本文提出了一種全新有關於承擔特質的任務：在影片中的每一幀畫面中，尋
找具有承擔特質的區域以及判斷承擔特質的有無。以往有關承擔特質的研究
中，只著重在圖像的偵測；為了此項在影片中偵測承擔特質的新任務，我們
提出一個新的承擔特質資料庫Support Affordance Video (SAV) dataset，蒐
集支撐承擔特質的影片並設計一系列的動作情境，使得承擔特質有無的狀態
隨著情境中的動作及環境而改變。我們提出了網路架構，使用兩條不同的分
支加上專注於時間序的模組，預測在影片中的承擔特質的關注區域、承擔特
質的區域、及承擔特質有無的標籤。我們檢驗在SAV 資料集上測試的結果，
以驗證此方法的有效性。

This thesis proposes a new task on affordance: detecting the affordance
region and predicting the existence of affordance for each frame in a video
sequence. In the past, researches about affordance only focus on detection
for a single image. For this new task about affordance detection in videos,
we build a new affordance dataset, Support Affordance Video (SAV) dataset.
The dataset consists of support affordance videos that exhibit a series of action
scenarios to make the affordance existence status change as actions and environments
change in scenarios. We propose a network architecture that uses
two different branches and temporal modules to predict affordance attention
area, affordance region, and affordance existence label in a video. The experimental
results on SAV dataset provide a baseline of the new task and validate
the effectiveness of our method.

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