84 citations · 375 across the 68 of their papers we have counts for
15 papers · 1 filter
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…
Robust Learning of Diffusion Models with Extremely Noisy Conditions
Xin Chen, Gillian Dobbie, Xinyu Wang +3
Conditional diffusion models have the generative controllability by incorporating external conditions. However, their performance significantly degrades with noisy conditions, such…
Goal-oriented Backdoor Attack against Vision-Language-Action Models via Physical Objects
Zirun Zhou, Zhengyang Xiao, Haochuan Xu +3
Recent advances in vision-language-action (VLA) models have greatly improved embodied AI, enabling robots to follow natural language instructions and perform diverse tasks. However…
A tutorial note on collecting simulated data for vision-language-action models
Heran Wu, Zirun Zhou, Jingfeng Zhang
Traditional robotic systems typically decompose intelligence into independent modules for computer vision, natural language processing, and motion control. Vision-Language-Action (…
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…
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…
Adversarial Preference Learning for Robust LLM Alignment
Yuanfu Wang, Pengyu Wang, Chenyang Xi +13
Modern language models often rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors. However, they remain vulnerable to adversarial attacks due to th…
Learning without Isolation: Pathway Protection for Continual Learning
Zhikang Chen, Abudukelimu Wuerkaixi, Sen Cui +10
Deep networks are prone to catastrophic forgetting during sequential task learning, i.e., losing the knowledge about old tasks upon learning new tasks. To this end, continual learn…
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…
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…
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…
One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy
Jiacheng Zhang, Benjamin I. P. Rubinstein, Jingfeng Zhang +1
Statistical adversarial data detection (SADD) detects whether an upcoming batch contains adversarial examples (AEs) by measuring the distributional discrepancies between clean exam…
Accurate Forgetting for Heterogeneous Federated Continual Learning
Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang +6
Recent years have witnessed a burgeoning interest in federated learning (FL). However, the contexts in which clients engage in sequential learning remain under-explored. Bridging F…
Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
Xingjun Ma, Yifeng Gao, Yixu Wang +45
The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artifici…
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…