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Tingrui Shen

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IJCAI Conference 2025 Conference Paper

DFMU: Distribution-based Framework for Modeling Aleatoric Uncertainty in Multimodal Sentiment Analysis

Chen Tang
Tingrui Shen
Xinrong Gong
Chong Zhao
Tong Zhang

In Multimodal Sentiment Analysis (MSA), data noise arising from various sources can lead to uncertainty in Aleatoric Uncertainty (AU), significantly impacting model performance. Current efforts to address AU have insufficiently explored its sources. They primarily focus on modeling noise rather than implementing targeted modeling based on its origin. Consequently, these approaches struggle to effectively mitigate the influence of AU, resulting in sustained limitations in model performance. Our research identifies that the AU primarily stems from two problems: subjective bias in the annotation process and the complex set relationships of sentiment features. To specifically address them, we propose DFMU, a Distribution-based Framework for Modeling Aleatoric Uncertainty, which incorporates an uncertainty modeling block capable of encoding uncertainty distributions and adaptively adjusting optimization objectives. Furthermore, we introduce distribution-based contrastive learning with sentiment words replacement to better capture the complex relationships among features. Extensive experiments on three public MSA datasets, i. e. , MOSI, MOSEI, and SIMS, demonstrate that the proposed model maintains robust performance even under high noise conditions and achieves state-of-the-art results on these popular datasets.

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TMLR Journal 2025 Journal Article

Teaching Diffusion Models to Ground Alpha Matte

Tianyi Xiang
Weiying Zheng
Yutao Jiang
Tingrui Shen
Hewei Yu
Yangyang Xu
Shengfeng He

The power of visual language models is showcased in visual understanding tasks, where language-guided models achieve impressive flexibility and precision. In this paper, we extend this capability to the challenging domain of image matting by framing it as a soft grounding problem, enabling a single diffusion model to handle diverse objects, textures, and transparencies, all directed by descriptive text prompts. Our method teaches the diffusion model to ground alpha mattes by guiding it through a process of instance-level localization and transparency estimation. First, we introduce an intermediate objective that trains the model to accurately localize semantic components of the matte based on natural language cues, establishing a robust spatial foundation. Building on this, the model progressively refines its transparency estimation abilities, using the learned semantic structure as a prior to enhance the precision of alpha matte predictions. By treating spatial localization and transparency estimation as distinct learning objectives, our approach allows the model to fully leverage the semantic depth of diffusion models, removing the need for rigid visual priors. Extensive experiments highlight our model’s adaptability, precision, and computational efficiency, setting a new benchmark for flexible, text-driven image matting solutions.

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