# 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}{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
[77] & Generative Prompt Model for Weakly Supervised Object Localization & [PERSON] & 2023 & 4 & 14 \\ \hline
[78] & Open-World Weakly-Supervised Object Localization & [PERSON] & 2023 & 1 & 11 \\ \hline
[79] & Further Improving Weakly-supervised Object Localization via Causal Knowledge Distillation & [PERSON] & 2023 & 1 & 11 \\ \hline
[80] & What is Where by Looking: Weakly-Supervised Open-World Phase-Grounding without Text Inputs & [PERSON] & 2022 & 5 & 9 \\ \hline
[81] & Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization & [PERSON] & 2023 & 2 & 8 \\ \hline
[82] & Deep Learning for Weakly-Supervised Object Detection and Localization: A Survey & [PERSON] & 2022 & 39 & 8 \\ \hline
[83] & Rethinking the Localization in Weakly Supervised Object Localization & [PERSON] & 2023 & 0 & 7 \\ \hline
[84] & MCTformer+: Multi-Class Token Transformer for Weakly Supervised Semantic Segmentation & [PERSON] & 2023 & 0 & 7 \\ \hline
[85] & Adversarial Transformers for Weakly Supervised Object Localization & [PERSON] & 2022 & 1 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Derivative works on domain adaption for object classification. Domain Adaptation for Image Segmentation  
Prior works as shown in Table 2 are the research papers that were most commonly cited by the papers in the graph. This means that they are important seminal works for this field and it could be a good idea to get familiar with them. In Table 2, the most frequently referenced work [30; 31] enhanced performance on a variety of image recognition tasks by introducing a revolutionary design for deep neural networks. There are 23 graph citations and 141,511 citations in this work. The most recent study [32] suggested a technique for knowledge transfer between domains by utilizing cycle-consistency losses and generative adversarial networks. This work has the most citations of any of the works in the table, with 2445 total citations and 29 graph citations. For four of the works in the table [33; 34; 35; 36; 37], 2016 is the most common year. These studies advanced semantic segmentation by offering fresh approaches, datasets, and standards. In Table 3, the most recent works are from 2023, which have three entries in the table [40; 41; 49]. These works provide an overview of the state-of-the-art methods, challenges, and applications of domain adaptation and semantic segmentation in various domains. The most influential work is [41], which has 18 citations and 18 graph citations. This work provides a comprehensive survey of test time adaptation methods that aim to improve the generalization performance of models under distribution shifts. This work covers various aspects of test time adaptation, such as definitions, taxonomies, evaluation protocols, applications, and open challenges. Domain Adaptation for Object Identification  
In Table 4, the most cited work is [50], which introduced a novel architecture for deep neural networks that improved the performance on various image recognition tasks. This work has 141,511 citations and 33 graph citations. The most recent work is [51], which proposed an improved version of the YOLO (You Only Look Once) framework that achieved state-of-the-art results on several object detection benchmarks. This work has 7287 citations and 31 graph citations, which is impressive considering its recency. The most frequent last author is [PERSON], who has three works in the table [52]. These works are part of the YOLO series, which pioneered the idea of using a single neural network to perform object detection in real time. The most common theme is region proposal networks (RPNs), which are a technique used to generate candidate regions for object detection. There are four works in the table that use RPNs [33; 54; 53].