Multimodal Sentiment Analysis (MSA) models typically suffer significant performance degradation under domain shifts. While Test-Time Adaptation (TTA) aims to mitigate this, existing discriminative approaches often succumb to “confident but wrong” predictions on out-of-distribution samples. Conversely, generative models offer robust calibration but incur prohibitive computational costs. To bridge this gap, we propose GD-Adapt (Generative-Discriminative Adaptation), a novel TTA framework that harmonizes the robustness of generative diffusion models with the efficiency of discriminative regression networks via Bayesian Diffusion Distillation (BDD). Specifically, we introduce Auxiliary Generative Regularization (AGR) during pretraining to enforce manifold-aware feature learning. Extensive experiments across five cross-domain scenarios demonstrate our method’s superiority. For instance, on the challenging MOSI to SIMS shift, GD-Adapt reduces Mean Absolute Error (MAE) from 0.6872 to 0.5673 and boosts binary accuracy by 5.81 percentage points (reaching 57.33%). Notably, in scenarios such as SIMS to MOSI, we achieve an 11.18-point gain over the non-adapted baseline.

While diffusion and flow-matching models have advanced TTS, generating high-arousal emotions remains a persistent challenge due to the trade-off between stability and expressiveness. Existing systems often suffer from linguistic collapse when pursuing high intensity or fail to meet target emotional levels under stable settings. In this work, we identify that standard Gaussian initialization inevitably introduces a neutral prosody bias, while uniform Classifier-Free Guidance often distorts the acoustic manifold, leading to artifacts. To address this, we propose an inference framework that rectifies the emotional trajectory. An Emotion-Rectified Noise Prior injects a semantic gradient at initialization to align sampling with the target emotional manifold, and Likelihood-Inverse Guidance adaptively schedules guidance via a conditional/unconditional likelihood ratio, strengthening guidance only when the trajectory drifts toward a neutral fallback. Extensive experiments demonstrate that our method effectively resolves the stability bottleneck in high-intensity scenarios, achieving superior linguistic accuracy and emotional fidelity without model retraining. Audio samples are available at https://showtts.github.io/emotionTTS/.

In recent years, CLIP-based text-video retrieval methods have developed rapidly, with research focusing on constructing diverse features and achieving effective interactions. However, the asymmetry of cross-modal information poses a challenge to accurately establishing retrieval relationships. To overcome this challenge, we propose a novel video retrieval framework, termed the Dual-Pathway and Dual-View model (DPDV), which consists of the Dual-Pathway Partitioning Module (DPPM) for constructing features at an appropriate granularity and the Dual-View Interaction Module (DVIM) for performing effective feature interactions. For DPPM, we simulate a human macro-level cognitive perspective by partitioning visual features into two categories based on their relevance to the text query and supplementing less relevant features with additional textual information. For DVIM, we simulate a human alignment strategy from macro to micro levels, focusing on local visual features while comprehensively modeling fine-grained interactions. We evaluate DPDV on five benchmark datasets, achieving leading retrieval performance.