An official website of the United States government
Human state recognition is a critical topic with pervasive and important applications in human–machine systems. Multimodal fusion, which entails integrating metrics from various data sources, has proven to be a potent method for boosting recognition performance. Although recent multimodal-based models have shown promising results, they often fall short in fully leveraging sophisticated fusion strategies essential for modeling adequate cross-modal dependencies in the fusion representation. Instead, they rely on costly and inconsistent feature crafting and alignment. To address this limitation, we propose an end-to-end multimodal transformer framework for multimodal human state recognition called Husformer. Specifically, we propose using cross-modal transformers, which inspire one modality to reinforce itself through directly attending to latent relevance revealed in other modalities, to fuse different modalities while ensuring sufficient awareness of the cross-modal interactions introduced. Subsequently, we utilize a self-attention transformer to further prioritize contextual information in the fusion representation. Extensive experiments on two human emotion corpora (DEAP and WESAD) and two cognitive load datasets [multimodal dataset for objective cognitive workload assessment on simultaneous tasks (MOCAS) and CogLoad] demonstrate that in the recognition of the human state, our Husformer outperforms both state-of-the-art multimodal baselines and the use of a single modality by a large margin, especially when dealing with raw multimodal features. We also conducted an ablation study to show the benefits of each component in Husformer. Experimental details and source code are available at https://github.com/SMARTlab-Purdue/Husformer.
more »
« less
A Fairness-Aware Fusion Framework for Multimodal Cyberbullying Detection
Recent reports of bias in multimedia algorithms (e.g., lesser accuracy of face detection for women and persons of color) have underscored the urgent need to devise approaches which work equally well for different demographic groups. Hence, we posit that ensuring fairness in multimodal cyber-bullying detectors (e.g., equal performance irrespective of the gender of the victim) is an important research challenge. We propose a fairness-aware fusion framework that ensures that both fairness and accuracy remain important considerations when combining data coming from multiple modalities. In this Bayesian fusion framework, the inputs coming from different modalities are combined in a way that is cognizant of the different confidence levels associated with each feature and the interdependencies between features. Specifically, this framework assigns weights to different modalities not just based on accuracy but also their fairness. Results of applying the framework on a multimodal (visual + text) cyberbullying detection problem demonstrate the value of the proposed framework in ensuring both accuracy and fairness.
more »
« less
- Award ID(s):
- 1915790
- PAR ID:
- 10231393
- Date Published:
- Journal Name:
- 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM)
- Page Range / eLocation ID:
- 166 to 173
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Multimodal fusion networks play a pivotal role in leveraging diverse sources of information for enhanced machine learning applications in aerial imagery. However, current approaches often suffer from a bias towards certain modalities, diminishing the potential benefits of multimodal data. This paper addresses this issue by proposing a novel modality utilization-based training method for multimodal fusion networks. The method aims to guide the network’s utilization on its input modalities, ensuring a balanced integration of complementary information streams, effectively mitigating the overutilization of dominant modalities. The method is validated on multimodal aerial imagery classification and image segmentation tasks, effectively maintaining modality utilization within ±10% of the user-defined target utilization and demonstrating the versatility and efficacy of the proposed method across various applications. Furthermore, the study explores the robustness of the fusion networks against noise in input modalities, a crucial aspect in real-world scenarios. The method showcases better noise robustness by maintaining performance amidst environmental changes affecting different aerial imagery sensing modalities. The network trained with 75.0% EO utilization achieves significantly better accuracy (81.4%) in noisy conditions (noise variance = 0.12) compared to traditional training methods with 99.59% EO utilization (73.7%). Additionally, it maintains an average accuracy of 85.0% across different noise levels, outperforming the traditional method’s average accuracy of 81.9%. Overall, the proposed approach presents a significant step towards harnessing the full potential of multimodal data fusion in diverse machine learning applications such as robotics, healthcare, satellite imagery, and defense applications.more » « less
-
null (Ed.)Sentiment detection is an important building block for multiple information retrieval tasks such as product recommendation, cyberbullying, fake news and misinformation detection. Unsurprisingly, multiple commercial APIs, each with different levels of accuracy and fairness, are now publicly available for sentiment detection. Users can easily incorporate these APIs in their applications. While combining inputs from multiple modalities or black-box models for increasing accuracy is commonly studied in multimedia computing literature, there has been little work on combining different modalities for increasing fairness of the resulting decision. In this work, we audit multiple commercial sentiment detection APIs for the gender bias in two-actor news headlines settings and report on the level of bias observed. Next, we propose a "Flexible Fair Regression" approach, which ensures satisfactory accuracy and fairness by jointly learning from multiple black-box models. The results pave way for fair yet accurate sentiment detectors for multiple applications.more » « less
-
By employing generative deep learning techniques, Deepfakes are created with the intent to create mistrust in society, manipulate public opinion and political decisions, and for other malicious purposes such as blackmail, scamming, and even cyberstalking. As realistic deepfake may involve manipulation of either audio or video or both, thus it is important to explore the possibility of detecting deepfakes through the inadequacy of generative algorithms to synchronize audio and visual modalities. Prevailing performant methods, either detect audio or video cues for deepfakes detection while few ensemble the results after predictions on both modalities without inspecting relationship between audio and video cues. Deepfake detection using joint audiovisual representation learning is not explored much. Therefore, this paper proposes a unified multimodal framework, Multimodaltrace, which extracts learned channels from audio and visual modalities, mixes them independently in IntrAmodality Mixer Layer (IAML), processes them jointly in IntErModality Mixer Layers (IEML) from where it is fed to multilabel classification head. Empirical results show the effectiveness of the proposed framework giving state-of-the-art accuracy of 92.9% on the FakeAVCeleb dataset. The cross-dataset evaluation of the proposed framework on World Leaders and Presidential Deepfake Detection Datasets gives an accuracy of 83.61% and 70% respectively. The study also provides insights into how the model focuses on different parts of audio and visual features through integrated gradient analysismore » « less
-
Identifying persuasive speakers in an adversarial environment is a critical task. In a national election, politicians would like to have persuasive speakers campaign on their behalf. When a company faces adverse publicity, they would like to engage persuasive advocates for their position in the presence of adversaries who are critical of them. Debates represent a common platform for these forms of adversarial persuasion. This paper solves two problems: the Debate Outcome Prediction (DOP) problem predicts who wins a debate while the Intensity of Persuasion Prediction (IPP) problem predicts the change in the number of votes before and after a speaker speaks. Though DOP has been previously studied, we are the first to study IPP. Past studies on DOP fail to leverage two important aspects of multimodal data: 1) multiple modalities are often semantically aligned, and 2) different modalities may provide diverse information for prediction. Our M2P2 (Multimodal Persuasion Prediction) framework is the first to use multimodal (acoustic, visual, language) data to solve the IPP problem. To leverage the alignment of different modalities while maintaining the diversity of the cues they provide, M2P2 devises a novel adaptive fusion learning framework which fuses embeddings obtained from two modules -- an alignment module that extracts shared information between modalities and a heterogeneity module that learns the weights of different modalities with guidance from three separately trained unimodal reference models. We test M2P2 on the popular IQ2US dataset designed for DOP. We also introduce a new dataset called QPS (from Qipashuo, a popular Chinese debate TV show) for IPP - we plan to release this dataset when the paper is published. M2P2 significantly outperforms 3 recent baselines on both datasets.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigMM50055.2020.00032
