diff --git a/materialandmethods.typ b/materialandmethods.typ
index 425d27c..500745d 100644
--- a/materialandmethods.typ
+++ b/materialandmethods.typ
@@ -374,21 +374,35 @@ Its use of frozen pre-trained feature extractors is key to avoiding overfitting
 == Alternative Methods
 
 There are several alternative methods to few-shot learning which are not used in this bachelor thesis.
-Either they performed worse on benchmarks compared to the used methods or they were released after my literature research.
-#todo[Do it!]
+Either they performed worse on benchmarks compared to the used methods or they were released after my initial literature research.
 
-=== SgVA-CLIP
+=== SgVA-CLIP (Semantic-guided Visual Adapting CLIP)
 // https://arxiv.org/pdf/2211.16191v2
 // https://arxiv.org/abs/2211.16191v2
 
+SgVA-CLIP (Semantic-guided Visual Adapting CLIP) is a framework that improves few-shot learning by adapting pre-trained vision-language models like CLIP.
+It focuses on generating better visual features for specific tasks while still using the general knowledge from the pre-trained model.
+Instead of only aligning images and text, SgVA-CLIP includes a special visual adapting layer that makes the visual features more discriminative for the given task.
+This process is supported by knowledge distillation, where detailed information from the pre-trained model guides the learning of the new visual features.
+Additionally, the model uses contrastive losses to further refine both the visual and textual representations.~#cite(<peng2023sgvaclipsemanticguidedvisualadapting>)
+
+One advantage of SgVA-CLIP is that it can work well with very few labeled samples, making it suitable for applications like anomaly detection.
+The use of pre-trained knowledge helps reduce the need for large datasets.
+However, a disadvantage is that it depends heavily on the quality and capabilities of the pre-trained model.
+If the pre-trained model lacks relevant information for the task, SgVA-CLIP might struggle to adapt.
+This might be a no-go for anomaly detection tasks because the images in such tasks are often very task-specific and not covered by general pre-trained models.
+Also, fine-tuning the model can require considerable computational resources, which might be a limitation in some cases.~#cite(<peng2023sgvaclipsemanticguidedvisualadapting>)
+
 === TRIDENT
 // https://arxiv.org/pdf/2208.10559v1
 // https://arxiv.org/abs/2208.10559v1
 
+== SOT
 // https://arxiv.org/pdf/2204.03065v1
 // https://arxiv.org/abs/2204.03065v1
 
 // anomaly detect
+== GLASS
 // https://arxiv.org/pdf/2407.09359v1
 // https://arxiv.org/abs/2407.09359v1
 
diff --git a/sources.bib b/sources.bib
index 8dc1f69..08707bf 100644
--- a/sources.bib
+++ b/sources.bib
@@ -137,3 +137,13 @@
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2204.07305},
 }
+
+@misc{peng2023sgvaclipsemanticguidedvisualadapting,
+      title={SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification},
+      author={Fang Peng and Xiaoshan Yang and Linhui Xiao and Yaowei Wang and Changsheng Xu},
+      year={2023},
+      eprint={2211.16191},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2211.16191},
+}