Recently, an audio-visual segmentation (AVS) task has been introduced, aiming
to group pixels with sounding objects within a given video. This task
necessitates a first-ever audio-driven pixel-level understanding of the scene,
posing significant challenges. In this paper, we propose an innovative
audio-visual transformer framework, termed COMBO, an acronym for COoperation of
Multi-order Bilateral relatiOns. For the first time, our framework explores
three types of bilateral entanglements within AVS: pixel entanglement, modality
entanglement, and temporal entanglement. Regarding pixel entanglement, we
employ a Siam-Encoder Module (SEM) that leverages prior knowledge to generate
more precise visual features from the foundational model. For modality
entanglement, we design a Bilateral-Fusion Module (BFM), enabling COMBO to
align corresponding visual and auditory signals bi-directionally. As for
temporal entanglement, we introduce an innovative adaptive inter-frame
consistency loss according to the inherent rules of temporal. Comprehensive
experiments and ablation studies on AVSBench-object (84.7 mIoU on S4, 59.2 mIou
on MS3) and AVSBench-semantic (42.1 mIoU on AVSS) datasets demonstrate that
COMBO surpasses previous state-of-the-art methods. Code and more results will
be publicly available at this https URL

提出了一种名为 COMBO 的创新的音频 - 视觉转换器框架，其中探索了音频 - 视觉分割中的像素纠缠、模态纠缠和时间纠缠等三种纠缠关系，通过各种实验和消融研究表明该方法优于以往的最先进方法。

合作有关系：探索多阶双边关系用于音频视觉分割

Cooperation Does Matter: Exploring Multi-Order Bilateral Relations for  Audio-Visual Segmentation

Open-domain question answering (QA) systems are often built with retrieval
modules. However, retrieving passages from a given source is known to suffer
from insufficient knowledge coverage. Alternatively, prompting large language
models (LLMs) to generate contextual passages based on their parametric
knowledge has been shown to improve QA performance. Yet, LLMs tend to
"hallucinate" content that conflicts with the retrieved knowledge. Based on the
intuition that answers supported by both sources are more likely to be correct,
we propose COMBO, a Compatibility-Oriented knowledge Merging for Better
Open-domain QA framework, to effectively leverage the two sources of
information. Concretely, we match LLM-generated passages with retrieved
counterparts into compatible pairs, based on discriminators trained with silver
compatibility labels. Then a Fusion-in-Decoder-based reader model handles
passage pairs to arrive at the final answer. Experiments show that COMBO
outperforms competitive baselines on three out of four tested open-domain QA
benchmarks. Further analysis reveals that our proposed framework demonstrates
greater efficacy in scenarios with a higher degree of knowledge conflicts.

基于知识兼容性匹配的 COMBO 框架能够有效地利用两种信息源，改善开放领域的问答性能，并在四项测试问答基准中有三个的竞争对手表现更好，特别在存在较高程度知识冲突的场景中表现出更大的功效。

合并生成和检索知识的开放领域问答

Merging Generated and Retrieved Knowledge for Open-Domain QA

Data-driven black-box model-based optimization (MBO) problems arise in a
great number of practical application scenarios, where the goal is to find a
design over the whole space maximizing a black-box target function based on a
static offline dataset. In this work, we consider a more general but
challenging MBO setting, named constrained MBO (CoMBO), where only part of the
design space can be optimized while the rest is constrained by the environment.
A new challenge arising from CoMBO is that most observed designs that satisfy
the constraints are mediocre in evaluation. Therefore, we focus on optimizing
these mediocre designs in the offline dataset while maintaining the given
constraints rather than further boosting the best observed design in the
traditional MBO setting. We propose retrieval-enhanced offline model-based
optimization (ROMO), a new derivable forward approach that retrieves the
offline dataset and aggregates relevant samples to provide a trusted
prediction, and use it for gradient-based optimization. ROMO is simple to
implement and outperforms state-of-the-art approaches in the CoMBO setting.
Empirically, we conduct experiments on a synthetic Hartmann (3D) function
dataset, an industrial CIO dataset, and a suite of modified tasks in the
Design-Bench benchmark. Results show that ROMO performs well in a wide range of
constrained optimization tasks.

在这项工作中，我们考虑了一种更加普遍但具有挑战性的约束模型优化问题（CoMBO），其中只有部分设计空间可以进行优化，而其余部分受到环境的约束。我们提出了一种新的推理增强的离线模型优化方法（ROMO），它通过检索离线数据集并聚合相关样本来提供可信的预测，然后在此基础上进行梯度优化。ROMO 在 CoMBO 设置中表现出色，优于最先进的方法。

ROMO: 检索增强的离线基于模型的优化

ROMO: Retrieval-enhanced Offline Model-based Optimization

We introduce COMBO - a fully neural NLP system for accurate part-of-speech
tagging, morphological analysis, lemmatisation, and (enhanced) dependency
parsing. It predicts categorical morphosyntactic features whilst also exposes
their vector representations, extracted from hidden layers. COMBO is an easy to
install Python package with automatically downloadable pre-trained models for
over 40 languages. It maintains a balance between efficiency and quality. As it
is an end-to-end system and its modules are jointly trained, its training is
competitively fast. As its models are optimised for accuracy, they achieve
often better prediction quality than SOTA. The COMBO library is available at:
this https URL

COMBO 是一个完全基于神经网络的 NLP 系统，支持准确的词性标注、形态分析、词形还原和 (增强) 依赖分析，提供超过 40 种语言的自动可下载的预训练模型，平衡了效率和质量。