We study the multi-domain retrieval task, where user queries require retrieving relevant documents spanning multiple domains such as medicine, economics, and films. An underlying challenge is domain confusion, in which a query pertains to any of the K domains, complicating the routing to the appropriate domain expert and hindering the generation of precise domain-specific embeddings. One straightforward approach is to train a unified expert that embeds all categories into a shared space. However, this practice suffers from low retrieval precision, as embeddings from different domains may overlap, resulting in ambiguous retrieval due to insufficient domain-specific knowledge. To address this issue, we propose MiDream, MultI-Domain Retrieval EnhAnceMent, by leveraging the strengths of Mixture-of-Experts (MoE) techniques. MiDream mitigates the domain-confusion through a Domain Routing Network (DRN) that learns to route queries to a relevant domain-specific expert dynamically. This routing enables optimization of each domain-specific expert, establishing more discriminative embedding space. Each domain-specific expert is itself a MoE structure, consisting of a Sub-Domain Routing Network (SDRN) and two lightweight experts with Gated Linear layers, ensuring rapid adaptation across domains with efficient training and swift inference. We validate the effectiveness of MiDream on multi-domain datasets, demonstrating that our approach outperforms existing retrieval methods with the equivalent parameter counts, achieving an 8.7%~14.6% improvement in NDCG and a 10.4%~14.9% improvement in MRR.

Open-ended visual storytelling presents a formidable challenge for current text-to-image models, which frequently struggle to preserve both narrative coherence and consistent character depictions across generated sequences. To address this, we introduce StoryCrafter, a multi-character diffusion model that leverages a novel instance-level cross-attention module with supervised fine-tuning to ensure precise text-character alignment and consistent multi-character interactions throughout the narrative. Further, we propose Direct-Diffusion Group Relative Policy Optimization (D2GRPO), a novel RLHF stage that optimizes denoising strategies using automated story-aligned rewards, selecting the best candidate frames from a generated group. We evaluate our approach through human assessments and vision language model (VLM) scoring, measuring text-to-image alignment, style and character consistency, and fine-grained detail quality. Experiments on three benchmarks demonstrate that StoryCrafter outperforms existing methods, achieving 7% improvements in storytelling consistency and 10% in character accuracy, while outperforming baselines in both human and VLM evaluations.

Identifying squamous cell carcinoma and adenocarcinoma subtypes of metastatic cervical lymphadenopathy (CLA) is critical for localizing the primary lesion and initiating timely therapy. B-mode ultrasound (BUS), color Doppler flow imaging (CDFI), ultrasound elastography (UE) and dynamic contrast-enhanced ultrasound provide effective tools for identification but synthesis of modality information is a challenge for clinicians. Therefore, based on deep learning, rationally fusing these modalities with clinical information to personalize the classification of metastatic CLA requires new explorations. In this paper, we propose Multi-step Modality Fusion Network (MSMFN) for multi-modal ultrasound fusion to identify histological subtypes of metastatic CLA. MSMFN can mine the unique features of each modality and fuse them in a hierarchical three-step process. Specifically, first, under the guidance of high-level BUS semantic feature maps, information in CDFI and UE is extracted by modality interaction, and the static imaging feature vector is obtained. Then, a self-supervised feature orthogonalization loss is introduced to help learn modality heterogeneity features while maintaining maximal task-consistent category distinguishability of modalities. Finally, six encoded clinical information are utilized to avoid prediction bias and improve prediction ability further. Our three-fold cross-validation experiments demonstrate that our method surpasses clinicians and other multi-modal fusion methods with an accuracy of 80.06%, a true-positive rate of 81.81%, and a true-negative rate of 80.00%. Our network provides a multi-modal ultrasound fusion framework that considers prior clinical knowledge and modality-specific characteristics. Our code will be available at: https://github.com/RichardSunnyMeng/MSMFN.