Weakly supervised segmentation methods have gained significant attention due
to their ability to reduce the reliance on costly pixel-level annotations
during model training. However, the current weakly supervised nuclei
segmentation approaches typically follow a two-stage pseudo-label generation
and network training process. The performance of the nuclei segmentation
heavily relies on the quality of the generated pseudo-labels, thereby limiting
its effectiveness. This paper introduces a novel domain-adaptive weakly
supervised nuclei segmentation framework using cross-task interaction
strategies to overcome the challenge of pseudo-label generation. Specifically,
we utilize weakly annotated data to train an auxiliary detection task, which
assists the domain adaptation of the segmentation network. To enhance the
efficiency of domain adaptation, we design a consistent feature constraint
module integrating prior knowledge from the source domain. Furthermore, we
develop pseudo-label optimization and interactive training methods to improve
the domain transfer capability. To validate the effectiveness of our proposed
method, we conduct extensive comparative and ablation experiments on six
datasets. The results demonstrate the superiority of our approach over existing
weakly supervised approaches. Remarkably, our method achieves comparable or
even better performance than fully supervised methods. Our code will be
released in this https URL

使用领域自适应的弱监督核分割框架，通过交叉任务相互作用策略克服伪标签生成的挑战，并通过弱注释数据训练辅助检测任务，设计一种一致特征约束模块来提高领域自适应的效率。此外，我们还开发了伪标签优化和交互式训练方法来提升领域转移能力，实验证明我们的方法在六个数据集上具有优越性能。

DAWN: 基于跨任务相互作用的领域自适应弱监督核分割

DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via  Cross-Task Interactions

The detection head constitutes a pivotal component within object detectors,
tasked with executing both classification and localization functions.
Regrettably, the commonly used parallel head often lacks omni perceptual
capabilities, such as deformation perception, global perception and cross-task
perception. Despite numerous methods attempt to enhance these abilities from a
single aspect, achieving a comprehensive and unified solution remains a
significant challenge. In response to this challenge, we have developed an
innovative detection head, termed UniHead, to unify three perceptual abilities
simultaneously. More precisely, our approach (1) introduces deformation
perception, enabling the model to adaptively sample object features; (2)
proposes a Dual-axial Aggregation Transformer (DAT) to adeptly model long-range
dependencies, thereby achieving global perception; and (3) devises a Cross-task
Interaction Transformer (CIT) that facilitates interaction between the
classification and localization branches, thus aligning the two tasks. As a
plug-and-play method, the proposed UniHead can be conveniently integrated with
existing detectors. Extensive experiments on the COCO dataset demonstrate that
our UniHead can bring significant improvements to many detectors. For instance,
the UniHead can obtain +2.7 AP gains in RetinaNet, +2.9 AP gains in FreeAnchor,
and +2.1 AP gains in GFL. The code will be publicly available. Code Url:
this https URL

我们提出了一种名为 UniHead 的创新检测头，同时统一了三种感知能力：变形感知、全局感知和跨任务交互，使得我们的方法在 COCO 数据集上显著改进了多个检测器的性能。

UniHead：统一多感知检测算法

UniHead: Unifying Multi-Perception for Detection Heads

Multi-task scene understanding aims to design models that can simultaneously
predict several scene understanding tasks with one versatile model. Previous
studies typically process multi-task features in a more local way, and thus
cannot effectively learn spatially global and cross-task interactions, which
hampers the models' ability to fully leverage the consistency of various tasks
in multi-task learning. To tackle this problem, we propose an Inverted Pyramid
multi-task Transformer, capable of modeling cross-task interaction among
spatial features of different tasks in a global context. Specifically, we first
utilize a transformer encoder to capture task-generic features for all tasks.
And then, we design a transformer decoder to establish spatial and cross-task
interaction globally, and a novel UP-Transformer block is devised to increase
the resolutions of multi-task features gradually and establish cross-task
interaction at different scales. Furthermore, two types of Cross-Scale
Self-Attention modules, i.e., Fusion Attention and Selective Attention, are
proposed to efficiently facilitate cross-task interaction across different
feature scales. An Encoder Feature Aggregation strategy is further introduced
to better model multi-scale information in the decoder. Comprehensive
experiments on several 2D/3D multi-task benchmarks clearly demonstrate our
proposal's effectiveness, establishing significant state-of-the-art
performances.

提出了一个可以在全局情境下建立不同任务的空间特征之间交互的倒金字塔多任务转换器，以及两种跨尺度自注意模块，以有效地促进跨任务交互。

InvPT++：倒立金字塔多任务变压器用于视觉场景理解

InvPT++: Inverted Pyramid Multi-Task Transformer for Visual Scene  Understanding

This paper addresses the question of how to efficiently learn from disjoint,
compatible label sequences. We argue that the compatible structures between
disjoint label sets help model learning and inference. We verify this
hypothesis on the task of semantic role labeling (SRL), specifically, tagging a
sentence with two role sequences: VerbNet arguments and PropBank arguments.
Prior work has shown that cross-task interaction improves performance. However,
the two tasks are still separately decoded, running the risk of generating
structurally inconsistent label sequences (as per lexicons like SEMLINK). To
eliminate this issue, we first propose a simple and effective setup that
jointly handles VerbNet and PropBank labels as one sequence. With this setup,
we show that enforcing SEMLINK constraints during decoding constantly improves
the overall F1. With special input constructions, our joint model infers
VerbNet arguments from PropBank arguments with over 99% accuracy. We also
propose a constrained marginal model that uses SEMLINK information during
training to further benefit from the large amounts of PropBank-only data. Our
models achieve state-of-the-art F1's on VerbNet and PropBank argument labeling
on the CoNLL05 dataset with strong out-of-domain generalization.

通过将句子中两个角色序列共同处理，并在解码过程中强制执行 SEMLINK 约束条件，提高了语义角色标注任务的性能，并在 CoNLL05 数据集上取得了最先进的 F1 分数。