Multi-task networks can potentially improve performance and computational
efficiency compared to single-task networks, facilitating online deployment.
However, current multi-task architectures in point cloud perception combine
multiple task-specific point cloud representations, each requiring a separate
feature encoder and making the network structures bulky and slow. We propose
PAttFormer, an efficient multi-task architecture for joint semantic
segmentation and object detection in point clouds that only relies on a
point-based representation. The network builds on transformer-based feature
encoders using neighborhood attention and grid-pooling and a query-based
detection decoder using a novel 3D deformable-attention detection head design.
Unlike other LiDAR-based multi-task architectures, our proposed PAttFormer does
not require separate feature encoders for multiple task-specific point cloud
representations, resulting in a network that is 3x smaller and 1.4x faster
while achieving competitive performance on the nuScenes and KITTI benchmarks
for autonomous driving perception. Our extensive evaluations show substantial
gains from multi-task learning, improving LiDAR semantic segmentation by +1.7%
in mIou and 3D object detection by +1.7% in mAP on the nuScenes benchmark
compared to the single-task models.

提出了一种高效的多任务架构 PAttFormer，在点云中实现联合语义分割和目标检测，相较于单一任务网络，不需要多个特定任务点云表示的独立特征编码器，结构更小更快，同时在自动驾驶感知领域的 nuScenes 和 KITTI 基准测试中实现了竞争性的性能，多任务学习表现出显著的增益。

高效激光雷达多任务感知的基于点的方法

A Point-Based Approach to Efficient LiDAR Multi-Task Perception

Object detection and semantic segmentation are pivotal components in
biomedical image analysis. Current single-task networks exhibit promising
outcomes in both detection and segmentation tasks. Multi-task networks have
gained prominence due to their capability to simultaneously tackle segmentation
and detection tasks, while also accelerating the segmentation inference.
Nevertheless, recent multi-task networks confront distinct limitations such as
the difficulty in striking a balance between accuracy and inference speed.
Additionally, they often overlook the integration of cross-scale features,
which is especially important for biomedical image analysis. In this study, we
propose an efficient end-to-end multi-task network capable of concurrently
performing object detection and semantic segmentation called YOLO-Med. Our
model employs a backbone and a neck for multi-scale feature extraction,
complemented by the inclusion of two task-specific decoders. A cross-scale
task-interaction module is employed in order to facilitate information fusion
between various tasks. Our model exhibits promising results in balancing
accuracy and speed when evaluated on the Kvasir-seg dataset and a private
biomedical image dataset.

本研究提出了一种高效的端到端多任务网络 YOLO-Med，能够同时进行目标检测和语义分割，通过多尺度特征提取和任务特定解码器的结合，以及跨尺度任务交互模块的引入，实现了准确性和速度的平衡，并在 Kvasir-seg 数据集和私有生物医学图像数据集上获得了良好的结果。

YOLO-MED：生物医学图像的多任务交互网络

YOLO-MED : Multi-Task Interaction Network for Biomedical Images

Multi-task networks are commonly utilized to alleviate the need for a large
number of highly specialized single-task networks. However, two common
challenges in developing multi-task models are often overlooked in literature.
First, enabling the model to be inherently incremental, continuously
incorporating information from new tasks without forgetting the previously
learned ones (incremental learning). Second, eliminating adverse interactions
amongst tasks, which has been shown to significantly degrade the single-task
performance in a multi-task setup (task interference). In this paper, we show
that both can be achieved simply by reparameterizing the convolutions of
standard neural network architectures into a non-trainable shared part (filter
bank) and task-specific parts (modulators), where each modulator has a fraction
of the filter bank parameters. Thus, our reparameterization enables the model
to learn new tasks without adversely affecting the performance of existing
ones. The results of our ablation study attest the efficacy of the proposed
reparameterization. Moreover, our method achieves state-of-the-art on two
challenging multi-task learning benchmarks, PASCAL-Context and NYUD, and also
demonstrates superior incremental learning capability as compared to its close
competitors.

该研究提出了一种新的重新参数化方法，通过将标准神经网络体系结构的卷积重新参数化成一个不可训练的共享部分（滤波器银行）和任务特定部分（调制器），从而使模型能够学习新任务而不会对现有任务的性能产生负面影响。该方法在 PASCAL-Context 和 NYUD 两个任务上达到了最先进的表现，并且证明了其比竞争对手具有更优秀的增量学习能力。