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

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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.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…