Zobrazeno 1 - 10
of 9 686
pro vyhledávání: '"A. A. Bulat"'
The annotation of patient organs is a crucial part of various diagnostic and treatment procedures, such as radiotherapy planning. Manual annotation is extremely time-consuming, while its automation using modern image analysis techniques has not yet r
Externí odkaz:
http://arxiv.org/abs/2410.17920
Despite recent success in discriminative approaches in monocular depth estimation its quality remains limited by training datasets. Generative approaches mitigate this issue by leveraging strong priors derived from training on internet-scale datasets
Externí odkaz:
http://arxiv.org/abs/2409.15010
Publikováno v:
Fuel 379 (2025) 133018
The viscosity of crude oil is an important physical property that largely determines the fluidity of oil and its ability to seep through porous media such as geological rock. Predicting crude oil viscosity requires the development of reliable models
Externí odkaz:
http://arxiv.org/abs/2409.05917
Autor:
Ghaleb, Esam, Khaertdinov, Bulat, Pouw, Wim, Rasenberg, Marlou, Holler, Judith, Özyürek, Aslı, Fernández, Raquel
Publikováno v:
INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (ICMI 2024)
In face-to-face dialogues, the form-meaning relationship of co-speech gestures varies depending on contextual factors such as what the gestures refer to and the individual characteristics of speakers. These factors make co-speech gesture representati
Externí odkaz:
http://arxiv.org/abs/2409.10535
Despite recent successes, LVLMs or Large Vision Language Models are prone to hallucinating details like objects and their properties or relations, limiting their real-world deployment. To address this and improve their robustness, we present CLIP-DPO
Externí odkaz:
http://arxiv.org/abs/2408.10433
We propose a novel approach to identification in structural vector autoregressions (SVARs) that uses external instruments for heteroscedasticiy of a structural shock of interest. This approach does not require lead/lag exogeneity for identification,
Externí odkaz:
http://arxiv.org/abs/2407.03265
Large language models play a crucial role in modern natural language processing technologies. However, their extensive use also introduces potential security risks, such as the possibility of black-box attacks. These attacks can embed hidden maliciou
Externí odkaz:
http://arxiv.org/abs/2406.14048
Leveraging 3D semantics for direct 3D reconstruction has a great potential yet unleashed. For instance, by assuming that walls are vertical, and a floor is planar and horizontal, we can correct distorted room shapes and eliminate local artifacts such
Externí odkaz:
http://arxiv.org/abs/2406.12054
Recent advancements in Deep and Self-Supervised Learning (SSL) have led to substantial improvements in Speech Emotion Recognition (SER) performance, reaching unprecedented levels. However, obtaining sufficient amounts of accurately labeled data for t
Externí odkaz:
http://arxiv.org/abs/2406.07900
Autor:
Krutikov, Sergei, Khaertdinov, Bulat, Kiriukhin, Rodion, Agrawal, Shubham, De Vries, Kees Jan
Transformer-based neural networks, empowered by Self-Supervised Learning (SSL), have demonstrated unprecedented performance across various domains. However, related literature suggests that tabular Transformers may struggle to outperform classical Ma
Externí odkaz:
http://arxiv.org/abs/2405.13692