# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 2 Domain Adaptation Techniques
### Overview of Domain Adaptation Techniques
#### 2.1.1 Scholarly Landscape Visualization
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**References** & **Title** & **Last Author** & **Year** & **Citations** & **Graph References** \\ \hline
[54] & Towards Large-Scale Small Object Detection: Survey and Benchmarks & [PERSON] & 2022 & 34 & 5 \\ \hline
[58] & Semi-Supervised Person Detection in Aerial Images with Instance Segmentation and Maximum Mean Discrepancy Distance & [PERSON] & 2023 & 1 & 4 \\ \hline
[60] & A unified and costless approach for improving small and long-tail object detection in aerial images of traffic scenarios & [PERSON] & 2022 & 1 & 4 \\ \hline
[61] & Pareto Refocusing for Drone-View Object Detection & [PERSON] & 2023 & 3 & 4 \\ \hline
[62] & RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection & [PERSON] & 2022 & 24 & 4 \\ \hline
[63] & DB-YOLOv5: A UAV Object Detection Model Based on Dual Backbone Network for Security Surveillance & [PERSON] & 2023 & 0 & 3 \\ \hline
[64] & Small object detection leveraging density-aware scale adaptation & [PERSON] & 2023 & 0 & 3 \\ \hline
[65] & CFANet: Efficient Detection of UAV Image Based on Cross-Layer Feature Aggregation & [PERSON] & 2023 & 1 & 3 \\ \hline
[66] & OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV Images & [PERSON] & 2023 & 1 & 3 \\ \hline
[67] & Object Detection for UAV Aerial Scenarios Based on Vectorized IOU & [PERSON] & 2023 & 3 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Derivative works on domain adaptation for object identification. Tables 2, 4 and 6 show prior works, while Tables 3, 5 and 7 shows derivative works related to semantic segmentation, object identification and object classification in the field of domain adaption, which are two important tasks in computer vision. The table includes the references, titles, last authors, years, citations, and graph citations of each work. The citations indicate how many times the work has been cited by other papers, while the graph citations indicate how many times the work has been cited by papers that also cite the current paper. These tables can be used to compare the impact and relevance of different works in the field. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**References** & **Title** & **Last Author** & **Year** & **Citations** & **Graph** \\ \hline
[68] & Learning Deep Features for Discriminative Localization & [PERSON] & 2015 & 7207 & 41 \\ \hline
[69] & Adversarial Complementary Learning for Weakly Supervised Object Localization & [PERSON] & 2018 & 455 & 41 \\ \hline
[70] & Self-produced Guidance for Weakly-supervised Object Localization & [PERSON] & 2018 & 210 & 40 \\ \hline
[75] & Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-Supervised Object and Action Localization & [PERSON] & 2017 & 541 & 39 \\ \hline
[76] & CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features & [PERSON] & 2019 & 2993 & 38 \\ \hline
[71] & Attention-Based Dropout Layer for Weakly Supervised Object Localization & [PERSON] & 2019 & 277 & 37 \\ \hline
[72] & DANet: Divergent Activation for Weakly Supervised Object Localization & [PERSON] & 2019 & 112 & 30 \\ \hline
[73] & The Caltech-UCSD Birds-200–2011 Dataset & [PERSON] & 2011 & 3635 & 30 \\ \hline
[74] & Deep Residual Learning for Image Recognition & [PERSON] & 2015 & 141,511 & 29 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Prior works on domain adaption for object classification.