Multimodal Aspect-Based Sentiment Analysis (MABSA) involves extracting aspect terms from text-image pairs and identifying their sentiments. Most existing tasks consider one fixed sentiment category with explicitly mentioned aspects. However, these tasks seldom consider expressive sentiment categories, implicit aspects, and explainability. To this end, we introduce a novel task of Open-domain Explainable Multimodal Aspect-Based Sentiment Reasoning (OX-MABSR). This task enables the prediction of open-vocabulary aspect-sentiment pairs, together with the generation of sentiment explanations and reasoning paths. To benchmark OX-MABSR task, we construct OX-MABSR-Bench, a dataset annotated with explicit and implicit aspects, expressive sentiment categories, as well as perceptual and cognitive two-level explanations. The explanations capture visual and textual cues, including aesthetics, facial expressions, scenes, and textual semantics, together with background and situational knowledge. In addition, we annotate the reasoning paths that trace how the sentiment evolves from surface cues to a deeper contextual understanding. To address OX-MABSR task, we propose MABSR-LLM. Extensive experimental results show our MABSR-LLM outperforms strong baselines. To the best of our knowledge, we are the first to provide a unified framework for open-domain and explainable MABSR.

Multimodal Conversational Aspect-based Sentiment Analysis (MCA BSA) is a challenging task for multimodal dialogue understanding. Existing works often treat the entire dialogue as a flat sequence and feed it into Large Language Models (LLMs) for pipeline-style generation. However, these methods sometimes accumulate errors and overlook critical discourse structure and fine-grained inter-word relations that are essential for accurate sentiment reasoning. To address these limitations, we propose SDG-MLLM, a unified generative framework that integrates Structured Dialogue Graphs into Multimodal LLM (MLLM) for an end-to-end MCABSA. Specifically, we construct heterogeneous dialogue graphs that capture diverse structural relations, including syntactic dependencies, coreference links, speaker turns, reply flow, semantic role labeling, and sentiment propagation paths. These graphs are encoded using a heterogeneous dialogue graph encoder, and the resulting structure-aware graph features are injected into the embedding layer of LLM. Furthermore, SDG-MLLM incorporates aligned multimodal features such as image, audio, and video cues at the utterance level to enable unified and context-aware multimodal reasoning. Experiments on the MCABSA dataset show that SDG-MLLM significantly outperforms strong baselines across multiple tasks. In addition, our method also achieved top performance in the ACM MM 2025 Grand Challenge of MCABSA. Our code is available at https://github.com/Liuxj-Anya/SDG-MLLM.

In order to perform machine learning among multiple parties while protecting the privacy of raw data, privacy-preserving machine learning based on secure multi-party computation (MPL for short) has been a hot spot in recent. The configuration of MPL usually follows the peer-to-peer architecture, where each party has the same chance to reveal the output result. However, typical business scenarios often follow a hierarchical architecture where a powerful, usuallyprivileged party, leads the tasks of machine learning. Only theprivileged party can reveal the final model even if otherassistant parties collude with each other. It is even required to avoid the abort of machine learning to ensure the scheduled deadlines and/or save used computing resources when part ofassistant parties drop out.