesco, Gannot, Sharon, Gras, Florian, Gunson, Nancie, Horaud, Radu, D'Incà, Moreno, Kimouche, Imad, Lemaignan, Séverin, Lemon, Oliver, Liotard, Cyril, Marchionni, Luca, Moradi, Mordehay, Pajdla, Tomas, Pino, Maribel, Polic, Michal, Py, Matthieu, Rado, Ariel, Ren, Bin, Ricci, Elisa, Rigaud, Anne-Sophie, Rota, Paolo, Romeo, Marta, Sebe, Nicu, Sieińska, Weronika, Tandeitnik, Pinchas, Tonini, Francesco, Turro, Nicolas, Wintz, Timothée, Yu, Yanchao
Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation of such systems by end-users is necessary. While several robotic platfo
Externí odkaz:
http://arxiv.org/abs/2404.07560