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pro vyhledávání: '"68T45"'
When developing Computer Aided Detection (CAD) systems for Digital Breast Tomosynthesis (DBT), the complexity arising from the volumetric nature of the modality poses significant technical challenges for obtaining large-scale accurate annotations. Wi
Externí odkaz:
http://arxiv.org/abs/2409.16581
Robustness is a fundamental aspect for developing safe and trustworthy models, particularly when they are deployed in the open world. In this work we analyze the inherent capability of one-stage object detectors to robustly operate in the presence of
Externí odkaz:
http://arxiv.org/abs/2411.04586
Autor:
Hawashin, Hala, Sadrzadeh, Mehrnoosh
While large language models (LLMs) have advanced the field of natural language processing (NLP), their "black box" nature obscures their decision-making processes. To address this, researchers developed structured approaches using higher order tensor
Externí odkaz:
http://arxiv.org/abs/2411.04242
We propose Few-Class Arena (FCA), as a unified benchmark with focus on testing efficient image classification models for few classes. A wide variety of benchmark datasets with many classes (80-1000) have been created to assist Computer Vision archite
Externí odkaz:
http://arxiv.org/abs/2411.01099
Image labeling is a critical bottleneck in the development of computer vision technologies, often constraining the potential of machine learning models due to the time-intensive nature of manual annotations. This work introduces a novel approach that
Externí odkaz:
http://arxiv.org/abs/2410.24116
Quanta image sensors, such as SPAD arrays, are an emerging sensor technology, producing 1-bit arrays representing photon detection events over exposures as short as a few nanoseconds. In practice, raw data are post-processed using heavy spatiotempora
Externí odkaz:
http://arxiv.org/abs/2410.23247
Reliable re-identification of individuals within large wildlife populations is crucial for biological studies, ecological research, and wildlife conservation. Classic computer vision techniques offer a promising direction for Animal Re-identification
Externí odkaz:
http://arxiv.org/abs/2410.22927
Contemporary state-of-the-art video object segmentation (VOS) models compare incoming unannotated images to a history of image-mask relations via affinity or cross-attention to predict object masks. We refer to the internal memory state of the initia
Externí odkaz:
http://arxiv.org/abs/2410.22451
Autor:
Alekseevsky, D. V., Spiro, A.
We propose a differential geometric model of hypercolumns in the primary visual cortex V1 that combines features of the symplectic model of the primary visual cortex by A. Sarti, G. Citti and J. Petitot and of the spherical model of hypercolumns by P
Externí odkaz:
http://arxiv.org/abs/2410.20184
Autor:
Schneider, David, Sajadmanesh, Sina, Sehwag, Vikash, Sarfraz, Saquib, Stiefelhagen, Rainer, Lyu, Lingjuan, Sharma, Vivek
Publikováno v:
Proceedings of the 2nd International Workshop on Privacy-Preserving Computer Vision, ECCV 2024
Privacy-preserving computer vision is an important emerging problem in machine learning and artificial intelligence. The prevalent methods tackling this problem use differential privacy or anonymization and obfuscation techniques to protect the priva
Externí odkaz:
http://arxiv.org/abs/2410.17098