Can Out-of-Domain data help to Learn Domain-Specific Prompts for Multimodal Misinformation Detection?
This addresses the challenge of detecting fake news in various domains without training separate models, which is incremental as it builds on existing vision-language models.
The paper tackles the problem of multimodal misinformation detection across different domains by proposing DPOD, a framework that uses out-of-domain data to improve detection for all desired domains simultaneously, achieving state-of-the-art performance on NewsCLIPpings and VERITE benchmarks.
Spread of fake news using out-of-context images and captions has become widespread in this era of information overload. Since fake news can belong to different domains like politics, sports, etc. with their unique characteristics, inference on a test image-caption pair is contingent on how well the model has been trained on similar data. Since training individual models for each domain is not practical, we propose a novel framework termed DPOD (Domain-specific Prompt tuning using Out-of-domain data), which can exploit out-of-domain data during training to improve fake news detection of all desired domains simultaneously. First, to compute generalizable features, we modify the Vision-Language Model, CLIP to extract features that helps to align the representations of the images and corresponding captions of both the in-domain and out-of-domain data in a label-aware manner. Further, we propose a domain-specific prompt learning technique which leverages training samples of all the available domains based on the extent they can be useful to the desired domain. Extensive experiments on the large-scale NewsCLIPpings and VERITE benchmarks demonstrate that DPOD achieves state of-the-art performance for this challenging task. Code: https://github.com/scviab/DPOD.