Augmenting LiDAR input with multiple previous frames provides richer semantic
information and thus boosts performance in 3D object detection, However,
crowded point clouds in multi-frames can hurt the precise position information
due to the motion blur and inaccurate point projection. In this work, we
propose a novel feature fusion strategy, DynStaF (Dynamic-Static Fusion), which
enhances the rich semantic information provided by the multi-frame (dynamic
branch) with the accurate location information from the current single-frame
(static branch). To effectively extract and aggregate complimentary features,
DynStaF contains two modules, Neighborhood Cross Attention (NCA) and
Dynamic-Static Interaction (DSI), operating through a dual pathway
architecture. NCA takes the features in the static branch as queries and the
features in the dynamic branch as keys (values). When computing the attention,
we address the sparsity of point clouds and take only neighborhood positions
into consideration. NCA fuses two features at different feature map scales,
followed by DSI providing the comprehensive interaction. To analyze our
proposed strategy DynStaF, we conduct extensive experiments on the nuScenes
dataset. On the test set, DynStaF increases the performance of PointPillars in
NDS by a large margin from 57.7% to 61.6%. When combined with CenterPoint, our
framework achieves 61.0% mAP and 67.7% NDS, leading to state-of-the-art
performance without bells and whistles.

本文提出了一种新的特征融合策略 DynStaF，它通过动态分支和静态分支的结合来增强多帧提供的丰富的语义信息，并有效提取和聚合补充特征，DynStaF 包含两个模块，一个是邻域交叉注意力模块（NCA），另一个是动静交互模块（DSI），经在追踪算法中的大量实验验证，DynStaF 相较于原有算法提高了性能。

DynStatF: 一种高效的用于 LiDAR 3D 目标检测的特征融合策略

DynStatF: An Efficient Feature Fusion Strategy for LiDAR 3D Object  Detection

Multimodal sentiment analysis is a very actively growing field of research. A
promising area of opportunity in this field is to improve the multimodal fusion
mechanism. We present a novel feature fusion strategy that proceeds in a
hierarchical fashion, first fusing the modalities two in two and only then
fusing all three modalities. On multimodal sentiment analysis of individual
utterances, our strategy outperforms conventional concatenation of features by
1%, which amounts to 5% reduction in error rate. On utterance-level multimodal
sentiment analysis of multi-utterance video clips, for which current
state-of-the-art techniques incorporate contextual information from other
utterances of the same clip, our hierarchical fusion gives up to 2.4% (almost
10% error rate reduction) over currently used concatenation. The implementation
of our method is publicly available in the form of open-source code.

本论文提出了一种新的特征融合策略并应用在情感分析中，该策略通过分层融合两种模态，然后再将其与第三种模态融合，在个别话语的情感分析和视频剪辑的情感分析中均取得了优异的效果。