影片中時空的表示法學習對於電腦視覺領域中的動作辨識研究是至關重要的。為了解決影片中動作識別和理解的表示法學習問題，我們通過學習：（1）每張影片中的靜態外觀，以及（2）連續影片中的時間上的運動資訊。在本篇論文中，我們提出了一種基於光流的運動和外觀網絡（FMA-Net），包括一個生成器網絡，一個分類網絡和一個鑑別器網絡，以學習影片中運動和外觀的分解表示法。此外，為了捕捉運動細節的資訊，我們建議從光流預測中學習，且無需在測試階段進行流量計算。我們提出的FMA-Net是一個端到端的架構，可以同時學習分類網絡，並在對抗訓練中生成準確的光流。我們在兩個動作識別的資料集上進行實驗：UCF101和HMDB51。在相同的設置下，我們的實驗結果表明，提出的FMA-Net不僅優於基線網絡，而且與其他採用最先進的方法相比我們方法獲得了有競爭力的結果。

Spatiotemporal representation learning in videos is essential to action recognition in computer vision. We address the problem of video representation learning for video action recognition and understanding through learning: (1) static appearance in each frame, and (2) temporal motion across consecutive frames. In this thesis, we propose a Flow-based Motion and Appearance Network (FMA-Net), which includes a generator network, a classification network and a discriminator network, to learn a decomposed representation of motion and appearance in videos. Furthermore, in order to capture motion details, we propose to learn the model from optical flow prediction without flow computation at test time. The proposed FMA-Net is an end-to-end framework and can simultaneously learn the classification network and generate accurate optical flow in adversarial training. We perform experiments on two action recognition benchmarks: UCF101 and HMDB51. Under the same setting, our experimental results show that the proposed FMA-Net not only outperforms the baseline network but also achieves competitive results with state-of-the-art methods on these two datasets.

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