We introduce a novel cross-reference image quality assessment method that
effectively fills the gap in the image assessment landscape, complementing the
array of established evaluation schemes -- ranging from full-reference metrics
like SSIM, no-reference metrics such as NIQE, to general-reference metrics
including FID, and Multi-modal-reference metrics, e.g., CLIPScore. Utilising a
neural network with the cross-attention mechanism and a unique data collection
pipeline from NVS optimisation, our method enables accurate image quality
assessment without requiring ground truth references. By comparing a query
image against multiple views of the same scene, our method addresses the
limitations of existing metrics in novel view synthesis (NVS) and similar tasks
where direct reference images are unavailable. Experimental results show that
our method is closely correlated to the full-reference metric SSIM, while not
requiring ground truth references.

介绍一种新型的交叉参考图像质量评估方法，利用神经网络和独特的数据采集流水线以及 NVW 优化，实现了不需要真实参考图像的准确图像质量评估。

CrossScore: 走向多视角图像评估和打分

CrossScore: Towards Multi-View Image Evaluation and Scoring

Deception detection has attracted increasing attention due to its importance
in many practical scenarios. Currently, data scarcity harms the development of
this field. On the one hand, it is costly to hire participants to simulate
deception scenarios. On the other hand, it is difficult to collect videos
containing deceptive behaviors on the Internet. To address data scarcity, this
paper proposes a new data collection pipeline. Specifically, we use GPT-4 to
simulate a role-play between a suspect and a police officer. During
interrogation, the suspect lies to the police officer to evade responsibility
for the crime, while the police officer uncovers the truth and gathers
evidence. Compared with previous datasets, this strategy reduces data
collection costs, providing a promising way to increase the dataset size.
Meanwhile, we extend the traditional deception detection task to deception
reasoning, further providing evidence for deceptive parts. This dataset can
also be used to evaluate the complex reasoning capability of current large
language models and serve as a reasoning benchmark for further research.

为了解决数据匮乏的问题，本文提出一种新的数据收集方法，使用 GPT-4 模拟嫌疑人和警官之间的角色扮演，并将传统的欺骗检测任务扩展到欺骗推理，以提供更多的证据。这个数据集还可以评估当前大型语言模型的复杂推理能力，并作为进一步研究的推理基准。

欺骗检测是否能更深入？欺骗推理的数据集、评估和基准

Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark  for Deception Reasoning

In this paper, we present Generic Object Detection (GenOD), one of the
largest object detection systems deployed to a web-scale general visual search
engine that can detect over 900 categories for all Microsoft Bing Visual Search
queries in near real-time. It acts as a fundamental visual query understanding
service that provides object-centric information and shows gains in multiple
production scenarios, improving upon domain-specific models. We discuss the
challenges of collecting data, training, deploying and updating such a
large-scale object detection model with multiple dependencies. We discuss a
data collection pipeline that reduces per-bounding box labeling cost by 81.5%
and latency by 61.2% while improving on annotation quality. We show that GenOD
can improve weighted average precision by over 20% compared to multiple
domain-specific models. We also improve the model update agility by nearly 2
times with the proposed disjoint detector training compared to joint
fine-tuning. Finally we demonstrate how GenOD benefits visual search
applications by significantly improving object-level search relevance by 54.9%
and user engagement by 59.9%.

本文介绍 Generic Object Detection（GenOD）系统，是一个可以在微软 Bing Visual Search 的所有查询中实时检测超过 900 个类别的最大的物体检测系统之一，可提供以物体为中心的信息，并在多种生产场景中表现出色。作者讨论了收集数据、训练、部署和更新此类大规模物体检测模型所面临的挑战。我们展示了 GenOD 相对于多个领域特定模型可以提高 20% 以上的加权平均精度，并通过所提出的不相交探测器训练来将模型更新敏捷度提高近 2 倍。最后，我们展示了 GenOD 如何通过显著提高搜索相关性和用户参与度来改善视觉搜索应用。