From d8dc64042c06e6c09ffafed34fbb077addf94d88 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=8E=8B=E8=80=81=E6=9D=BF?= Date: Sat, 28 Sep 2024 10:49:55 +0800 Subject: [PATCH] update --- main.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/main.tex b/main.tex index c7b6449..59d0b45 100644 --- a/main.tex +++ b/main.tex @@ -246,7 +246,7 @@ where $\boldsymbol{c}^{l}_{j} \in \mathbb{R}^{2}$ and $\boldsymbol{c}^{g} \in \m x_{i,j}&=-y_{i,j}\tan \theta_j +\frac{r^{g}_j}{\cos \theta_j},\label{positions}\\ i&=1,2,\cdots,N_p,\notag \end{align} -where the y-coordinates $\{y_{1,j}, y_{2,j},\cdots,y_{N,j}\}$ of the $j$-th lane anchor are uniformly sampled vertically from the image, as previously mentioned. +where the y-coordinates $\{y_{1,j}, y_{2,j},\cdots,y_{N_p,j}\}$ of the $j$-th lane anchor are uniformly sampled vertically from the image, as previously mentioned. \par Given the feature maps $P_1, P_2, P_3$ from FPN, we can extract feature vectors corresponding to the positions of feature points $\{(x_{1,j},y_{1,j}),(x_{2,j},y_{2,j}),\cdots,(x_{N,j},y_{N,j})\}_{j=1}^{N^{lpm}_{pos}}$, respectively denoted as $\boldsymbol{F}_{1}, \boldsymbol{F}_{2}, \boldsymbol{F}_{3}\in \mathbb{R} ^{N^{lpm}_{pos}\times C_f}$. To enhance representation, similar to \cite{detr}, we employ a weighted sum strategy to combine features from different levels as \begin{equation}