# SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark +
## 2 Related Work  
In this subsection, we first introduce two types of video object segmentation tasks related to our work: mask-based video object segmentation and language-based video object segmentation. Subsequently, we further explore relevant research on sketch-guided image and video understanding. The essential characteristics of related works are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Query** & **Method** & **Task** & **Core Architecture** & **Training Set** & **Optical Flow** & **Bounding Box** & **Fine-Grained** \\ \hline \multirow{7}{*}{Text} & VOSwL [14] & \multicolumn{2}{c}{Two-Stream CNN +} & Ref-DAVIS & ✓ & ✓ & ✓ \\  & & Segmentation CNN & & & & & \\  & URVOS [15] & \multicolumn{2}{c}{Two-Stream CNN +} & Ref-YouTube + Ref-DAVIS & - & - & ✓ \\  & HINet [29] & \multicolumn{2}{c}{BERT + CNN} & Ref-DAVIS & ✓ & - & ✓ \\  & YOFO [30] & \multicolumn{2}{c}{WOS} & BERT + CNN & Ref-YouTube + Ref-DAVIS & - & - & ✓ \\  & MLR [31] & \multicolumn{2}{c}{BERT+Multi-Stream} & Ref-YouTube + Ref-DAVIS & - & - & ✓ \\  & LBDT [32] & \multicolumn{2}{c}{BERT + CNN} & Ref-YouTube + Ref-DAVIS & - & - & ✓ \\  & MTTR [33] & \multicolumn{2}{c}{Transformer} & Ref-YouTube + Ref-DAVIS & - & - & ✓ \\  & ReferFormer [34] & \multicolumn{2}{c}{Transformer} & Ref-YouTube + Ref-DAVIS & - & ✓ & ✓ \\  & SigMg [35] & \multicolumn{2}{c}{Transformer} & Ref-YouTube + Ref-DAVIS & - & ✓ & ✓ \\  & LoSh [36] & \multicolumn{2}{c}{Transformer} & Ref-YouTube + Ref-DAVIS & ✓ & - & ✓ \\ \hline \multirow{7}{*}{Mask} & OSVOS [37] & \multicolumn{2}{c}{CNN} & DAVIS & - & - & ✓ \\  & Segflow [38] & \multicolumn{2}{c}{CNN} & DAVIS & ✓ & - & ✓ \\  & Feelvos [39] & \multicolumn{2}{c}{CNN} & DAVIS & - & - & ✓ \\  & CFBI [40] & \multicolumn{2}{c}{Multi-Stream CNN +} & YouTube + DAVIS & - & - & ✓ \\  & QDMN [41] & \multicolumn{2}{c}{\multirow{2}{*}{VOS}} & \multicolumn{2}{c}{Multi-Stream CNN +} & YouTube + DAVIS + static image & - & ✓ \\  & & Attention & & datasets [42, 43] & & - & ✓ \\  & STM [44] & \multicolumn{2}{c}{Multi-Stream CNN +} & YouTube + DAVIS + static image & - & - & ✓ \\  & & Attention & & datasets [42, 43] & & - & ✓ \\  & & Two-Stream CNN + & \multicolumn{2}{c}{YouTube + DAVIS + static image} & & & \\  & & Attention & & datasets [42, 46, 47, 48, 49] + BL30K [50] & - & - & ✓ \\ \hline \multirow{7}{*}{Sketch} & Doodle [16] & SBIR & CNN + Attention & QuickDraw/Sketch [51]/TuBerlin [52] & - & - & - \\  & Livesketch [17] & SBIR & RNN & QuickDraw & - & - & - \\  & StyleMeUp [18] & SBIR & CNN & QMUL [53] & - & - & - \\  & SLM [20] & SBIR & CNN & QMUL & - & - & ✓ \\  & MoreP [21] & SBIR & RNN + CNN & QMUL & - & - & ✓ \\  & SWW [22] & SBIR & RNN + CNN & QMUL & - & - & ✓ \\  & Partially [19] & SBIR & CNN & QMUL & - & - & ✓ \\  & SGL [54] & SBIL & Two-Stream CNN & QuickDraw & - & ✓ & - \\  & Sketch-a- & \multicolumn{2}{c}{SkipC} & Multi-Stream CNN & TuBerlin & - & - & - \\  & Classifier [55] & \multicolumn{2}{c}{Sketch-a-} & \multicolumn{2}{c}{SkipS} & Two-Stream CNN & Sketchy [51] & - & - & - \\  & Segmenter [56] & \multicolumn{2}{c}{SGVR} & Two-Stream CNN & FG-SBVR & ✓ & - & ✓ \\  & FG-SBVR [57] & \multicolumn{2}{c}{SBVR} & Two-Stream CNN & SketchVideo & - & - & ✓ \\  & \multicolumn{2}{c}{Vanilla} & SKVOS & Two-Stream CNN & Sketch-VOS & - & - & ✓ \\  & Baseline [25] & \multicolumn{2}{c}{} & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of essential characteristics for related work. CNN: Convolutional Neural Network; GNN: Graph Neural Network; RNN: Recurrent Neural Network; SBIR: sketch-based image retrieval; SBIL: Sketch-based Image Location; SBIC: Sketch-based Image Classification; SBIS: Sketch-based Image Segmentation; SBVR: sketch-based video retrieval; SKVOS: sketch-based video object segmentation; Optical Flow: indicates whether optical flow is utilized; Bounding Box: specifies whether bounding boxes are employed; Fine-Grained: clarifies whether the task is fine-grained.