Multispectral pedestrian detection achieves better visibility in challenging
conditions and thus has a broad application in various tasks, for which both
the accuracy and computational cost are of paramount importance. Most existing
approaches treat RGB and infrared modalities equally, typically adopting two
symmetrical CNN backbones for multimodal feature extraction, which ignores the
substantial differences between modalities and brings great difficulty for the
reduction of the computational cost as well as effective crossmodal fusion. In
this work, we propose a novel and efficient framework named WCCNet that is able
to differentially extract rich features of different spectra with lower
computational complexity and semantically rearranges these features for
effective crossmodal fusion. Specifically, the discrete wavelet transform (DWT)
allowing fast inference and training speed is embedded to construct a
dual-stream backbone for efficient feature extraction. The DWT layers of WCCNet
extract frequency components for infrared modality, while the CNN layers
extract spatial-domain features for RGB modality. This methodology not only
significantly reduces the computational complexity, but also improves the
extraction of infrared features to facilitate the subsequent crossmodal fusion.
Based on the well extracted features, we elaborately design the crossmodal
rearranging fusion module (CMRF), which can mitigate spatial misalignment and
merge semantically complementary features of spatially-related local regions to
amplify the crossmodal complementary information. We conduct comprehensive
evaluations on KAIST and FLIR benchmarks, in which WCCNet outperforms
state-of-the-art methods with considerable computational efficiency and
competitive accuracy. We also perform the ablation study and analyze thoroughly
the impact of different components on the performance of WCCNet.

在具有挑战性条件下，多光谱行人检测在各种任务中实现更好的可视性，因此具有广泛的应用，其中准确性和计算成本非常重要。本文提出了一种名为 WCCNet 的新颖高效的框架，能够差异化地提取不同光谱的丰富特征，降低计算复杂性，并对这些特征进行语义重排，以实现有效的跨模态融合。

WCCNet: 带有波变换的 CNN 与跨模态重排融合用于快速的多光谱行人检测

WCCNet: Wavelet-integrated CNN with Crossmodal Rearranging Fusion for  Fast Multispectral Pedestrian Detection

Deception detection in conversations is a challenging yet important task,
having pivotal applications in many fields such as credibility assessment in
business, multimedia anti-frauds, and custom security. Despite this, deception
detection research is hindered by the lack of high-quality deception datasets,
as well as the difficulties of learning multimodal features effectively. To
address this issue, we introduce DOLOS, the largest gameshow deception
detection dataset with rich deceptive conversations. DOLOS includes 1,675 video
clips featuring 213 subjects, and it has been labeled with audio-visual feature
annotations. We provide train-test, duration, and gender protocols to
investigate the impact of different factors. We benchmark our dataset on
previously proposed deception detection approaches. To further improve the
performance by fine-tuning fewer parameters, we propose Parameter-Efficient
Crossmodal Learning (PECL), where a Uniform Temporal Adapter (UT-Adapter)
explores temporal attention in transformer-based architectures, and a
crossmodal fusion module, Plug-in Audio-Visual Fusion (PAVF), combines
crossmodal information from audio-visual features. Based on the rich
fine-grained audio-visual annotations on DOLOS, we also exploit multi-task
learning to enhance performance by concurrently predicting deception and
audio-visual features. Experimental results demonstrate the desired quality of
the DOLOS dataset and the effectiveness of the PECL. The DOLOS dataset and the
source codes will be publicly available soon.

本研究介绍了一个名为 DOLOS 的数据集并使用其进行欺骗检测研究，使用多任务学习和跨模态融合模块进行性能提升。