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20182026
most citedAttacks Which Do Not Kill Training Make Adversarial Learning Stronger

84 citations · 375 across the 68 of their papers we have counts for

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28 papers · 1 filter

cs.CV2026

DuplexBridge: Coordinating Understanding and Generation for Unified Image Editing

Yidi Liu, Jie Xiao, Chaojie Mao +2

Complex image editing requires faithful task interpretation and accurate execution, but a failed edit does not reveal which responsibility failed. We present DuplexBridge, which us…

cs.CV2026

Avatar V: Scaling Video-Reference Avatar Video Generation

Benjamin Liang, Ce Chen, Desmond Lin +20

Generating avatar videos that are not merely visually similar to a target individual but behaviorally recognizable, faithfully reproducing their talking rhythm, gestural tendencies…

cs.CV2026

Wan-Image: Pushing the Boundaries of Generative Visual Intelligence

Chaojie Mao, Chen-Wei Xie, Chongyang Zhong +55

We present Wan-Image, a unified visual generation system explicitly engineered to paradigm-shift image generation models from casual synthesizers into professional-grade productivi…

cs.CV2026

Wan-Weaver: Interleaved Multi-modal Generation via Decoupled Training

Jinbo Xing, Zeyinzi Jiang, Yuxiang Tuo +15

Recent unified models have made unprecedented progress in both understanding and generation. However, while most of them accept multi-modal inputs, they typically produce only sing…

cs.CV2025

Model-agnostic Adversarial Attack and Defense for Vision-Language-Action Models

Haochuan Xu, Yun Sing Koh, Shuhuai Huang +4

Vision-Language-Action (VLA) models have achieved revolutionary progress in robot learning, enabling robots to execute complex physical robot tasks from natural language instructio…

cs.CV2025

InstructAttribute: Fine-grained Object Attributes editing with Instruction

Xingxi Yin, Jingfeng Zhang, Yue Deng +3

Text-to-image (T2I) diffusion models are widely used in image editing due to their powerful generative capabilities. However, achieving fine-grained control over specific object at…

cs.CV2025

Stable Vision Concept Transformers for Medical Diagnosis

Lijie Hu, Songning Lai, Yuan Hua +3

Transparency is a paramount concern in the medical field, prompting researchers to delve into the realm of explainable AI (XAI). Among these XAI methods, Concept Bottleneck Models…

cs.CV2025★ 12 cited

Wan: Open and Advanced Large-Scale Video Generative Models

Team Wan, Ang Wang, Baole Ai +58

This report presents Wan, a comprehensive and open suite of video foundation models designed to push the boundaries of video generation. Built upon the mainstream diffusion transfo…

cs.CV2025

VACE: All-in-One Video Creation and Editing

Zeyinzi Jiang, Zhen Han, Chaojie Mao +3

Diffusion Transformer has demonstrated powerful capability and scalability in generating high-quality images and videos. Further pursuing the unification of generation and editing…

cs.CV2025

ICE-Bench: A Unified and Comprehensive Benchmark for Image Creating and Editing

Yulin Pan, Xiangteng He, Chaojie Mao +4

Image generation has witnessed significant advancements in the past few years. However, evaluating the performance of image generation models remains a formidable challenge. In thi…

cs.CV2025

ACE++: Instruction-Based Image Creation and Editing via Context-Aware Content Filling

Chaojie Mao, Jingfeng Zhang, Yulin Pan +4

We report ACE++, an instruction-based diffusion framework that tackles various image generation and editing tasks. Inspired by the input format for the inpainting task proposed by…

cs.CV2024

ACE: All-round Creator and Editor Following Instructions via Diffusion Transformer

Zhen Han, Zeyinzi Jiang, Yulin Pan +5

Diffusion models have emerged as a powerful generative technology and have been found to be applicable in various scenarios. Most existing foundational diffusion models are primari…

cs.CV2024★ 1 cited

ColorEdit: Training-free Image-Guided Color editing with diffusion model

Xingxi Yin, Zhi Li, Jingfeng Zhang +2

Text-to-image (T2I) diffusion models, with their impressive generative capabilities, have been adopted for image editing tasks, demonstrating remarkable efficacy. However, due to a…

cs.CV2024

An Individual Identity-Driven Framework for Animal Re-Identification

Yihao Wu, Di Zhao, Jingfeng Zhang +1

Reliable re-identification of individuals within large wildlife populations is crucial for biological studies, ecological research, and wildlife conservation. Classic computer visi…

cs.CV2024★ 1 cited

Towards Multi-dimensional Explanation Alignment for Medical Classification

Lijie Hu, Songning Lai, Wenshuo Chen +5

The lack of interpretability in the field of medical image analysis has significant ethical and legal implications. Existing interpretable methods in this domain encounter several…

cs.CV2024

Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training

Jiacheng Zhang, Feng Liu, Dawei Zhou +2

Adversarial training (AT) trains models using adversarial examples (AEs), which are natural images modified with specific perturbations to mislead the model. These perturbations ar…

cs.CV2024

Text Guided Image Editing with Automatic Concept Locating and Forgetting

Jia Li, Lijie Hu, Zhixian He +3

With the advancement of image-to-image diffusion models guided by text, significant progress has been made in image editing. However, a persistent challenge remains in seamlessly i…

cs.CV2024

StyleBooth: Image Style Editing with Multimodal Instruction

Zhen Han, Chaojie Mao, Zeyinzi Jiang +2

Given an original image, image editing aims to generate an image that align with the provided instruction. The challenges are to accept multimodal inputs as instructions and a scar…

cs.CV2024

Locate, Assign, Refine: Taming Customized Promptable Image Inpainting

Yulin Pan, Chaojie Mao, Zeyinzi Jiang +3

Prior studies have made significant progress in image inpainting guided by either text description or subject image. However, the research on inpainting with flexible guidance or c…

cs.CV2024

Make Me Happier: Evoking Emotions Through Image Diffusion Models

Qing Lin, Jingfeng Zhang, Yew-Soon Ong +1

Despite the rapid progress in image generation, emotional image editing remains under-explored. The semantics, context, and structure of an image can evoke emotional responses, mak…

cs.CV2023★ 1 cited

SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing

Zeyinzi Jiang, Chaojie Mao, Yulin Pan +2

Image diffusion models have been utilized in various tasks, such as text-to-image generation and controllable image synthesis. Recent research has introduced tuning methods that ma…

cs.CV2023★ 2 cited

Fair Text-to-Image Diffusion via Fair Mapping

Jia Li, Lijie Hu, Jingfeng Zhang +3

In this paper, we address the limitations of existing text-to-image diffusion models in generating demographically fair results when given human-related descriptions. These models…

cs.CV2023★ 1 cited

GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks

Salah Ghamizi, Jingfeng Zhang, Maxime Cordy +3

While leveraging additional training data is well established to improve adversarial robustness, it incurs the unavoidable cost of data collection and the heavy computation to trai…

cs.CV2022★ 3 cited

Accelerating Score-based Generative Models for High-Resolution Image Synthesis

Hengyuan Ma, Li Zhang, Xiatian Zhu +2

Score-based generative models (SGMs) have recently emerged as a promising class of generative models. The key idea is to produce high-quality images by recurrently adding Gaussian…