fix: correct some info

assignment_2/slide.typ

@@ -61,7 +61,7 @@ Given a point cloud *$P = \{p_1, p_2, dots, p_n\}$*
 
 where each point *$p_i in RR^3$* represents a 3D coordinate in the railway environment,
 
-our task is to assign each point a semantic label *$l_i in \{0, 1, dots, C-1\}$*
+Our task is to assign each point a semantic label *$l_i in \{0, 1, dots, C-1\}$*
 
 where *$C = 13$* represents our predefined classes.
 
@@ -77,7 +77,7 @@ The function $f: P -> L$ maps the input point cloud to a set of labels $L = \{l_
   #v(1em)
   #text(size: 1.2em, weight: "bold")[Pilot Study Aims]
 
-  - Establish baseline performance using RandLA-Net
+  - Establish *baseline* performance using RandLA-Net
   - Evaluate feasibility of detecting extremely rare objects (0.001% of data)
 
 ][
@@ -109,32 +109,26 @@ The function $f: P -> L$ maps the input point cloud to a set of labels $L = \{l_
 
   #text(size: 1.1em, weight: "bold")[Training Setup]
 
-  - 858 training files, 172 test files
-  - Only 18 training files and 1 test file contain foreign objects
-  - 1/4 downsampling ratio
+  - *858* training files, *172* test files
+  - Only *18* training files and *1* test file contain foreign objects
+  - *1/4* downsampling ratio
   - NVIDIA RTX 4090 GPU
 
 ][
   #text(size: 1.1em, weight: "bold")[Data Collection]
 
-  - 1,031 PLY files with 248M+ points
-  - 13 semantic classes including railway infrastructure elements
-  - "Box" class (label 11) represents foreign objects
-  - Extreme class imbalance: boxes only 0.001% of points
+  - *1,031* PLY files with *248M+* points
+  - *13* semantic classes including railway infrastructure elements
+  - "Box" class (label *11*) represents foreign objects
+  - Extreme class imbalance: boxes only *0.001%* of points
 
 ]
 
-#slide(composer: (1fr, 3fr))[
-  #text(size: 1em, weight: "bold")[Inspection on data]
+#slide(composer: (2fr, 3fr))[
+  #text(size: 1.2em, weight: "bold")[Inspection on data]
   #v(1em)
   #text(size: 0.8em)[Table: Distribution of semantic classes in the railway LiDAR dataset]
 ][
-
-
-
-  // #text(size: 1.1em, weight: "bold")[Inspection on Data]
-
-
   #set text(size: 0.7em)
   #table(
     columns: (auto, auto, auto, auto),
@@ -153,7 +147,7 @@ The function $f: P -> L$ maps the input point cloud to a set of labels $L = \{l_
     [8], [Mountain], [51,685,366], [20.82%],
     [9], [Train], [9,047,963], [3.65%],
     [10], [Human], [275,077], [0.11%],
-    [11], [Box (foreign object)], [3,080], [0.001%],
+    [11], [*Box (object)*], [*3,080*], [*0.001%*],
     [12], [Others], [2,360,810], [0.95%],
   )
 
@@ -190,7 +184,7 @@ The function $f: P -> L$ maps the input point cloud to a set of labels $L = \{l_
 #slide()[
   #text(weight: "bold")[Results]
 
-  - The overall mean IoU across all classes was 70.29\%,
+  - The overall mean IoU across all classes was *70.29\%*,
   - the IoU for our target class— *"Box"* (foreign object)—was *0.00\%* (Will be discussed later).
   - IoU of other classes was relatively high, with *"Train"* achieving *95.22\%* and *"Ground"* achieving *89.68\%*.
 ][
@@ -237,15 +231,14 @@ The function $f: P -> L$ maps the input point cloud to a set of labels $L = \{l_
 
   Let's look at Cross Entropy Loss:
 
-  //   \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
-  // l_n = - w_{y_n} \log \frac{\exp(x_{n,y_n})}{\sum_{c=1}^C \exp(x_{n,c})}
-  // \cdot \mathbb{1}\{y_n \not= \text{ignore\_index}\}
   $
     ell(x, y) = 1 / N sum_(n=1)^N - w_(y_n) log frac(exp(x_{n,y_n}) , sum_(c=1)^C exp(x_{n,c}))
   $
 
   where $w_{y_n}$ is the weight for class $y_n$ and $N$ is the number of points in the batch.
 
+  In this case we just *blindly set weight for all class as 1*, which is not suitable. We should add weight on classes like boxes and human.
+
 ]
 
 
@@ -263,7 +256,9 @@ The function $f: P -> L$ maps the input point cloud to a set of labels $L = \{l_
 
 #slide(align: auto)[
 
-  #v(6em)
+  #text(weight: "bold")[Why Model Performs Bad on Boxes]
+
+  #v(5em)
 
   The model is *biased towards the majority classes*, leading to poor performance on the minority class (foreign objects).
 
@@ -273,9 +268,9 @@ The function $f: P -> L$ maps the input point cloud to a set of labels $L = \{l_
 
   #text(weight: "bold")[Future Work]
 
-  - Add weights in loss functions to address class imbalance
-  - Explore data augmentation techniques to increase the representation of the "Box" class
-  - Consider ensemble methods or multi-task learning to improve detection performance
+  - Add weights in loss functions to *address class imbalance*
+  - Explore *data augmentation* techniques to increase the representation of the "Box" class
+  - Consider *ensemble methods* or multi-task learning to improve detection performance
 ]