Critical research about camera-and-LiDAR-based semantic object segmentation
for autonomous driving significantly benefited from the recent development of
deep learning. Specifically, the vision transformer is the novel ground-breaker
that successfully brought the multi-head-attention mechanism to computer vision
applications. Therefore, we propose a vision-transformer-based network to carry
out camera-LiDAR fusion for semantic segmentation applied to autonomous
driving. Our proposal uses the novel progressive-assemble strategy of vision
transformers on a double-direction network and then integrates the results in a
cross-fusion strategy over the transformer decoder layers. Unlike other works
in the literature, our camera-LiDAR fusion transformers have been evaluated in
challenging conditions like rain and low illumination, showing robust
performance. The paper reports the segmentation results over the vehicle and
human classes in different modalities: camera-only, LiDAR-only, and
camera-LiDAR fusion. We perform coherent controlled benchmark experiments of
CLFT against other networks that are also designed for semantic segmentation.
The experiments aim to evaluate the performance of CLFT independently from two
perspectives: multimodal sensor fusion and backbone architectures. The
quantitative assessments show our CLFT networks yield an improvement of up to
10\% for challenging dark-wet conditions when comparing with
Fully-Convolutional-Neural-Network-based (FCN) camera-LiDAR fusion neural
network. Contrasting to the network with transformer backbone but using single
modality input, the all-around improvement is 5-10\%.

提出了基于 Vision Transformer 的网络进行相机 - LiDAR 融合，用于自动驾驶的语义分割，成功地在挑战性条件下如雨天和低照明环境下展示了健壮的性能。

CLFT：用于自动驾驶语义分割的相机 - LiDAR 融合变压器

CLFT: Camera-LiDAR Fusion Transformer for Semantic Segmentation in  Autonomous Driving

Semantic segmentation is a key prerequisite to robust image understanding for
applications in \acrlong{ai} and Robotics. \acrlong{fss}, in particular,
concerns the extension and optimization of traditional segmentation methods in
challenging conditions where limited training examples are available. A
predominant approach in \acrlong{fss} is to rely on a single backbone for
visual feature extraction. Choosing which backbone to leverage is a deciding
factor contributing to the overall performance. In this work, we interrogate on
whether fusing features from different backbones can improve the ability of
\acrlong{fss} models to capture richer visual features. To tackle this
question, we propose and compare two ensembling techniques-Independent Voting
and Feature Fusion. Among the available \acrlong{fss} methods, we implement the
proposed ensembling techniques on PANet. The module dedicated to predicting
segmentation masks from the backbone embeddings in PANet avoids trainable
parameters, creating a controlled `in vitro' setting for isolating the impact
of different ensembling strategies. Leveraging the complementary strengths of
different backbones, our approach outperforms the original single-backbone
PANet across standard benchmarks even in challenging one-shot learning
scenarios. Specifically, it achieved a performance improvement of +7.37\% on
PASCAL-5\textsuperscript{i} and of +10.68\% on COCO-20\textsuperscript{i} in
the top-performing scenario where three backbones are combined. These results,
together with the qualitative inspection of the predicted subject masks,
suggest that relying on multiple backbones in PANet leads to a more
comprehensive feature representation, thus expediting the successful
application of \acrlong{fss} methods in challenging, data-scarce environments.

利用多个骨干网络在 PANet 中的特征融合可以提高在挑战性、数据稀缺环境中对全景分割方法的性能。

超越局部：集成骨干网络进行少样本分割

More than the Sum of Its Parts: Ensembling Backbone Networks for  Few-Shot Segmentation

In this paper, we investigate the robustness of traffic sign recognition
algorithms under challenging conditions. Existing datasets are limited in terms
of their size and challenging condition coverage, which motivated us to
generate the Challenging Unreal and Real Environments for Traffic Sign
Recognition (CURE-TSR) dataset. It includes more than two million traffic sign
images that are based on real-world and simulator data. We benchmark the
performance of existing solutions in real-world scenarios and analyze the
performance variation with respect to challenging conditions. We show that
challenging conditions can decrease the performance of baseline methods
significantly, especially if these challenging conditions result in loss or
misplacement of spatial information. We also investigate the effect of data
augmentation and show that utilization of simulator data along with real-world
data enhance the average recognition performance in real-world scenarios. The
dataset is publicly available at this https URL

研究交通标志识别算法在挑战性条件下的鲁棒性，并构建了基于真实世界和模拟数据的 CURE-TSR 数据集，分析了现有解决方案在真实世界场景下的性能，并显示出挑战性条件会显著降低基线方法的性能。此外，我们还调查了数据增强的效果，并表明与真实世界数据一起利用模拟数据可以增强真实世界场景中的平均识别性能。

CURE-TSR: 面向交通标志识别挑战虚拟和真实环境

CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign  Recognition

Robust face detection is one of the most important pre-processing steps to
support facial expression analysis, facial landmarking, face recognition, pose
estimation, building of 3D facial models, etc. Although this topic has been
intensely studied for decades, it is still challenging due to numerous variants
of face images in real-world scenarios. In this paper, we present a novel
approach named Multiple Scale Faster Region-based Convolutional Neural Network
(MS-FRCNN) to robustly detect human facial regions from images collected under
various challenging conditions, e.g. large occlusions, extremely low
resolutions, facial expressions, strong illumination variations, etc. The
proposed approach is benchmarked on two challenging face detection databases,
i.e. the Wider Face database and the Face Detection Dataset and Benchmark
(FDDB), and compared against recent other face detection methods, e.g.
Two-stage CNN, Multi-scale Cascade CNN, Faceness, Aggregate Chanel Features,
HeadHunter, Multi-view Face Detection, Cascade CNN, etc. The experimental
results show that our proposed approach consistently achieves highly
competitive results with the state-of-the-art performance against other recent
face detection methods.

本文提出了一种名为 MS-FRCNN 的新方法，它可以鲁棒地检测在面临各种困难的情况下搜集的图像中的人类面部区域，基于 Wider Face 数据库和 Face Detection 数据集与基准（FDDB）对比表明，该方法在各种情况下相对于其他最新的人脸检测方法，始终保持高度竞争力的最新表现。