This work proposes a novel approach for hand gesture recognition using an
inexpensive, low-resolution (24 x 32) thermal sensor processed by a Spiking
Neural Network (SNN) followed by Sparse Segmentation and feature-based gesture
classification via Robust Principal Component Analysis (R-PCA). Compared to the
use of standard RGB cameras, the proposed system is insensitive to lighting
variations while being significantly less expensive compared to high-frequency
radars, time-of-flight cameras and high-resolution thermal sensors previously
used in literature. Crucially, this paper shows that the innovative use of the
recently proposed Monostable Multivibrator (MMV) neural networks as a new class
of SNN achieves more than one order of magnitude smaller memory and compute
complexity compared to deep learning approaches, while reaching a top gesture
recognition accuracy of 93.9% using a 5-class thermal camera dataset acquired
in a car cabin, within an automotive context. Our dataset is released for
helping future research.

使用低分辨率 (24 x 32) 热传感器，结合脉冲神经网络、稀疏分割和基于特征的手势分类，提出了一种新颖的手势识别方法。与标准 RGB 摄像头相比，该系统对光照变化不敏感，且价格显著低于以往文献中使用的高频雷达、飞行时间摄像头和高分辨率热传感器。关键是，本文通过创新地使用最近提出的单稳态多谐振荡器 (MMV) 神经网络作为新型脉冲神经网络，在记忆和计算复杂度方面较深度学习方法小一个数量级，同时在汽车环境下使用 5 类热相机数据集达到 93.9% 的手势识别准确率。我们提供了该数据集以供未来研究使用。

利用低分辨率热成像相机及脉冲神经网络与稀疏分割实现资源高效的姿势识别

Resource-Efficient Gesture Recognition using Low-Resolution Thermal  Camera via Spiking Neural Networks and Sparse Segmentation

Bird's-eye View (BeV) representations have emerged as the de-facto shared
space in driving applications, offering a unified space for sensor data fusion
and supporting various downstream tasks. However, conventional models use grids
with fixed resolution and range and face computational inefficiencies due to
the uniform allocation of resources across all cells. To address this, we
propose PointBeV, a novel sparse BeV segmentation model operating on sparse BeV
cells instead of dense grids. This approach offers precise control over memory
usage, enabling the use of long temporal contexts and accommodating
memory-constrained platforms. PointBeV employs an efficient two-pass strategy
for training, enabling focused computation on regions of interest. At inference
time, it can be used with various memory/performance trade-offs and flexibly
adjusts to new specific use cases. PointBeV achieves state-of-the-art results
on the nuScenes dataset for vehicle, pedestrian, and lane segmentation,
showcasing superior performance in static and temporal settings despite being
trained solely with sparse signals. We will release our code along with two new
efficient modules used in the architecture: Sparse Feature Pulling, designed
for the effective extraction of features from images to BeV, and Submanifold
Attention, which enables efficient temporal modeling. Our code is available at
this https URL

提出了 PointBeV 方法，在稀疏的 Bird's-eye View 表示中进行分割模型操作，以有效控制内存使用，并在静态和时间设置中展示卓越性能。

PointBeV：基于稀疏方法的 BeV 预测

PointBeV: A Sparse Approach to BeV Predictions

Image Segmentation has been an active field of research as it has a wide
range of applications, ranging from automated disease detection to self-driving
cars. In the past five years, various papers came up with different objective
loss functions used in different cases such as biased data, sparse
segmentation, etc. In this paper, we have summarized some of the well-known
loss functions widely used for Image Segmentation and listed out the cases
where their usage can help in fast and better convergence of a model.
Furthermore, we have also introduced a new log-cosh dice loss function and
compared its performance on the NBFS skull-segmentation open-source data-set
with widely used loss functions. We also showcased that certain loss functions
perform well across all data-sets and can be taken as a good baseline choice in
unknown data distribution scenarios. Our code is available at Github:
this https URL

本文介绍了广泛用于图像分割的知名损失函数，并列出了能够帮助模型快速优化的情况。同时，我们还介绍了一种新的 Log-cosh Dice 损失函数，并与广泛使用的损失函数在 NBFS 颅骨分割开源数据集上进行了比较。我们还展示了某些损失函数在所有数据集上表现良好，并可作为未知数据分布情况下的良好基准选择。欲了解更多信息，请访问 Github 链接。