Automated deception detection is crucial for assisting humans in accurately
assessing truthfulness and identifying deceptive behavior. Conventional
contact-based techniques, like polygraph devices, rely on physiological signals
to determine the authenticity of an individual's statements. Nevertheless,
recent developments in automated deception detection have demonstrated that
multimodal features derived from both audio and video modalities may outperform
human observers on publicly available datasets. Despite these positive
findings, the generalizability of existing audio-visual deception detection
approaches across different scenarios remains largely unexplored. To close this
gap, we present the first cross-domain audio-visual deception detection
benchmark, that enables us to assess how well these methods generalize for use
in real-world scenarios. We used widely adopted audio and visual features and
different architectures for benchmarking, comparing single-to-single and
multi-to-single domain generalization performance. To further exploit the
impacts using data from multiple source domains for training, we investigate
three types of domain sampling strategies, including domain-simultaneous,
domain-alternating, and domain-by-domain for multi-to-single domain
generalization evaluation. Furthermore, we proposed the Attention-Mixer fusion
method to improve performance, and we believe that this new cross-domain
benchmark will facilitate future research in audio-visual deception detection.
Protocols and source code are available at
\href{https://github.com/Redaimao/cross_domain_DD}{this https URL}.

通过使用跨领域基准测试，提出了一种多模态特征的注意力混合方法，用于改进自动欺骗检测的性能。

跨领域音视频欺诈检测的基准测试

Benchmarking Cross-Domain Audio-Visual Deception Detection

Relation Extraction (RE) has attracted increasing attention, but current RE
evaluation is limited to in-domain evaluation setups. Little is known on how
well a RE system fares in challenging, but realistic out-of-distribution
evaluation setups. To address this gap, we propose CrossRE, a new,
freely-available cross-domain benchmark for RE, which comprises six distinct
text domains and includes multi-label annotations. An additional innovation is
that we release meta-data collected during annotation, to include explanations
and flags of difficult instances. We provide an empirical evaluation with a
state-of-the-art model for relation classification. As the meta-data enables us
to shed new light on the state-of-the-art model, we provide a comprehensive
analysis on the impact of difficult cases and find correlations between model
and human annotations. Overall, our empirical investigation highlights the
difficulty of cross-domain RE. We release our dataset, to spur more research in
this direction.

本文提出了一种新的跨领域关系抽取基准，并提供了元数据和多标签注释来帮助理解困难实例。作者通过在六个不同的文本领域上进行评估，评估了最先进的关系分类模型，发现跨领域关系抽取的困难性，为该方向的研究提供了新的数据集和启示。