most citedTraining-free Subject-Enhanced Attention Guidance for Compositional Text-to-image Generation

4 citations · 5 across the 7 of their papers we have counts for

collaborators

7 papers

cs.IR2026

Recommendation as Generation: Unifying Personalized Video Generation and Recommendation at Industrial Scale

Yanhua Cheng, Bo Wang, Haotian Zhang +17

Traditional short-video recommendation systems match user interest to a fixed pool of pre-produced videos, which limits their ability to capture fine-grained and dynamic preference…

cs.CV2026

ProductWebGen: Benchmarking Multimodal Product Webpage Generation

Zhihong Liu, Siqi Kou, Zheng Li +5

Crafting a product display webpage from a source product image, along with layout and visual content instructions, holds significant practical value for domains such as marketing,…

cs.CV2026

Think-Then-Generate: Reasoning-Aware Text-to-Image Diffusion with LLM Encoders

Siqi Kou, Jiachun Jin, Zetong Zhou +8

Recent progress in text-to-image (T2I) diffusion models (DMs) has enabled high-quality visual synthesis from diverse textual prompts. Yet, most existing T2I DMs, even those equippe…

cs.CV2024★ 1 cited

Text-Video Multi-Grained Integration for Video Moment Montage

Zhihui Yin, Ye Ma, Xipeng Cao +3

The proliferation of online short video platforms has driven a surge in user demand for short video editing. However, manually selecting, cropping, and assembling raw footage into…

cs.CV2024

Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads

Siqi Kou, Jiachun Jin, Zhihong Liu +6

We introduce Orthus, an autoregressive (AR) transformer that excels in generating images given textual prompts, answering questions based on visual inputs, and even crafting length…

cs.MM2024

Music Grounding by Short Video

Zijie Xin, Minquan Wang, Jingyu Liu +4

Adding proper background music helps complete a short video to be shared. Previous work tackles the task by video-to-music retrieval (V2MR), aiming to find the most suitable music…

cs.CV2024★ 4 cited

Training-free Subject-Enhanced Attention Guidance for Compositional Text-to-image Generation

Shengyuan Liu, Bo Wang, Ye Ma +6

Existing subject-driven text-to-image generation models suffer from tedious fine-tuning steps and struggle to maintain both text-image alignment and subject fidelity. For generatin…