Radars, due to their robustness to adverse weather conditions and ability to
measure object motions, have served in autonomous driving and intelligent
agents for years. However, Radar-based perception suffers from its unintuitive
sensing data, which lack of semantic and structural information of scenes. To
tackle this problem, camera and Radar sensor fusion has been investigated as a
trending strategy with low cost, high reliability and strong maintenance. While
most recent works explore how to explore Radar point clouds and images, rich
contextual information within Radar observation are discarded. In this paper,
we propose a hybrid point-wise Radar-Optical fusion approach for object
detection in autonomous driving scenarios. The framework benefits from dense
contextual information from both the range-doppler spectrum and images which
are integrated to learn a multi-modal feature representation. Furthermore, we
propose a novel local coordinate formulation, tackling the object detection
task in an object-centric coordinate. Extensive results show that with the
information gained from optical images, we could achieve leading performance in
object detection (97.69\% recall) compared to recent state-of-the-art methods
FFT-RadNet (82.86\% recall). Ablation studies verify the key design choices and
practicability of our approach given machine generated imperfect detections.
The code will be available at this https URL

本文提出一种混合逐点雷达 - 光学融合方法，结合了来自范围 - 多普勒频谱和光学图像的密集上下文信息，用于自动驾驶场景中的目标检测，表现优于最近的 FFT-RadNet 方法。

ROFusion: 利用混合点云雷达 - 光学融合实现高效物体检测

ROFusion: Efficient Object Detection using Hybrid Point-wise  Radar-Optical Fusion

Visual grounding aims to localize an object in an image referred to by a
textual query phrase. Various visual grounding approaches have been proposed,
and the problem can be modularized into a general framework: proposal
generation, multi-modal feature representation, and proposal ranking. Of these
three modules, most existing approaches focus on the latter two, with the
importance of proposal generation generally neglected. In this paper, we
rethink the problem of what properties make a good proposal generator. We
introduce the diversity and discrimination simultaneously when generating
proposals, and in doing so propose Diversified and Discriminative Proposal
Networks model (DDPN). Based on the proposals generated by DDPN, we propose a
high performance baseline model for visual grounding and evaluate it on four
benchmark datasets. Experimental results demonstrate that our model delivers
significant improvements on all the tested data-sets (e.g., 18.8\% improvement
on ReferItGame and 8.2\% improvement on Flickr30k Entities over the existing
state-of-the-arts respectively)

本文介绍了 Diversified and Discriminative Proposal Networks 模型（DDPN），并在此基础上提出了一个高性能的视觉定位基线模型，通过在四个基准数据集上进行评估，证明了该模型在所有测试数据集上都有显着改善。