新一代视觉追踪模型SAMURAI：复杂的场景里也能追踪到主体
近日，华盛顿大学的研究团队发布了一个名为 SAMURAI 的新型视觉追踪模型。该模型基于沿的 Segment Anything Model2（SAM2），旨在解决在复杂场景中进行视觉对象追踪时所遇到的挑战，尤其是在处理快速移动和自遮挡物体时。SAM2在物体分割任务中表现出色，但在视觉追踪方面却存在一些限制。例如，在拥挤的场景中，固定窗口的记忆方式未能考虑到所选记忆的质量，这可能导致错误在视频序列中不断传播。为了解决这一问题，研究团队提出了 SAMURAI，通过引入时间运动线索以及运动感知记忆选择
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