Zobrazeno 1 - 10
of 192 715
pro vyhledávání: '"Collaborative approach"'
Human-in-the-loop Reasoning For Traffic Sign Detection: Collaborative Approach Yolo With Video-llava
Autor:
Azarafza, Mehdi, Idrees, Fatima, Bejnordi, Ali Ehteshami, Steinmetz, Charles, Henkler, Stefan, Rettberg, Achim
Traffic Sign Recognition (TSR) detection is a crucial component of autonomous vehicles. While You Only Look Once (YOLO) is a popular real-time object detection algorithm, factors like training data quality and adverse weather conditions (e.g., heavy
Externí odkaz:
http://arxiv.org/abs/2410.05096
Patients with chronic diseases or people with special health care needs are typically monitored by various health experts that address the problem from several perspectives. These experts usually do not interact directly between them; therefore, the
Externí odkaz:
http://arxiv.org/abs/2410.10837
Autor:
Dastan, Mine, Fiorentino, Michele, Walter, Elias D., Diegritz, Christian, Uva, Antonio E., Eck, Ulrich, Navab, Nassir
Publikováno v:
IEEE Transactions on Visualization and Computer Graphics 2024
Mixed Reality (MR) is proven in the literature to support precise spatial dental drill positioning by superimposing 3D widgets. Despite this, the related knowledge about widget's visual design and interactive user feedback is still limited. Therefore
Externí odkaz:
http://arxiv.org/abs/2409.10258
Autor:
Baby, Dheeraj, Pal, Soumyabrata
We investigate the low rank matrix completion problem in an online setting with ${M}$ users, ${N}$ items, ${T}$ rounds, and an unknown rank-$r$ reward matrix ${R}\in \mathbb{R}^{{M}\times {N}}$. This problem has been well-studied in the literature an
Externí odkaz:
http://arxiv.org/abs/2408.05843
Cross-document event coreference resolution (CDECR) involves clustering event mentions across multiple documents that refer to the same real-world events. Existing approaches utilize fine-tuning of small language models (SLMs) like BERT to address th
Externí odkaz:
http://arxiv.org/abs/2406.02148
Human-AI collaboration to identify and correct perceptual errors in chest radiographs has not been previously explored. This study aimed to develop a collaborative AI system, CoRaX, which integrates eye gaze data and radiology reports to enhance diag
Externí odkaz:
http://arxiv.org/abs/2406.19686