# Deep Pixel-Level Matching via Attention for Video Co-Segmentation
## 5 Conclusions  
In this paper, we present a framework containing a new attention module for video co-segmentation. The attention module is designed to obtain the similarity information in pixel-level from two input frames for refining the feature maps of each frame through an attention mechanism in order to generate accurate segmentation results. We conducted an ablation study on the ObMiC dataset and compared the results of our framework with those of other video segmentation methods that perform well in common information extraction on the DAVIS 2016 dataset. Experimental results show that our framework achieves higher accuracies.  
[PERSON] contributed the idea and methodology and performed the experiments; [PERSON], [PERSON], [PERSON], and [PERSON] wrote the original draft; [PERSON] and [PERSON] performed some experiments; [PERSON] supervised the project and revised the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Development Fund of Macao SAR (0027/2018/A1 and 0016/2019/A1) and the Faculty Research Grant of Macau University of Science and Technology.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & \multicolumn{3}{c}{**Unsupervised Methods**} & \multicolumn{3}{c}{**Supervised Methods**} \\ \hline Video & VOS & FOS & BVS & DVCS & Baseline & Ours \\ Blackswan & 84.2 & 73.2 & **94.3** & 91.5 & 87.8 & 92.9 \\ Bmx-Bumps & 30.9 & 24.1 & 43.4 & 45.2 & 41.6 & **47.4** \\ Bmx-Trees & 19.3 & 18.0 & 38.2 & 41.1 & 45.1 & **50.9** \\ Breakdance & 54.9 & 46.7 & 50.0 & 52.9 & 81.0 & **84.4** \\ Breakdance-Flare & 55.9 & 61.6 & 72.7 & 60.2 & 84.0 & **87.9** \\ Camel & 57.9 & 56.2 & 66.9 & 82.7 & 85.8 & **91.9** \\ Car-Roundabout & 64.0 & 80.8 & 85.1 & 75.2 & 88.7 & **91.6** \\ Car-Shadow & 58.9 & 69.8 & 57.8 & 75.9 & 92.4 & **93.6** \\ Cows & 33.7 & 79.1 & 89.5 & 88.7 & 88.1 & **92.2** \\ Dance-Jump & 74.8 & 59.8 & **74.5** & 64.2 & 66.6 & 69.9 \\ Dance-Twirl & 38.0 & 45.3 & 49.2 & 60.6 & 77.3 & **81.6** \\ Dog & 69.2 & 70.8 & 72.3 & 86.4 & 91.3 & **93.6** \\ Drift-Chiance & 18.8 & 66.7 & 3.3 & 71.5 & 79.9 & **81.6** \\ Drift-Straight & 19.4 & 68.3 & 40.2 & 66.6 & 89.4 & **91.7** \\ Goat & 70.5 & 55.4 & 66.1 & 79.4 & 85.6 & **87.7** \\ Horsejump-High & 37.0 & 57.8 & 80.1 & **80.9** & 71.2 & 79.0 \\ Horsejump-Low & 63.0 & 52.6 & 60.1 & 75.8 & 77.1 & **82.2** \\ Kite-Surf & 58.5 & 27.2 & 42.5 & **68.7** & 54.5 & 63.5 \\ Kite-Walk & 19.7 & 64.9 & **87.0** & 71.6 & 71.3 & 75.5 \\ Libby & 61.1 & 50.7 & 77.6 & **79.9** & 64.9 & 72.2 \\ Mallard-Water & 78.5 & 8.7 & 90.7 & 74.6 & 89.6 & **92.3** \\ Motocross-Bumps & 68.9 & 61.7 & 40.1 & 83.3 & 83.5 & **88.1** \\ Motocross-Jump & 28.8 & 60.2 & 34.1 & 68.6 & 86.0 & **89.3** \\ Paradgliding & 86.1 & 72.5 & 87.5 & 90.2 & 87.4 & **91.6** \\ Paradgliding-Launch & 55.9 & 50.6 & **64.0** & 60.0 & 57.0 & 59.7 \\ Parkour & 41.0 & 45.8 & 75.6 & 77.9 & 81.0 & **84.7** \\ Rhino & 67.5 & 77.6 & 78.2 & 83.8 & 92.6 & **94.7** \\ Rollerblade & 51.0 & 31.8 & 58.8 & 77.0 & 81.0 & **85.0** \\ Scooter-Black & 50.2 & 52.2 & 33.7 & 44.5 & 85.6 & **87.2** \\ Scooter-Gray & 36.3 & 32.5 & 50.8 & 66.1 & 80.7 & **84.3** \\ Soapbox & 75.7 & 41.0 & 78.9 & 79.4 & 81.2 & **85.6** \\ Stroller & 75.9 & 58.0 & 76.7 & **87.8** & 78.5 & 84.9 \\ \hline Avg. & 53.3 & 53.8 & 63.1 & 72.3 & 78.4 & **82.5** \\ \hline \end{tabular}
\end{table}
Table 2: The average intersection-over-union (IoU) scores of our framework, baseline (ResNet), and four video object segmentation methods on the DAVIS dataset. Four video object segmentation methods are unsupervised learning methods; the baseline and our framework are supervised learning methods. Numbers in bold are the best performance of each row.