Named entity recognition (NER) is a fundamental task in natural language
processing. Recent works treat named entity recognition as a reading
comprehension task, constructing type-specific queries manually to extract
entities. This paradigm suffers from three issues. First, type-specific queries
can only extract one type of entities per inference, which is inefficient.
Second, the extraction for different types of entities is isolated, ignoring
the dependencies between them. Third, query construction relies on external
knowledge and is difficult to apply to realistic scenarios with hundreds of
entity types. To deal with them, we propose Parallel Instance Query Network
(PIQN), which sets up global and learnable instance queries to extract entities
from a sentence in a parallel manner. Each instance query predicts one entity,
and by feeding all instance queries simultaneously, we can query all entities
in parallel. Instead of being constructed from external knowledge, instance
queries can learn their different query semantics during training. For training
the model, we treat label assignment as a one-to-many Linear Assignment Problem
(LAP) and dynamically assign gold entities to instance queries with minimal
assignment cost. Experiments on both nested and flat NER datasets demonstrate
that our proposed method outperforms previous state-of-the-art models.

提出了 Parallel Instance Query Network (PIQN)，并将标签分配视为基于 Linear Assignment Problem (LAP) 的一对多问题，通过全局可学习实例查询并行提取命名实体，取得了比之前最先进的模型更好的表现。

用于命名实体识别的并行实例查询网络

Parallel Instance Query Network for Named Entity Recognition

Differentiable solvers for the linear assignment problem (LAP) have attracted
much research attention in recent years, which are usually embedded into
learning frameworks as components. However, previous algorithms, with or
without learning strategies, usually suffer from the degradation of the
optimality with the increment of the problem size. In this paper, we propose a
learnable linear assignment solver based on deep graph networks. Specifically,
we first transform the cost matrix to a bipartite graph and convert the
assignment task to the problem of selecting reliable edges from the constructed
graph. Subsequently, a deep graph network is developed to aggregate and update
the features of nodes and edges. Finally, the network predicts a label for each
edge that indicates the assignment relationship. The experimental results on a
synthetic dataset reveal that our method outperforms state-of-the-art baselines
and achieves consistently high accuracy with the increment of the problem size.
Furthermore, we also embed the proposed solver, in comparison with
state-of-the-art baseline solvers, into a popular multi-object tracking (MOT)
framework to train the tracker in an end-to-end manner. The experimental
results on MOT benchmarks illustrate that the proposed LAP solver improves the
tracker by the largest margin.

本文提出了一种可学习的线性分配求解器，基于深度图网络实现，将费用矩阵转换为二分图，并通过节点和边的特征聚合和更新来选择可靠边，实验结果表明，与现有算法相比，该方法在保证准确性的前提下，能有效提高处理规模。此外，该分配求解器在多目标跟踪领域有着广泛的应用。

GLAN: 基于图的线性分配网络

GLAN: A Graph-based Linear Assignment Network

The graph matching problem seeks to find an alignment between the nodes of
two graphs that minimizes the number of adjacency disagreements. Solving the
graph matching is increasingly important due to it's applications in operations
research, computer vision, neuroscience, and more. However, current
state-of-the-art algorithms are inefficient in matching very large graphs,
though they produce good accuracy. The main computational bottleneck of these
algorithms is the linear assignment problem, which must be solved at each
iteration. In this paper, we leverage the recent advances in the field of
optimal transport to replace the accepted use of linear assignment algorithms.
We present GOAT, a modification to the state-of-the-art graph matching
approximation algorithm "FAQ" (Vogelstein, 2015), replacing its linear sum
assignment step with the "Lightspeed Optimal Transport" method of Cuturi
(2013). The modification provides improvements to both speed and empirical
matching accuracy. The effectiveness of the approach is demonstrated in
matching graphs in simulated and real data examples.

本文提出一种名为 GOAT 的修正版算法，它使用最优输运的方法替代了线性分配算法，以加快计算和提高准确性，用于解决图匹配问题。

最优输运在图匹配中的应用

Graph Matching via Optimal Transport

In this paper, we propose the differentiable mask-matching network (DMM-Net)
for solving the video object segmentation problem where the initial object
masks are provided. Relying on the Mask R-CNN backbone, we extract mask
proposals per frame and formulate the matching between object templates and
proposals at one time step as a linear assignment problem where the cost matrix
is predicted by a CNN. We propose a differentiable matching layer by unrolling
a projected gradient descent algorithm in which the projection exploits the
Dykstra's algorithm. We prove that under mild conditions, the matching is
guaranteed to converge to the optimum. In practice, it performs similarly to
the Hungarian algorithm during inference. Meanwhile, we can back-propagate
through it to learn the cost matrix. After matching, a refinement head is
leveraged to improve the quality of the matched mask. Our DMM-Net achieves
competitive results on the largest video object segmentation dataset
YouTube-VOS. On DAVIS 2017, DMM-Net achieves the best performance without
online learning on the first frames. Without any fine-tuning, DMM-Net performs
comparably to state-of-the-art methods on SegTrack v2 dataset. At last, our
matching layer is very simple to implement; we attach the PyTorch code ($<50$
lines) in the supplementary material. Our code is released at
this https URL

本文提出了可微的掩膜匹配网络 (DMM-Net)，用于解决视频目标分割问题，其基于 Mask R-CNN 骨干网络，通过将目标模板和帧中提取的掩膜候选匹配，提出了一种解决线性分配问题的算法，在保证收敛至最优时进行反向传播以学习成本矩阵，最后使用精化头部网络改善匹配掩膜的质量，并通过在 YouTube-VOS 数据集上创造有竞争力的结果来证明其有效性。