1 citations · 1 across the 8 of their papers we have counts for
8 papers
Same Semantics, Different Paths: Self-Improving Alignment for Vision-Text Compression
Tianyu Liang, Xiangxi Zheng, Yilin Wang +1
Vision-Text Compression (VTC) renders long texts into images and encodes them through the vision encoder (ViT), compressing thousands of text tokens into far fewer visual tokens. H…
Efficient Frame Selection for Long Videos at Test Time with Attention-Based MLLM Selectors
Yilin Wang, Xiangxi Zheng, Dongxing Mao +6
Understanding long videos with multimodal large language models (MLLMs) requires selecting a compact set of frames from thousands of candidates, yet identifying the right frames se…
DataEvolver: Self-Evolving Multi-Agent Data Construction for Text-Rich Image Generation
Siyu Yan, Yizhen Gao, Yilin Wang +2
Text-rich image generation is one of the most challenging settings in image generation, since models must simultaneously produce visually realistic images and render legible, seman…
TextGround4M: A Prompt-Aligned Dataset for Layout-Aware Text Rendering
Dongxing Mao, Yilin Wang, Linjie Li +2
Despite recent advances in text-to-image generation, models still struggle to accurately render prompt-specified text with correct spatial layout -- especially in multi-span, struc…
OmniControlNet: Dual-stage Integration for Conditional Image Generation
Yilin Wang, Haiyang Xu, Xiang Zhang +4
We provide a two-way integration for the widely adopted ControlNet by integrating external condition generation algorithms into a single dense prediction method and incorporating i…
Bayesian Diffusion Models for 3D Shape Reconstruction
Haiyang Xu, Yu Lei, Zeyuan Chen +4
We present Bayesian Diffusion Models (BDM), a prediction algorithm that performs effective Bayesian inference by tightly coupling the top-down (prior) information with the bottom-u…
TokenCompose: Text-to-Image Diffusion with Token-level Supervision
Zirui Wang, Zhizhou Sha, Zheng Ding +2
We present TokenCompose, a Latent Diffusion Model for text-to-image generation that achieves enhanced consistency between user-specified text prompts and model-generated images. De…
Dolfin: Diffusion Layout Transformers without Autoencoder
Yilin Wang, Zeyuan Chen, Liangjun Zhong +3
In this paper, we introduce a novel generative model, Diffusion Layout Transformers without Autoencoder (Dolfin), which significantly improves the modeling capability with reduced…