13 citations · 85 across the 35 of their papers we have counts for
14 papers · 1 filter
Sim4Seg: Boosting Multimodal Multi-disease Medical Diagnosis Segmentation with Region-Aware Vision-Language Similarity Masks
Lingran Song, Yucheng Zhou, Jianbing Shen
Despite significant progress in pixel-level medical image analysis, existing medical image segmentation models rarely explore medical segmentation and diagnosis tasks jointly. Howe…
ComplexBench-Edit: Benchmarking Complex Instruction-Driven Image Editing via Compositional Dependencies
Chenglin Wang, Yucheng Zhou, Qianning Wang +2
Text-driven image editing has achieved remarkable success in following single instructions. However, real-world scenarios often involve complex, multi-step instructions, particular…
Draw ALL Your Imagine: A Holistic Benchmark and Agent Framework for Complex Instruction-based Image Generation
Yucheng Zhou, Jiahao Yuan, Qianning Wang
Recent advancements in text-to-image (T2I) generation have enabled models to produce high-quality images from textual descriptions. However, these models often struggle with comple…
Self-Rewarding Large Vision-Language Models for Optimizing Prompts in Text-to-Image Generation
Hongji Yang, Yucheng Zhou, Wencheng Han +1
Text-to-image models are powerful for producing high-quality images based on given text prompts, but crafting these prompts often requires specialized vocabulary. To address this,…
DC-ControlNet: Decoupling Inter- and Intra-Element Conditions in Image Generation with Diffusion Models
Hongji Yang, Wencheng Han, Yucheng Zhou +1
In this paper, we introduce DC (Decouple)-ControlNet, a highly flexible and precisely controllable framework for multi-condition image generation. The core idea behind DC-ControlNe…
Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral Constraints
Guanjie Chen, Xinyu Zhao, Yucheng Zhou +3
Diffusion Transformers (DiT) have emerged as a powerful architecture for image and video generation, offering superior quality and scalability. However, their practical application…
SURf: Teaching Large Vision-Language Models to Selectively Utilize Retrieved Information
Jiashuo Sun, Jihai Zhang, Yucheng Zhou +3
Large Vision-Language Models (LVLMs) have become pivotal at the intersection of computer vision and natural language processing. However, the full potential of LVLMs Retrieval-Augm…
MemoryMamba: Memory-Augmented State Space Model for Defect Recognition
Qianning Wang, He Hu, Yucheng Zhou
As automation advances in manufacturing, the demand for precise and sophisticated defect detection technologies grows. Existing vision models for defect recognition methods are ins…
InsectMamba: Insect Pest Classification with State Space Model
Qianning Wang, Chenglin Wang, Zhixin Lai +1
The classification of insect pests is a critical task in agricultural technology, vital for ensuring food security and environmental sustainability. However, the complexity of pest…
Residual-based Language Models are Free Boosters for Biomedical Imaging
Zhixin Lai, Jing Wu, Suiyao Chen +2
In this study, we uncover the unexpected efficacy of residual-based large language models (LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of…
Visual In-Context Learning for Large Vision-Language Models
Yucheng Zhou, Xiang Li, Qianning Wang +1
In Large Visual Language Models (LVLMs), the efficacy of In-Context Learning (ICL) remains limited by challenges in cross-modal interactions and representation disparities. To over…
Style-Aware Contrastive Learning for Multi-Style Image Captioning
Yucheng Zhou, Guodong Long
Existing multi-style image captioning methods show promising results in generating a caption with accurate visual content and desired linguistic style. However, existing methods ov…
Improving Cross-modal Alignment for Text-Guided Image Inpainting
Yucheng Zhou, Guodong Long
Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image. Existing methods are based on a strong vision encoder and a cross-moda…
Multimodal Event Transformer for Image-guided Story Ending Generation
Yucheng Zhou, Guodong Long
Image-guided story ending generation (IgSEG) is to generate a story ending based on given story plots and ending image. Existing methods focus on cross-modal feature fusion but ove…