add stuff for CAML

materialandmethods.typ

@@ -197,9 +197,11 @@ There are several different ResNet architectures, the most common are ResNet-18,
 For this bachelor theis the ResNet-50 architecture was used to predict the corresponding embeddings for the few-shot learning methods.
 
 === P$>$M$>$F
-Todo
+// https://arxiv.org/pdf/2204.07305
 
-=== CAML
+#todo[Todo]#cite(<pmfpaper>)
+
+=== CAML <CAML>
 // https://arxiv.org/pdf/2310.10971v2
 CAML (Context aware meta learning) is one of the state-of-the-art methods for few-shot learning.
 It consists of three different components: a frozen pre-trained image encoder, a fixed Equal Length and Maximally Equiangular Set (ELMES) class encoder and a non-causal sequence model.
@@ -237,12 +239,9 @@ Afterwards it is passed through a simple MLP network to predict the class of the
 *Large-Scale Pre-Training:*
 CAML is pre-trained on a huge number of images from ImageNet-1k, Fungi, MSCOCO, and WikiArt datasets.
 Those datasets span over different domains and help to detect any new visual concept during inference.
-Only the non-causal sequence model is trained and the image encoder and ELMES encoder are frozen.
+Only the non-causal sequence model is trained and the weights of the image encoder and ELMES encoder are kept frozen.
 ~#cite(<caml_paper>)
 
-*Theoretical Analysis:*
-#todo[Mybe not that important?]
-
 *Inference:*
 During inference, CAML processes the following:
 - Encodes the support set images and labels with the pre-trained feature and class encoders.
@@ -250,7 +249,7 @@ During inference, CAML processes the following:
 - Passes the sequence through the non-causal sequence model, enabling dynamic interaction between query and support set representations.
 - Extracts the transformed query embedding and classifies it using a Multi-Layer Perceptron (MLP).~#cite(<caml_paper>)
 
-*Results:*
+*Performance:*
 CAML achieves state-of-the-art performance in universal meta-learning across 11 few-shot classification benchmarks,
 including generic object recognition (e.g., MiniImageNet), fine-grained classification (e.g., CUB, Aircraft),
 and cross-domain tasks (e.g., Pascal+Paintings).