視覺追蹤的多尺度視覺基礎網路

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姓名

王品灃(Pin-Feng Wang) 查詢紙本館藏

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通訊工程學系

論文名稱

視覺追蹤的多尺度視覺基礎網路
(Multi-Scale Vision Foundation Networks for Visual Tracking)

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至系統瀏覽論文 (2026-8-1以後開放)

摘要(中)

在單目標追蹤中，採用階層式（hierarchical）的Vision Transformer（ViT）架構的追蹤器，往往追蹤表現不如plain ViT，同時文獻彼此之間架構都是有差異的，並沒有一個通用的網路架構。本論文提出一個通用的階層式網路架構（HyperXTrack），第一個將骨幹網路的架構，引用到追蹤任務上作為交互作用網路，同時加入時空上下文，空間的上下文是多尺度資訊，時間的上下文提供歷史資訊。HyperXTrack能進行全局與局部空間交互作用，且交互作用計算複雜度為影像解析度的線性複雜度。HyperXTrack每一個block都是先進行比對細緻紋理特徵，再進行整個物件外觀輪廓的交互比對。交互骨幹網路採用本論文所提之注意力機制，同時採用經典的堆疊規則在注意力機制前使用卷積。最後，本論文提出輕量的重新預訓練策略，可以使用預訓練好的MaxViT網路參數，將更改網路交互運算的網路重新訓練一個epoch，就可以讓網路的參數可以遷移到下游任務上。實驗結果顯示，本論文設計的HyperXTrack架構在GOT-10k數據集上AO以75%超越OSTrack的71%，同時僅需要使用30M參數量的階層式架構，就可以超越OSTrack的93M參數量的ViT架構。

摘要(英)

In single object tracking, the hierarchical Vision Transformer (ViT) architectures usually perform worse than plain ViT among current trackers. At the same time, the network architectures of state-of-the-art trackers are distinct, and thus there is no general purposed network architecture. This paper presents HyperXTrack, the first backbone network architecture that is applied to interaction in visual tracking. In addition, the proposed backbone interacts spatio-temporal context, where spatial context is the multi-scale information and temporal context provides historical information. HyperXTrack proceeds global and local spatial interaction, and computation complexity is linear with image resolution. After correlating with local texture features, the contour of the entire object is interacting. Interaction backbone networks adopt the proposed attention mechanism and the classic stacking rule where convolutions are applied before attention mechanism. Finally, this thesis proposes lightweight re-pretraining strategy. After modifying the existing network MaxViT, this thesis uses the pre-trained MaxViT weights, and re-pretrains only one epoch. Then the network can transfer to the downstream tasks. The experimental results show that HyperXTrack surpasses OSTrack′s 71% in AO with 71.8% on the GOT-10k dataset. HyperXTrack using a hierarchical architecture only needs 30M parameters, which can surpass OSTrack architecture with 93M parameters.

關鍵字(中)

★ 單目標追蹤
★ 階層式
★ 重新預訓練
★ 視覺轉換器
★ 模板更新策略

關鍵字(英)

★ single object tracking
★ hierarchical
★ re-pretraining
★ vision Transformer
★ template update strategy

論文目次

中文摘要 V
英文摘要 VI
誌謝 VII
目錄 VIII
圖目錄 X
表目錄 XIII
符號說明 XIV
一、緒論 1
1-1前言 1
1-2 研究動機 1
1-3 研究方法 2
1-4 論文架構 4
二、基於特徵交互之視覺追蹤介紹 6
2-1 特徵交互對於視覺追蹤技術重要性 6
2-2 基於TRANSFORMER之視覺追蹤技術現況 10
2-2-1 分別進行特徵抽取與交互作用之網路 10
2-2-2 同時進行特徵抽取與交互作用之網路 15
2-3 遷移學習對於視覺追蹤任務的影響性 22
2-4 總結 23
三、基於計算機視覺的TRANSFORMER網路 24
3-1 卷積神經網路 24
3-2 VISION TRANSFORMER 26
3-3 階層式VISION TRANSFORMER網路 30
3-4 總結 37
四、全局多尺度的視覺基礎網路 38
4-1 不同滑動窗口所獲得特徵圖 38
4-2 HYPERXTRACK 40
4-2-1 通用電腦視覺的網路架構 41
4-2-2 PREDICTION HEAD 50
4-2-3 HIERARCHICAL架構 51
4-3 神經網路輕量的重新預訓練策略 52
4-4 總結 53
五、實驗結果與討論 55
5-1 實驗的參數設定 55
5-1-1 訓練階段實驗參數設定 55
5-1-2 推論階段實驗參數設定 59
5-2實驗結果 59
5-3 消融實驗 64
5-4 追蹤結果圖 68
5-5 總結 78
六、結論與未來展望 79
參考文獻 80

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指導教授

唐之瑋(Chih-Wei Tang)

審核日期

2023-7-19

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