Temporal Action Detection (TAD) aims to identify specific actions in long, untrimmed videos by determining their start, end times and categories, yet existing models suffer from performance degradation under out-of-distribution scenarios due to unrealistic i.i.d. assumptions. While domain generalization (DG) offers a promising solution, image-based DG methods fail to address the unique spatiotemporal challenges in video-based TAD, including the spatiotemporal complexities and significant variations in action instance scales and densities across domains. To bridge this gap, we propose the first DG framework tailored for TAD. We propose Scene-Aware Video Segmentation, which segments videos based on semantic similarity, addressing cross-domain action instance density and scale discrepancies. Additionally, we present Temporal-Aware Normalization Perturbation to generate diverse video features while preserving temporal integrity. We establish the first DG-TAD benchmark, evaluating 11 state-of-the-art DG methods across four datasets. The experiments demonstrate that our framework consistently outperforms existing approaches, achieving superior generalization on unseen domains. The proposed modules are architecture-agnostic, offering plug-and-play compatibility for broader video understanding tasks.

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.Code is available at https://github.com/MM-Speech/emo-tts.

Junjie Wang, Zequn Xie, Dan Yang, Jie Feng, Yue Shen, Duolin Sun, Meixiu Long, Yihan Jiao, Zhehao Tan, Jian Wang, Peng Wei, Jinjie Gu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Text-based person anomaly search retrieves specific behavioral events from surveillance archives using natural-language queries.Although recent pose-aware methods align geometric structures well, they face a fundamental Pose-Semantic Gap: semantically different actions can share similar skeletal geometries.While Multimodal Large Language Models (MLLMs) can reduce this ambiguity, using them for large-scale retrieval is computationally prohibitive.We propose the Structure-Semantic Decoupled Cascade (SSDC) framework, which decouples retrieval into two stages:(1) Structure-Aware Coarse Retrieval, where a lightweight model quickly filters candidates by skeletal similarity; and (2) Detective Squad Interaction, a multi-agent semantic verification module.The squad consists of a Detective for fast binary filtering, an Analyst for evidence extraction, and a Writer for semantic synthesis.Finally, we re-rank candidates by fusing the synthesized captions with structural priors.Experiments on the PAB benchmark show that SSDC achieves state-of-theart performance by balancing efficiency and semantic reasoning.Our code is available

Text-based person search (TBPS) enables the retrieval of person images from large-scale databases using natural language descriptions, offering critical value in surveillance applications.However, a major challenge lies in the labor-intensive process of obtaining highquality textual annotations, which limits scalability and practical deployment.To address this, we introduce two complementary modules: Multi-Turn Text Generation (MTG) and Multi-Turn Text Interaction (MTI).MTG generates rich pseudo-labels through simulated dialogues with MLLMs, producing finegrained and diverse visual descriptions without manual supervision.MTI refines user queries at inference time through dynamic, dialogue-based reasoning, enabling the system to interpret and resolve vague, incomplete, or ambiguous descriptions-characteristics often seen in real-world search scenarios.Together, MTG and MTI form a unified and annotation-free framework that significantly improves retrieval accuracy, robustness, and usability.Extensive evaluations demonstrate that our method achieves competitive or superior results while eliminating the need for manual captions, paving the way for scalable and practical deployment of TBPS systems.