The field of multimodal processing is moving towards more sophisticated methods for sentiment analysis and media forensics. Researchers are developing innovative approaches to improve the accuracy and robustness of multimodal models, including the use of counterfactual data augmentation and debiasing techniques to reduce spurious correlations and mitigate the influence of biased words. Additionally, there is a growing focus on detecting and grounding manipulated content in multimodal data, with a emphasis on semantic-coordinated manipulations that maintain consistency across modalities.

Noteworthy papers include: Target-oriented Multimodal Sentiment Classification with Counterfactual-enhanced Debiasing, which introduces a novel counterfactual-enhanced debiasing framework to reduce spurious correlations. Prompt Pirates Need a Map: Stealing Seeds helps Stealing Prompts, which reveals a noise-generation vulnerability in major image-generation frameworks and proposes a genetic algorithm-based optimization method for prompt stealing. Beyond Artificial Misalignment: Detecting and Grounding Semantic-Coordinated Multimodal Manipulations, which pioneers the detection of semantically-coordinated manipulations and proposes a Retrieval-Augmented Manipulation Detection and Grounding framework.