Part-aware panoptic segmentation (PPS) requires (a) that each foreground
object and background region in an image is segmented and classified, and (b)
that all parts within foreground objects are segmented, classified and linked
to their parent object. Existing methods approach PPS by separately conducting
object-level and part-level segmentation. However, their part-level predictions
are not linked to individual parent objects. Therefore, their learning
objective is not aligned with the PPS task objective, which harms the PPS
performance. To solve this, and make more accurate PPS predictions, we propose
Task-Aligned Part-aware Panoptic Segmentation (TAPPS). This method uses a set
of shared queries to jointly predict (a) object-level segments, and (b) the
part-level segments within those same objects. As a result, TAPPS learns to
predict part-level segments that are linked to individual parent objects,
aligning the learning objective with the task objective, and allowing TAPPS to
leverage joint object-part representations. With experiments, we show that
TAPPS considerably outperforms methods that predict objects and parts
separately, and achieves new state-of-the-art PPS results.

通过使用一组共享的查询，我们提出了任务对齐的部分感知全景分割 (TAPPS) 方法，用于共同预测对象级别的分割以及这些对象内的部分级别分割，从而将学习目标与任务目标对齐，在全景分割任务中取得了新的最先进结果。

通过联合物体 - 部分表示实现任务对齐的部分感知全景分割

Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part  Representations

Most existing RGB-D salient object detection (SOD) methods focus on the
foreground region when utilizing the depth images. However, the background also
provides important information in traditional SOD methods for promising
performance. To better explore salient information in both foreground and
background regions, this paper proposes a Bilateral Attention Network (BiANet)
for the RGB-D SOD task. Specifically, we introduce a Bilateral Attention Module
(BAM) with a complementary attention mechanism: foreground-first (FF) attention
and background-first (BF) attention. The FF attention focuses on the foreground
region with a gradual refinement style, while the BF one recovers potentially
useful salient information in the background region. Benefitted from the
proposed BAM module, our BiANet can capture more meaningful foreground and
background cues, and shift more attention to refining the uncertain details
between foreground and background regions. Additionally, we extend our BAM by
leveraging the multi-scale techniques for better SOD performance. Extensive
experiments on six benchmark datasets demonstrate that our BiANet outperforms
other state-of-the-art RGB-D SOD methods in terms of objective metrics and
subjective visual comparison. Our BiANet can run up to 80fps on $224\times224$
RGB-D images, with an NVIDIA GeForce RTX 2080Ti GPU. Comprehensive ablation
studies also validate our contributions.

本文提出了一种双边注意力网络 (BiANet) 来探索前景和背景区域中的显著信息，其中介绍了一种双边注意力模块 (BAM)，通过前景优先和背景优先的注意机制来获取前景和背景信息，以更好地优化 RGB-D 任务的性能。基于多尺度和 BAM 技术，我们的 BiANet 在六个基准数据集上实现了显著优越的性能。