新一代視覺追蹤模型SAMURAI：複雜的場景裏也能追蹤到主體
近日，華盛頓大學的研究團隊發佈了一個名爲 SAMURAI 的新型視覺追蹤模型。該模型基於沿的 Segment Anything Model2（SAM2），旨在解決在複雜場景中進行視覺對象追蹤時所遇到的挑戰，尤其是在處理快速移動和自遮擋物體時。SAM2在物體分割任務中表現出色，但在視覺追蹤方面卻存在一些限制。例如，在擁擠的場景中，固定窗口的記憶方式未能考慮到所選記憶的質量，這可能導致錯誤在視頻序列中不斷傳播。爲了解決這一問題，研究團隊提出了 SAMURAI，通過引入時間運動線索以及運動感知記憶選擇
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