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pro vyhledávání: '"Couairon A"'
Foundation models have emerged as powerful tools across various domains including language, vision, and multimodal tasks. While prior works have addressed unsupervised image segmentation, they significantly lag behind supervised models. In this paper
Externí odkaz:
http://arxiv.org/abs/2406.02842
Autor:
Corradini, Barbara Toniella, Shukor, Mustafa, Couairon, Paul, Couairon, Guillaume, Scarselli, Franco, Cord, Matthieu
Foundation models have exhibited unprecedented capabilities in tackling many domains and tasks. Models such as CLIP are currently widely used to bridge cross-modal representations, and text-to-image diffusion models are arguably the leading models in
Externí odkaz:
http://arxiv.org/abs/2403.20105
One of the guiding principles for designing AI-based weather forecasting systems is to embed physical constraints as inductive priors in the neural network architecture. A popular prior is locality, where the atmospheric data is processed with local
Externí odkaz:
http://arxiv.org/abs/2405.14527
The rapid growth of transformer-based models increases the concerns about their integrity and ownership insurance. Watermarking addresses this issue by embedding a unique identifier into the model, while preserving its performance. However, most exis
Externí odkaz:
http://arxiv.org/abs/2310.11446
Denoising Diffusion Probabilistic Models (DDPMs) have recently achieved remarkable results in conditional and unconditional image generation. The pre-trained models can be adapted without further training to different downstream tasks, by guiding the
Externí odkaz:
http://arxiv.org/abs/2309.09614
Autor:
Viotti Anne-Lise, Alisauskas Skirmantas, Balla Prannay, Wahid Ammar Bin, Sytcevich Ivan, Guo Chen, Silletti Laura, Cartella Andrea, Tavakol Hamed, Grosse-Wortmann Uwe, Schönberg Arthur, Seidel Marcus, Manschwetus Bastian, Lang Tino, Trabattoni Andrea, Calegari Francesca, Couairon Arnaud, L’Huillier Anne, Arnold Cord L., Hartl Ingmar, Heyl Christoph M.
Publikováno v:
EPJ Web of Conferences, Vol 243, p 21002 (2020)
Externí odkaz:
https://doaj.org/article/ae12dc5139b446fea55862fe4aa2fc36
Large-scale text-to-image diffusion models have significantly improved the state of the art in generative image modelling and allow for an intuitive and powerful user interface to drive the image generation process. Expressing spatial constraints, e.
Externí odkaz:
http://arxiv.org/abs/2306.13754
Recently, diffusion-based generative models have achieved remarkable success for image generation and edition. However, existing diffusion-based video editing approaches lack the ability to offer precise control over generated content that maintains
Externí odkaz:
http://arxiv.org/abs/2306.08707
Autor:
Ramé, Alexandre, Couairon, Guillaume, Shukor, Mustafa, Dancette, Corentin, Gaya, Jean-Baptiste, Soulier, Laure, Cord, Matthieu
Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further align the network with the intended usage. Yet the imperfections in the
Externí odkaz:
http://arxiv.org/abs/2306.04488
Autor:
Tolan, Jamie, Yang, Hung-I, Nosarzewski, Ben, Couairon, Guillaume, Vo, Huy, Brandt, John, Spore, Justine, Majumdar, Sayantan, Haziza, Daniel, Vamaraju, Janaki, Moutakanni, Theo, Bojanowski, Piotr, Johns, Tracy, White, Brian, Tiecke, Tobias, Couprie, Camille
Publikováno v:
Remote Sensing of Environment 300, 113888, 2024
Vegetation structure mapping is critical for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation. Repeated measurements of these data allow for the observation of deforestation or degradat
Externí odkaz:
http://arxiv.org/abs/2304.07213