Diffusion-driven text-to-image (T2I) generation has achieved remarkable
advancements. To further improve T2I models' capability in numerical and
spatial reasoning, the layout is employed as an intermedium to bridge large
language models and layout-based diffusion models. However, these methods still
struggle with generating images from textural prompts with multiple objects and
complicated spatial relationships. To tackle this challenge, we introduce a
divide-and-conquer approach which decouples the T2I generation task into simple
subtasks. Our approach divides the layout prediction stage into numerical \&
spatial reasoning and bounding box prediction. Then, the layout-to-image
generation stage is conducted in an iterative manner to reconstruct objects
from easy ones to difficult ones. We conduct experiments on the HRS and NSR-1K
benchmarks and our approach outperforms previous state-of-the-art models with
notable margins. In addition, visual results demonstrate that our approach
significantly improves the controllability and consistency in generating
multiple objects from complex textural prompts.

通过采用分割和征服方法，我们改进了传统的扩散式文本到图像生成模型，使其在多个对象和复杂的空间关系下从文本生成图像，提高了可控性和一致性。

DivCon: 渐进式文本到图像生成的分割与征服

DivCon: Divide and Conquer for Progressive Text-to-Image Generation

As the number of recorded meetings increases, it becomes increasingly
important to utilize summarization technology to create useful summaries of
these recordings. However, there is a crucial lack of annotated meeting corpora
for developing this technology, as it can be hard to collect meetings,
especially when the topics discussed are confidential. Furthermore, meeting
summaries written by experienced writers are scarce, making it hard for
abstractive summarizers to produce sensible output without a reliable
reference. This lack of annotated corpora has hindered the development of
meeting summarization technology. In this paper, we present MeetingBank, a new
benchmark dataset of city council meetings over the past decade. MeetingBank is
unique among other meeting corpora due to its divide-and-conquer approach,
which involves dividing professionally written meeting minutes into shorter
passages and aligning them with specific segments of the meeting. This breaks
down the process of summarizing a lengthy meeting into smaller, more manageable
tasks. The dataset provides a new testbed of various meeting summarization
systems and also allows the public to gain insight into how council decisions
are made. We make the collection, including meeting video links, transcripts,
reference summaries, agenda, and other metadata, publicly available to
facilitate the development of better meeting summarization techniques. Our
dataset can be accessed at: this https URL

本文介绍了 MeetingBank—— 一个新的市政会议数据集，它采用了分治法来将会议摘要等任务分解为更小且可管理的任务。数据集包括会议视频链接、文本、摘要、议程等元数据，并公开发布以促进更好的 Meeting Summarization 技术的开发。

MeetingBank: 用于会议摘要的基准数据集

MeetingBank: A Benchmark Dataset for Meeting Summarization

We study a competitive online optimization problem with multiple inventories.
In the problem, an online decision maker seeks to optimize the allocation of
multiple capacity-limited inventories over a slotted horizon, while the
allocation constraints and revenue function come online at each slot. The
problem is challenging as we need to allocate limited inventories under
adversarial revenue functions and allocation constraints, while our decisions
are coupled among multiple inventories and different slots. We propose a
divide-and-conquer approach that allows us to decompose the problem into
several single inventory problems and solve it in a two-step manner with almost
no optimality loss in terms of competitive ratio (CR). Our approach provides
new angles, insights and results to the problem, which differs from the
widely-adopted primal-and-dual framework. Specifically, when the gradients of
the revenue functions are bounded in a positive range, we show that our
approach can achieve a tight CR that is optimal when the number of inventories
is small, which is better than all existing ones. For an arbitrary number of
inventories, the CR we achieve is within an additive constant of one to a lower
bound of the best possible CR among all online algorithms for the problem. We
further characterize a general condition for generalizing our approach to
different applications. For example, for a generalized one-way trading problem
with price elasticity, where no previous results are available, our approach
obtains an online algorithm that achieves the optimal CR up to a constant
factor.

本文研究了一种有多个容量有限库存的竞争性在线优化问题，提出了一种分治方法，能够有效应对在不同时间段内收益函数和分配约束的变化，并取得了优秀的最优解竞争比。