Deception detection within audio-visual modalities is vital across diverse sectors, notably in customs security and multimedia anti-fraud. However, this notable efficacy is lost by the necessity to train and deploy separate models for each conceivable modality scenario, leading to redundancy and inefficiency. Moreover, real-world environments where multi-modal models are deployed often fail to meet these idealized conditions. To overcome these challenges and further elevate performance levels, we propose an advanced Transformer-based framework complemented by an Audio-Visual Adapter (AVA) integrating temporal features from both audio and visual modalities. In addition, we introduce an innovative multi-modal contrastive learning method that is designed to enhance the correlation between uni-modal features and their integrated counterparts within a consistent feature space. Our designed method can deal with the flexible-model scenario instead of deploying different models for various modalities. Empirical evaluations conducted on two benchmark datasets have validated the superiority of our proposed model over other multi-modal fusion techniques, particularly in scenarios characterized by varying and missing modalities. This strongly affirms the effectiveness of our approach in significantly boosting the accuracy of deception detection in complex, real-world multi-modal scenarios. The codes will be released soon.

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 DOLOS1, 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 audiovisual features. Experimental results demonstrate the desired quality of the DOLOS dataset and the effectiveness of the PECL. The DOLOS dataset and the source codes are available at here.

Traditional applications of robot manipulators are mainly limited to tracking control tasks, in which the desired objectives are specified as desired positions or trajectories. In such applications, a convenient and easy way of programming robots is the traditional teach-and-playback method. However, advances in sensing and robotic technologies have led to the requirements of more demanding tasks, in which robots may need to interact with a human or follow the human’s instructions in performing a sequence of more complex tasks. In such applications, it is not sufficient to just learn the positions or motion and play it back using a robot controller. In this article, a task learning approach is proposed for human–robot interaction systems, where a set of interaction behaviors is formulated and solved by specifying the task requirements in terms of potential energy. The motion behaviors demonstrated by humans can thus be acquired by the robot by seeking the appropriate task parameters of the dynamic potential energy function. To play back and combine the tasks in a sequential way by using a single controller, a new robot controller is also proposed. Lyapunov-like analysis is adopted to guarantee the stability of the control system, and experimental results are given to validate the performance of the proposed learning and control strategies.

A high confidence in object detection is very crucial for object detector modules to be used in real world applications. Though the confidence scores in object detection can be improved by using better and larger training data set and using more robust architectures, which is an approach from the computer vision side, this paper aims to improve the detection confidence by finding the effective viewpoints which makes a dependable use of the available object detectors. In particular, we investigate the effect of viewing distance on detection confidence of an object detector, which can further be used to control a camera mounted on a mobile robot to approach a better viewing position. We consider the cases with both fixed focal length and variable focal length camera. Experimental results are presented to demonstrate the efficacy and validity of the techniques presented in this work.