227 citations · 378 across the 33 of their papers we have counts for
34 papers
Geometric Image Editing via Effects-Sensitive In-Context Inpainting with Diffusion Transformers
Shuo Zhang, Wenzhuo Wu, Huayu Zhang +8
Recent advances in diffusion models have significantly improved image editing. However, challenges persist in handling geometric transformations, such as translation, rotation, and…
AutoRed: A Free-form Adversarial Prompt Generation Framework for Automated Red Teaming
Muxi Diao, Yutao Mou, Keqing He +6
The safety of Large Language Models (LLMs) is crucial for the development of trustworthy AI applications. Existing red teaming methods often rely on seed instructions, which limits…
RotatedMVPS: Multi-view Photometric Stereo with Rotated Natural Light
Songyun Yang, Yufei Han, Jilong Zhang +4
Multiview photometric stereo (MVPS) seeks to recover high-fidelity surface shapes and reflectances from images captured under varying views and illuminations. However, existing MVP…
MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision
Zhonghao Yan, Muxi Diao, Yuxuan Yang +7
Accurately grounding regions of interest (ROIs) is critical for diagnosis and treatment planning in medical imaging. While multimodal large language models (MLLMs) combine visual p…
FairHuman: Boosting Hand and Face Quality in Human Image Generation with Minimum Potential Delay Fairness in Diffusion Models
Yuxuan Wang, Tianwei Cao, Huayu Zhang +3
Image generation has achieved remarkable progress with the development of large-scale text-to-image models, especially diffusion-based models. However, generating human images with…
Harnessing Caption Detailness for Data-Efficient Text-to-Image Generation
Xinran Wang, Muxi Diao, Yuanzhi Liu +4
Training text-to-image (T2I) models with detailed captions can significantly improve their generation quality. Existing methods often rely on simplistic metrics like caption length…
CineTechBench: A Benchmark for Cinematographic Technique Understanding and Generation
Xinran Wang, Songyu Xu, Xiangxuan Shan +6
Cinematography is a cornerstone of film production and appreciation, shaping mood, emotion, and narrative through visual elements such as camera movement, shot composition, and lig…
DriveRX: A Vision-Language Reasoning Model for Cross-Task Autonomous Driving
Muxi Diao, Lele Yang, Hongbo Yin +5
Effective autonomous driving hinges on robust reasoning across perception, prediction, planning, and behavior. However, conventional end-to-end models fail to generalize in complex…
ConMo: Controllable Motion Disentanglement and Recomposition for Zero-Shot Motion Transfer
Jiayi Gao, Zijin Yin, Changcheng Hua +5
The development of Text-to-Video (T2V) generation has made motion transfer possible, enabling the control of video motion based on existing footage. However, current methods have t…
Toward Generalizable Forgery Detection and Reasoning
Yueying Gao, Dongliang Chang, Bingyao Yu +5
Accurate and interpretable detection of AI-generated images is essential for mitigating risks associated with AI misuse. However, the substantial domain gap among generative models…
PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation
Zhonghao Yan, Zijin Yin, Tianyu Lin +3
The Segment Anything Model (SAM) has demonstrated strong and versatile segmentation capabilities, along with intuitive prompt-based interactions. However, customizing SAM for medic…
OmniEraser: Remove Objects and Their Effects in Images with Paired Video-Frame Data
Runpu Wei, Zijin Yin, Shuo Zhang +8
Inpainting algorithms have achieved remarkable progress in removing objects from images, yet still face two challenges: 1) struggle to handle the object's visual effects such as sh…
From Simple to Professional: A Combinatorial Controllable Image Captioning Agent
Xinran Wang, Muxi Diao, Baoteng Li +3
The Controllable Image Captioning Agent (CapAgent) is an innovative system designed to bridge the gap between user simplicity and professional-level outputs in image captioning tas…
Detailed Object Description with Controllable Dimensions
Xinran Wang, Haiwen Zhang, Baoteng Li +5
Object description plays an important role for visually impaired individuals to understand and compare the differences between objects. Recent multimodal large language models(MLLM…
Polyp-E: Benchmarking the Robustness of Deep Segmentation Models via Polyp Editing
Runpu Wei, Zijin Yin, Kongming Liang +6
Automatic polyp segmentation is helpful to assist clinical diagnosis and treatment. In daily clinical practice, clinicians exhibit robustness in identifying polyps with both locati…
Evaluating Attribute Comprehension in Large Vision-Language Models
Haiwen Zhang, Zixi Yang, Yuanzhi Liu +4
Currently, large vision-language models have gained promising progress on many downstream tasks. However, they still suffer many challenges in fine-grained visual understanding tas…
Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network
Wenjie Li, Heng Guo, Xuannan Liu +4
Face super-resolution aims to reconstruct a high-resolution face image from a low-resolution face image. Previous methods typically employ an encoder-decoder structure to extract f…
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew +55
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable A…
Benchmarking Segmentation Models with Mask-Preserved Attribute Editing
Zijin Yin, Kongming Liang, Bing Li +2
When deploying segmentation models in practice, it is critical to evaluate their behaviors in varied and complex scenes. Different from the previous evaluation paradigms only in co…
Vision-language Assisted Attribute Learning
Kongming Liang, Xinran Wang, Rui Wang +6
Attribute labeling at large scale is typically incomplete and partial, posing significant challenges to model optimization. Existing attribute learning methods often treat the miss…
Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images
Yi Zhong, Mengqiu Xu, Kongming Liang +2
Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections. Existing medical image segmentation methods are…
Super-Resolution Information Enhancement For Crowd Counting
Jiahao Xie, Wei Xu, Dingkang Liang +5
Crowd counting is a challenging task due to the heavy occlusions, scales, and density variations. Existing methods handle these challenges effectively while ignoring low-resolution…
Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation
Gangming Zhao, Kongming Liang, Chengwei Pan +4
Vessel segmentation is widely used to help with vascular disease diagnosis. Vessels reconstructed using existing methods are often not sufficiently accurate to meet clinical use st…
Multi-head Uncertainty Inference for Adversarial Attack Detection
Yuqi Yang, Songyun Yang, Jiyang Xie. Zhongwei Si +3
Deep neural networks (DNNs) are sensitive and susceptible to tiny perturbation by adversarial attacks which causes erroneous predictions. Various methods, including adversarial def…