Despite recent advances in multimodal pre-training for visual description,
state-of-the-art models still produce captions containing errors, such as
hallucinating objects not present in a scene. The existing prominent metric for
object hallucination, CHAIR, is limited to a fixed set of MS COCO objects and
synonyms. In this work, we propose a modernized open-vocabulary metric, ALOHa,
which leverages large language models (LLMs) to measure object hallucinations.
Specifically, we use an LLM to extract groundable objects from a candidate
caption, measure their semantic similarity to reference objects from captions
and object detections, and use Hungarian matching to produce a final
hallucination score. We show that ALOHa correctly identifies 13.6% more
hallucinated objects than CHAIR on HAT, a new gold-standard subset of MS COCO
Captions annotated for hallucinations, and 30.8% more on nocaps, where objects
extend beyond MS COCO categories. Our code is available at
this https URL

尽管现在的先进多模态预训练模型能产生图像的描述，但仍存在错误的描述，如对场景中不存在的物体的错误描绘。本文提出了一种现代化的开放词汇度量标准 ALOHa，它利用大型语言模型 (LLMs) 来衡量物体的虚构情况。我们通过使用 LLM 从候选描述中提取可基准化的物体，测量它们与参考描述和物体检测中的参考物体的语义相似度，并使用匈牙利匹配算法生成最终的虚构评分。我们展示了在 HAT 上，ALOHa 对相对于 CHAIR 这个以 MS COCO 物体和同义词为基准的先进描绘虚构评估指标能正确识别出 13.6% 更多的虚构物体，以及在 nocaps 上能识别出相对于 MS COCO 类别范围更广的物体的增长率为 30.8%。我们的代码可在此网址上获得。

ALOHa: 一种新的用于字幕模型中幻觉的评测指标

ALOHa: A New Measure for Hallucination in Captioning Models

This paper investigates the use of deep reinforcement learning (DRL) in a MAC
protocol for heterogeneous wireless networking referred to as
Deep-reinforcement Learning Multiple Access (DLMA). The thrust of this work is
partially inspired by the vision of DARPA SC2, a 3-year competition whereby
competitors are to come up with a clean-slate design that "best share spectrum
with any network(s), in any environment, without prior knowledge, leveraging on
machine-learning technique". Specifically, this paper considers the problem of
sharing time slots among a multiple of time-slotted networks that adopt
different MAC protocols. One of the MAC protocols is DLMA. The other two are
TDMA and ALOHA. The nodes operating DLMA do not know that the other two MAC
protocols are TDMA and ALOHA. Yet, by a series of observations of the
environment, its own actions, and the resulting rewards, a DLMA node can learn
an optimal MAC strategy to coexist harmoniously with the TDMA and ALOHA nodes
according to a specified objective (e.g., the objective could be the sum
throughput of all networks, or a general alpha-fairness objective).

本文研究了深度强化学习在异构无线网络的 MAC 协议中的应用，通过观察环境、采取行动和获得回报，DLMA 节点可以学习一种最优的 MAC 策略，以实现多个时隙网络的协调共存。