transreid: transformer-based object re-identification
时间: 2023-04-15 14:04:57 浏览: 139
transreid是一种基于transformer的目标重新识别技术。它使用transformer模型来学习目标之间的相似性,并将其应用于重新识别任务中。这种方法可以在不同场景下实现目标的准确识别,具有很高的应用价值。
相关问题
你知道2021-ICCV_TRANSREID_TRANSFORMER-BASED-OBJECT-RE-IDENTIFICATION这篇文章嘛
是的,作为一名大型语言模型,我对很多计算机科学领域的论文都有一定的了解,包括2021-ICCV_TRANSREID_TRANSFORMER-BASED-OBJECT-RE-IDENTIFICATION这篇文章。这是一篇关于基于Transformer的目标再识别方法的论文,它提出了一种新颖的TransReID模型来实现目标再识别任务,该模型利用Transformer架构和多尺度特征提取来获得更好的性能。该论文的研究成果在目标再识别领域取得了一定的进展和应用。
Transformer-Based Visual Segmentation: A Survey
Visual segmentation is one of the most important tasks in computer vision, which involves dividing an image into multiple segments, each of which corresponds to a different object or region of interest in the image. In recent years, transformer-based methods have emerged as a promising approach for visual segmentation, leveraging the self-attention mechanism to capture long-range dependencies in the image.
This survey paper provides a comprehensive overview of transformer-based visual segmentation methods, covering their underlying principles, architecture, training strategies, and applications. The paper starts by introducing the basic concepts of visual segmentation and transformer-based models, followed by a discussion of the key challenges and opportunities in applying transformers to visual segmentation.
The paper then reviews the state-of-the-art transformer-based segmentation methods, including both fully transformer-based approaches and hybrid approaches that combine transformers with other techniques such as convolutional neural networks (CNNs). For each method, the paper provides a detailed description of its architecture and training strategy, as well as its performance on benchmark datasets.
Finally, the paper concludes with a discussion of the future directions of transformer-based visual segmentation, including potential improvements in model design, training methods, and applications. Overall, this survey paper provides a valuable resource for researchers and practitioners interested in the field of transformer-based visual segmentation.