💡 Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers


▪Github: https://github.com/alpha-vllm/lumina-t2x
▪Paper: https://arxiv.org/abs/2405.05945
▪Demo: https://lumina.sylin.host/

@Machine_learn

⚡️ Deblur-GS: 3D Gaussian Splatting from Camera Motion Blurred Images


▪Code: https://github.com/Chaphlagical/Deblur-GS
▪Paper: https://chaphlagical.icu/Deblur-GS/static/paper/Deblur_GS_author_version.pdf
▪Project: https://chaphlagical.icu/Deblur-GS/

@Machine_learn

Images that Sound: Composing Images and Sounds on a Single Canvas

abs: https://arxiv.org/abs/2405.12221
project page: https://ificl.github.io/images-that-sound/
code: https://github.com/IFICL/images-that-sound

This paper introduces an inference-time procedure that generates images that are also spectrograms corresponding to the prompt. It uses a latent image and audio diffusion model with same latent space (Stable Diffusion v1.5 and Auffusion) and denoise the same latent with both.

@Machine_learn

🔥🔥🔥 YOLOv10: Real-Time End-to-End Object Detection

▪Paper: arxiv.org/pdf/2405.14458
▪Github: https://github.com/THU-MIG/yolov10/
▪Demo :https://huggingface.co/spaces/kadirnar/Yolov10
▪Colab: https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb#scrollTo=SaKTSzSWnG7s

@machine_learn

با عرض سلام این مقاله رو می خواییم برای Nature بفرستیم جایگاه های ۱ تا ۴ اش خالیه از دوستان کسی نیاز داشت در خدمتیم
Title:
Detection of brain tumors from images using the UNet architecture, with a comparative analysis of transfer learning methods and CNNs.
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Abstract:
Health is crucial for human life, especially brain health, which is vital for all executive functions. Diagnosing brain health issues is often done using magnetic resonance imaging (MRI) devices, which provide critical data for health decision-makers. Images from these devices serve as a significant source of big data for artificial intelligence applications. This big data facilitates high performance in image processing classification problems, a subfield of artificial intelligence. In this study, we aim to classify brain tumors such as glioma, meningioma, and pituitary tumors from brain MRI images using the UNet architecture. To compare the results and gain a better understanding, we also employed Convolutional Neural Networks (CNN) and CNN-based models like Inception-V3, EfficientNetB4, VGG19, along with transfer learning methods for classification tasks. The models were evaluated using F-score, recall, precision, and accuracy metrics. The best accuracy result was achieved with CNN-VGG16, reaching 97%. The same transfer learning model also showed an F-score of 96%, an Area Under the Curve (AUC) value of 98%, a recall value of 98%, and a precision value of 97%. The UNet architecture and CNN-based transfer learning models play a significant role in the early diagnosis and rapid treatment of brain tumors, which is vital for improving patient outcomes.
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Keywords:
Brain tumor detection, UNet, CNN, Transfer Learning.
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Journal:
Scientific Reports

@Raminmousa
@Machine_learn
@paper4money

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🎙 Real-time in-browser speech recognition



▪Сode: https://github.com/xenova/transformers.js/tree/v3/examples/webgpu-whisper
▪Hf: https://huggingface.co/spaces/Xenova/realtime-whisper-webgpu

@Machine_learn

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🔈 Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language



▪Paper: https://arxiv.org/abs/2406.05629
▪Website: https://mhamilton.net/denseav
▪Code: https://github.com/mhamilton723/DenseAV
▪Video: https://youtu.be/wrsxsKG-4eE

@Machine_learn