Text-guided image and 3D editing have advanced with diffusion-based models, yet methods like Delta Denoising Score often struggle with stability, spatial control, and editing strength. These limitations stem from reliance on complex auxiliary structures, which introduce conflicting optimization signals and restrict precise, localized edits. We introduce Stable Score Distillation (SSD), a streamlined framework that enhances stability and alignment in the editing process by anchoring a single classifier to the source prompt. Specifically, SSD utilizes Classifier-Free Guidance (CFG) equation to achieves cross-prompt alignment, and introduces a constant term null-text branch to stabilize the optimization process. This approach preserves the original content's structure and ensures that editing trajectories are closely aligned with the source prompt, enabling smooth, prompt-specific modifications while maintaining coherence in surrounding regions. Additionally, SSD incorporates a prompt enhancement branch to boost editing strength, particularly for style transformations. Our method achieves state-of-the-art results in 2D and 3D editing tasks, including NeRF and text-driven style edits, with faster convergence and reduced complexity, providing a robust and efficient solution for text-guided editing.

In multimodal sentiment analysis (MSA), recent works introduced uncertainty quantification to evaluate model prediction reliability, aiming to achieve well-performing and trustworthy models. However, a trustworthy model must not only quantify predictive uncertainty but also ensure that the quantified uncertainty is accurate, which recent studies have neglected. Therefore, we propose an epistemic uncertainty (EU) verification task, which aims to estimate and compare the accuracy of the uncertainty quantification of different models by constructing a test dataset with samples of hierarchical prediction difficulty. Inspired by this task, we propose a Reliable Uncertainty Trustworthy Model (RUTM), which is validated against the EU verification task to assess its accuracy in predicting uncertainty. RUTM improves generalizable sentiment modeling through Information Entropy Disentanglement method and achieves more accurate uncertainty predictions via the EU Modeling Module. Extensive experiments on three public MSA datasets confirm that our model achieves state-of-the-art results, surpassing all baselines in uncertainty prediction accuracy.

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.