Pedestrian anomaly behavior retrieval requires disambiguating subtle anomalous actions from routine ones using natural language. Existing cross-modal alignment methods often struggle with confounding biases: visual entanglement (dominant environments overshadowing target actions) and textual biases (limited stylistic diversity). These spurious correlations lead to erroneous matches among visually similar but semantically distinct samples. To address this, we reformulate the task from a causal intervention perspective. We propose a dual generative framework leveraging Large Multimodal Models (LMMs) to break environment-action and style-text confounders. Specifically, we synthesize causally meaningful hard negatives to decouple actions from environments, and augment positive queries with stylistically diverse rephrasings to eliminate narrative dependency. Extensive experiments on the Pedestrian Anomaly Behavior (PAB) benchmark yield consistent improvements of +1.71% in Recall@1 and +1.01% in mAP. Furthermore, zero-shot evaluation on the out-of-distribution UCC dataset demonstrates markedly enhanced generalization, achieving +3.23% and +2.46% gains in Recall@1 and mAP, respectively.

Large language models (LLMs) achieve strong fluency in open-domain question answering, yet often hallucinate in specialized medical QA and lack reliable uncertainty quantification, posing risks in safety-critical applications. We propose Uncertainty-guided Retrieval-augmented Generation (URGen), a training-free framework for guideline-grounded medical multiple-choice QA, where authoritative clinical documents (e.g., consensus statements, treatment protocols) are available and answer correctness depends on precise evidence retrieval. URGen is a four-stage pipeline that combines adaptive retrieval depth with uncertainty calibration: (1) multi-temperature knowledge retrieval to rank candidate keypoints, (2) zero-shot adaptive retrieval depth (ZARD) that expands context when rankings are ambiguous, and contracts it when rankings are consistent, (3) multi-temperature answer generation with aggregation over candidates, and (4) entropy-based uncertainty estimation over the answer distribution to enable abstention on high-uncertainty queries. Extensive experiments on two expert-annotated ophthalmology benchmarks, Thyroid Eye Disease and Graves’ Disease, show that URGen improves accuracy by 9.29% and 6.19% over a naive RAG-based Meditron-7B baseline, while achieving 3.01% and 4.57% expected calibration error (ECE). URGen is LLM-agnostic, consistently improving diverse backbones (, LLaMA-3.1 and Mistral) and providing interpretable uncertainty indicators for safety-critical deployment.

Large Multimodal Models (LMMs), characterized by their humanoid perception and generation capabilities, have been widely applied in critical physical world scenarios, yet their uncertainty remains mostly unknown. In this paper, we aim to answer three substantial questions: (1) Can we explicitly capture the uncertainty of LMMs? (2) How can we tackle the reasoning complexity arising from prompts across various modalities? (3) How can we quantify the uncertainty of LMMs when their responses extend to multiple modalities, beyond mere text? To answer these questions, we propose Uncertainty-o: (1) Uncertainty-o can explicitly estimate uncertainty of LMMs, with prompts and responses across five modalities, namely text, image, audio, video, and point cloud. (2) We propose multimodal prompt perturbation, where empirical studies indicate that semantically equivalent perturbations are more effective for eliciting LMM uncertainty. (3) We introduce multimodal semantic uncertainty, enabling uncertainty estimation when LMMs generate multimodal responses. Extensive quantitative and qualitative analyses validate the efficacy of Uncertainty-o in reliably estimating the uncertainty inherent in LMMs.

Existing facial forgery detection methods typically focus on binary classification or pixel-level localization, providing little semantic insight into the nature of the manipulation. To address this, we introduce Forgery Attribution Report Generation, a new multimodal task designed to provide post-hoc forensic evidence for manipulated images. This task jointly localizes forged regions (“Where“) and generates natural language explanations grounded in the editing process (“Why“). This dual-focus approach goes beyond traditional binary forensics, providing a comprehensive, interpretable understanding of the manipulation. To enable research in this domain, we present Multi-Modal Tamper Tracing (MMTT), a large-scale dataset of 152,217 samples. Each sample features a process-derived ground-truth mask and a human-authored textual description, ensuring high annotation precision and linguistic richness. We further propose ForgeryTalker, a unified end-to-end baseline that integrates vision and language via a shared encoder and dual decoders for mask and text generation. Experiments show that ForgeryTalker achieves competitive performance on both subtasks, i.e., 59.3 CIDEr and 73.67 IoU, establishing a strong baseline for explainable multimedia forensics. Our dataset and code are available at: https://github.com/NattyLianJc/Generating-Attribution-Reports.

Recent advances in generative AI have significantly enhanced the realism of multimodal media manipulation, thereby posing substantial challenges to manipulation detection. Existing manipulation detection and grounding approaches predominantly focus on manipulation type classification under result-oriented supervision, which not only lacks interpretability but also tends to overfit superficial artifacts. In this paper, we argue that generalizable detection requires incorporating explicit forensic reasoning, rather than merely classifying a limited set of manipulation types, which fails to generalize to unseen manipulation patterns. To this end, we propose REFORM, a reasoning-driven framework that shifts learning from outcome fitting to process modeling. REFORM adopts a three-stage curriculum that first induces forensic rationales, then aligns reasoning with final judgments, and finally refines logical consistency via reinforcement learning. To support this paradigm, we introduce ROM, a large-scale dataset with rich reasoning annotations. Extensive experiments show that REFORM establishes new state-of-the-art performance with superior generalization, achieving 81.52% ACC on ROM, 76.65% ACC on DGM4, and 74.9 F1 on MMFakeBench.

Enabling digital humans to express rich emotions has significant applications in dialogue systems, gaming, and other interactive scenarios. While recent advances in talking head synthesis have achieved impressive results in lip synchronization, they tend to overlook the rich and dynamic nature of facial expressions. To fill this critical gap, we introduce an end-to-end text-to-expression model that explicitly focuses on emotional dynamics. Our model learns expressive facial variations in a continuous latent space and generates expressions that are diverse, fluid, and emotionally coherent. To support this task, we introduce EmoAva, a large-scale and high-quality dataset containing 15,000 text–3D expression pairs. Extensive experiments on both existing datasets and EmoAva demonstrate that our method significantly outperforms baselines across multiple evaluation metrics, marking a significant advancement in the field.