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Segment Anything: The GPT-4.0 of Image Segmentation
Explore the revolutionary Segment Anything algorithm, the GPT-4.0 of image segmentation, and learn about its interactive, box region, and global segmentation capabilities.- 413
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Multi-scale Deep Learning Architectures for Person Re-identifification
This article introduces the role of multi-scale image resolution in person re-identification tasks and the idea of parameter sharing or feature interaction at corresponding layers, with a focus on the…- 286
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Vision Transformer Adapter for Dense Predictions
本文介绍了一种用于使得 ViT 架构适配下游密集预测任务的 Adapter,以及其在 COCO 数据集上的实验结果。通过使用 ViT-Adapter,可以显著提高下游密集预测任务的性能表现。- 174
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Grounded Segment Anything: Revolutionizing Computer Vision with SAM and Stable Diffusion
Discover how the Grounded Segment Anything project is revolutionizing computer vision with the SAM and Stable Diffusion models. Learn about the latest advancements in AI technology and image processin…- 451
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Squeeze-and-Excitation Networks: Improving Convolutional Neural Networks with Channel Relationships
Squeeze-and-Excitation Networks (SENets) are a novel architecture unit proposed to improve the representation capability of Convolutional Neural Networks by explicitly modeling the interdependence of …- 112
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IEEE UV 2022 Vision Meets Algae Object Detection Challenge BaseLine
本文介绍了IEEE UV 2022 Vision Meets Algae Object Detection Challenge的基准线设置和数据处理方法,以及如何使用yolo格式进行目标检测。文章内容涵盖计算机视觉、目标检测和数据预处理等方面。- 102
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Future Frame Prediction for Anomaly Detection – A New Baseline
Learn about how Future Frame Prediction using GAN networks can be used for anomaly detection in static camera videos. Explore the use of image space features and the application of this technique in r…- 457
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Segment Anything (SA) Project: Introducing a New Task, Model, and Image Segmentation Dataset
Introducing the Segment Anything (SA) project, a new task, model, and image segmentation dataset. Learn about the goals, methods, and results of this groundbreaking project.- 76
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CLIP vs DINOv2: Exploring Image Similarity in AI
Explore the strengths and intricacies of CLIP and DINOv2 in defining image similarity tasks in the world of AI. Witness the comparison between these two giants and see which model comes out on top.- 408
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