Improving Circulating Tumor Cell Detection Using Image Synthesis and Transformer Models in Cancer Diagnostics [PDF]
SensorsCancer is the second leading cause of death, significantly threatening human health. Effective treatment options are often lacking in advanced stages, making early diagnosis crucial for reducing mortality rates.
Shuang Liang, Xue Bai, Yu Gu
doaj +2 more sources
Evaluating land cover types from Landsat TM using SAGA GIS for vegetation mapping based on ISODATA and K-means clustering [PDF]
Acta Agriculturae Serbica, 2021 The paper presents the cartographic processing of the Landsat TM image by the two unsupervised classification methods of SAGA GIS: ISODATA and K-means clustering. The approaches were tested and compared for land cover type mapping.
Lemenkova Polina
doaj +1 more source
PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis [PDF]
International Conference on Learning Representations, 2023 The most advanced text-to-image (T2I) models require significant training costs (e.g., millions of GPU hours), seriously hindering the fundamental innovation for the AIGC community while increasing CO2 emissions.
Jun-Song Chen +10 more
semanticscholar +1 more source
Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis [PDF]
arXiv.org, 2023 Recent text-to-image generative models can generate high-fidelity images from text inputs, but the quality of these generated images cannot be accurately evaluated by existing evaluation metrics.
Xiaoshi Wu +6 more
semanticscholar +1 more source
MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing [PDF]
IEEE International Conference on Computer Vision, 2023 Despite the success in large-scale text-to-image generation and text-conditioned image editing, existing methods still struggle to produce consistent generation and editing results.
Ming Cao +5 more
semanticscholar +1 more source
Taming Transformers for High-Resolution Image Synthesis [PDF]
Computer Vision and Pattern Recognition, 2020 Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions.
Patrick Esser +2 more
semanticscholar +1 more source
Scaling up GANs for Text-to-Image Synthesis [PDF]
Computer Vision and Pattern Recognition, 2023 The recent success of text-to-image synthesis has taken the world by storm and captured the general public's imagination. From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models.
Minguk Kang +6 more
semanticscholar +1 more source
Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis [PDF]
International Conference on Learning Representations, 2022 Large-scale diffusion models have achieved state-of-the-art results on text-to-image synthesis (T2I) tasks. Despite their ability to generate high-quality yet creative images, we observe that attribution-binding and compositional capabilities are still ...
Weixi Feng +8 more
semanticscholar +1 more source
Real-Time Depth Video-Based Rendering for 6-DoF HMD Navigation and Light Field Displays
IEEE Access, 2021 This paper presents a novel approach to provide immersive free navigation with 6 Degrees of Freedom in real-time for natural and virtual scenery, for both static and dynamic content.
Daniele Bonatto +4 more
doaj +1 more source
Anti-DreamBooth: Protecting users from personalized text-to-image synthesis [PDF]
IEEE International Conference on Computer Vision, 2023 Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs.
Van Thanh Le +5 more
semanticscholar +1 more source