一、對這篇論文的簡單了解
1、SiamMask結合兩種網絡的任務，一個是目标跟蹤網絡，另一個是目标分割網絡，對于vot名額，SiamMask以精度取勝，對于vos名額，SiamMask以速度取勝，以前的一些視訊分割網絡隻能fps基本是1以下，但這個網絡可以達到55fps，強！
2、以前的vot大部分是線上學習一個分類器，然後後面的幀可以根據情況更新模闆再分類，是tracking-by-detection，比如kcf之類的方法；而Siamese系列的跟蹤網絡是學習第一幀的模闆與搜尋區域的相似性–response map，（ROW），把模闆的feature map當作是卷積核與搜尋區域的feature map進行卷積操作，這裡用來depth-wise卷積産生多通道的ROW，可以編碼更豐富的資訊。
3、一個ROW隻預測一個mask，和MaskRcnn不一樣，它是預測k個mask，k是類别；還要說明一點，box的分支是每個預測k個box，但這個K是提前設定的不同尺寸不同長寬比的框的數量。
4、如何根據生成的mask産生用于vot名額的框對評測也有影響，論文結合精度和速度選用了MBR
5、訓練資料集： COCO [31], ImageNet-VID [47] and YouTube-VOS [58]
6、在vot方面，超越了DaSiamRPN和kcf，decay小，更适合長視訊
二、性能比較，論文中給出的資料
1、網絡結構圖，但實際不是如論文中figure2這麼簡單的，還有refine子產品和adjust層，在附錄裡有具體展示，這裡也給出：：
2、與vot方面的sota工作對比
3、與vos方面的sota工作對比
4、ablation studies
三、對自己有益的原句摘抄
1、
It finds use in a wide range of scenarios
such as automatic surveillance, vehicle navigation, video labelling, human-computer interaction and activity recognition.
這裡是指視訊跟蹤
2、
In this paper, we aim at narrowing the gap between arbitrary object tracking and VOS by proposing SiamMask,
a simple multi-task learning approach that can be used
to address both problems.
3、
To achieve this goal, we simultaneously train a Siamese
network on three tasks, each corresponding to a different
strategy to establish correspondances between the target object and candidate regions in the new frames.
4、
Performance of Correlation Filter-based
trackers has then been notably improved with the adoption of multi-channel formulations [24, 20], spatial constraints [25, 13, 33, 29] and deep features (e.g. [12, 51])
5、這個不太了解，需要繼續學習
In order to exploit consistency between video frames,
several methods propagate the supervisory segmentation
mask of the first frame to the temporally adjacent ones via
graph labeling approaches (e.g. [55, 41, 50, 36, 1]). In
particular, Bao et al. [1] recently proposed a very accurate
method that makes use of a spatio-temporal MRF in which
temporal dependencies are modelled by optical flow, while
spatial dependencies are expressed by a CNN
6、
The loss function Lmask (Eq. 3) for the mask prediction task is a binary
logistic regression loss over all RoWs:
7、
In contrast to semantic segmentation methods in the style of FCN [32] and Mask RCNN [17], which maintain explicit spatial information
throughout the network, our approach follows the spirit
of [43, 44] and generates masks starting from a flattened representation of the object.
8、這個也不了解
Similarly to most VOS
methods, in case of multiple objects in the same video
(DAVIS-2017) we simply perform multiple inferences
9、
Interestingly, the refinement approach of Pinheiro et al. [44]
is very important for the contour accuracy FM, but less so
for the other metrics.