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diff --git a/data/3DSSD Point-based 3D Single Stage Object Detector.md b/data/3DSSD Point-based 3D Single Stage Object Detector.md
@@ -91,7 +91,7 @@ python lib/core/trainer.py --cfg configs/kitti/3dssd/3dssd.yaml
 ```
 
 ## どんなもの?
-### Single stage 3D物体検出モデル、3DSSDを提案した。
+- Single stage 3D物体検出モデル、3DSSDを提案した。
 - 精度と効率を調律させた軽量のモデルである。
 - 効率を上げるため、既存手法で大抵採用されているアップサンプリング層とrefinement stageをすべて省き、計算コストを大幅削減した。
 - 少ない代表点での検出を可能にするためのダウンサンプリング処理におけるfusion sampling strategyを提案する。
@@ -102,10 +102,6 @@ python lib/core/trainer.py --cfg configs/kitti/3dssd/3dssd.yaml
 
 > Figure 1. Illustration of the 3DSSD framework. On the whole, it is composed of backbone and box prediction network including a candidate generation layer and an anchor-free prediction head. (a) Backbone network. It takes the raw point cloud (x, y, z, r) as input, and generates global features for all representative points through several SA layers with fusion sampling (FS) strategy. (b) Candidate generation layer (CG). It downsamples, shifts and extracts features for representative points after SA layers. (c) Anchor-free prediction head.
 
-### Contribution
-> - We first propose a lightweight and effective point-based 3D single stage object detector, named 3DSSD. In our paradigm, we remove FP layers and the refinement module, which are indispensible in all existing point-based methods, contributing to huge deduction on inference time of our framework.
-> - A novel fusion sampling strategy in SA layers is developed to keep adequate interior points of different foreground instances, which preserves rich information for regression and classification.
-> - We design a delicate box prediction network, making our framework both effective and efficient further. Experimental results on KITTI and nuScenes dataset show that our framework outperforms all single stage methods, and has comparable performance to state-of-the-art two stage methods with a much faster speed, which is 38ms per scene.
 
 ## どうやって有効だと検証した?
 他モデルと比べた際の結果は以下の通り。

diff --git a/data/KPConv Flexible and Deformable Convolution for Point Clouds.md b/data/KPConv Flexible and Deformable Convolution for Point Clouds.md
@@ -75,7 +75,7 @@ python train_ModelNet40.py
 ```
 
 ## どんなもの?
-### 新規の点畳込み演算子、Kernel Point Convolution (KPConv)を提案した。
+- 新規の点畳込み演算子、Kernel Point Convolution (KPConv)を提案した。
 - >  KPConv also consists of a set of local 3D filters, but overcomes previous point convolution limitations as shown in related work.
   - "previous point convolution" are Projection networks, Graph convolution networks, Pointwise MLP networks, Point convolution networks.
   - > PCNN [2] design is the closest to KPConv.

diff --git a/...ointNeXt Revisiting PointNet++ with Improved Training and Scaling Strategies.md b/...ointNeXt Revisiting PointNet++ with Improved Training and Scaling Strategies.md
@@ -84,7 +84,6 @@ CUDA_VISIBLE_DEVICES=0 python examples/segmentation/main.py --cfg cfgs/s3dis/poi
 ```
 
 ## どんなもの?
-### PointNet++をベースとして改修したモデル、PointNeXtを提案した。
 - > In this work, we revisit PointNet++, the classical and widely used network, and find that its full potential has yet to be explored, mainly due to two factors that were not present at the time of PointNet++: (1) superior training strategies and (2) effective model scaling strategies.
   1. > Through a comprehensive empirical study on various benchmarks, e.g., ScanObjecNN [48] for object classification and S3DIS [1] for semantic segmentation, we discover that training strategies, i.e., data augmentation and optimization techniques, play an important role in the network’s performance. In fact, a large part of the performance gain of state-of-the-art (SOTA) methods [51, 47, 62] over PointNet++ [32] is due to improved training strategies that are, unfortunately, less publicized compared to architectural changes.
   2.  > Effectively expanding PointNet++ from its original small scale to a larger scale is a topic worth studying because larger models are generally expected to enable richer representations and perform better [2, 18, 61]. However, we find that the naive way of using more building blocks or increasing the channel size in PointNet++ only leads to an overhead in latency and no significant improvement in accuracy (see Sec. 4.4.2). For effective and efficient model scaling, we introduce residual connections [14], an inverted bottleneck design [40], and separable MLPs [34] into PointNet++.
@@ -94,9 +93,6 @@ CUDA_VISIBLE_DEVICES=0 python examples/segmentation/main.py --cfg cfgs/s3dis/poi
 
 > Figure 2: PointNeXt architecture. PointNeXt shares the same Set Abstraction and Feature Propagation blocks as PointNet++ [32], while adding an additional MLP layer at the beginning and scaling the architecture with the proposed Inverted Residual MLP (InvResMLP) blocks. Here, we present PointNeXt for segmentation as an example. Refer to Appendix for the classification architecture.
 
-### Contribution
-> - We present the first systematic study of training strategies in the point cloud domain and show that PointNet++ strikes back (+8.2% OA on ScanObjectNN and +13.6% mIoU on S3DIS) by simply adopting improved training strategies alone. The improved training strategies are general and can be easily applied to improve other methods [31, 51, 29].
-> - We propose PointNeXt, the next version of PointNets. PointNeXt is scalable and surpasses SOTA on all tasks studied, including object classification [48], semantic segmentation [1], and part segmentation [59], while being faster than SOTA in inference.
 
 ## どうやって有効だと検証した?
 他モデルと比べた際の結果は以下の通り。