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20242026
most citedA Survey of Generative Search and Recommendation in the Era of Large Language Models

2 citations · 3 across the 18 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

DramaDirector: Geometry-Guided Short Drama Generation

Hengji Zhou, Sijie Liu, Jianrun Chen +3

Short dramas, with their rapid shot rhythms, dialogue-driven focus shifts, and demanding cinematographic grounding, pose challenges that prompt-level or text-only video generation…

cs.AI2026

Navigating User Behavior toward Personalized Multimodal Generation

Hengji Zhou, Yufeng Liu, Ye Liu +3

Modern AIGC pipelines deliver high-fidelity images and videos but presuppose a well-formed creation instruction, while end users rarely articulate visual details, leaving generator…

cs.AI2026

TailorMind: Towards Preference-Aligned Multimodal Content Generation

Hengji Zhou, Ye Liu, Yufeng Liu +3

Personalized content systems depend on available UGC and struggle when suitable content is absent, delayed, or costly to create. Although multimodal generators can synthesize conte…

cs.CV2026

TEMA: Anchor the Image, Follow the Text for Multi-Modification Composed Image Retrieval

Zixu Li, Yupeng Hu, Zhiheng Fu +3

Composed Image Retrieval (CIR) is an important image retrieval paradigm that enables users to retrieve a target image using a multimodal query that consists of a reference image an…

cs.CV2026

SJD-VP: Speculative Jacobi Decoding with Verification Prediction for Autoregressive Image Generation

Bingqi Shan, Baoquan Zhang, Xiaochen Qi +3

Speculative Jacobi Decoding (SJD) has emerged as a promising method for accelerating autoregressive image generation. Despite its potential, existing SJD approaches often suffer fr…

cs.LG2026

AR-Omni: A Unified Autoregressive Model for Any-to-Any Generation

Dongjie Cheng, Ruifeng Yuan, Yongqi Li +5

Real-world perception and interaction are inherently multimodal, encompassing not only language but also vision and speech, which motivates the development of "Omni" MLLMs that sup…