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pro vyhledávání: '"P, Parisot"'
Text-to-image diffusion generative models can generate high quality images at the cost of tedious prompt engineering. Controllability can be improved by introducing layout conditioning, however existing methods lack layout editing ability and fine-gr
Externí odkaz:
http://arxiv.org/abs/2411.10913
Visual domain gaps often impact object detection performance. Image-to-image translation can mitigate this effect, where contrastive approaches enable learning of the image-to-image mapping under unsupervised regimes. However, existing methods often
Externí odkaz:
http://arxiv.org/abs/2410.05058
Autor:
Tudosiu, Petru-Daniel, Yang, Yongxin, Zhang, Shifeng, Chen, Fei, McDonagh, Steven, Lampouras, Gerasimos, Iacobacci, Ignacio, Parisot, Sarah
Text-to-image generation has achieved astonishing results, yet precise spatial controllability and prompt fidelity remain highly challenging. This limitation is typically addressed through cumbersome prompt engineering, scene layout conditioning, or
Externí odkaz:
http://arxiv.org/abs/2404.02790
Electronically Assisted Astronomy consists in capturing deep sky images with a digital camera coupled to a telescope to display views of celestial objects that would have been invisible through direct observation. This practice generates a large quan
Externí odkaz:
http://arxiv.org/abs/2311.10617
Autor:
Parisot, Olivier, Jaziri, Mahmoud
Amateur and professional astronomers can easily capture a large number of deep sky images with recent smart telescopes. However, afterwards verification is still required to check whether the celestial objects targeted are actually visible in the ima
Externí odkaz:
http://arxiv.org/abs/2311.10592
Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted cl
Externí odkaz:
http://arxiv.org/abs/2304.01830
Autor:
Parisot, Olivier, Tamisier, Thomas
Reproducible images preprocessing is important in the field of computer vision, for efficient algorithms comparison or for new images corpus preparation. In this paper, we propose a method to obtain an explicit and ordered sequence of transformations
Externí odkaz:
http://arxiv.org/abs/2303.07151
Image quality assessment (IQA) forms a natural and often straightforward undertaking for humans, yet effective automation of the task remains highly challenging. Recent metrics from the deep learning community commonly compare image pairs during trai
Externí odkaz:
http://arxiv.org/abs/2211.05215
Autor:
Verwimp, Eli, Yang, Kuo, Parisot, Sarah, Lanqing, Hong, McDonagh, Steven, Pérez-Pellitero, Eduardo, De Lange, Matthias, Tuytelaars, Tinne
In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classification and object detection. The benchmark utilises SODA10M, a recently rel
Externí odkaz:
http://arxiv.org/abs/2210.03482
Autor:
Olivier Parisot, Mahmoud Jaziri
Publikováno v:
Astronomy, Vol 3, Iss 2, Pp 122-138 (2024)
Electronically Assisted Astronomy is a fascinating activity requiring suitable conditions and expertise to be fully appreciated. Complex equipment, light pollution around urban areas and lack of contextual information often prevents newcomers from ma
Externí odkaz:
https://doaj.org/article/37351dc2b13643ecae8951f24b261ba4
