6 papers
Chain of World: World Model Thinking in Latent Motion
Fuxiang Yang, Donglin Di, Lulu Tang +6
Vision-Language-Action (VLA) models are a promising path toward embodied intelligence, yet they often overlook the predictive and temporal-causal structure underlying visual dynami…
Driving in Spikes: An Entropy-Guided Object Detector for Spike Cameras
Ziyan Liu, Qi Su, Lulu Tang +2
Object detection in autonomous driving suffers from motion blur and saturation under fast motion and extreme lighting. Spike cameras, offer microsecond latency and ultra high dynam…
VisualDAN: Exposing Vulnerabilities in VLMs with Visual-Driven DAN Commands
Aofan Liu, Lulu Tang
Vision-Language Models (VLMs) have garnered significant attention for their remarkable ability to interpret and generate multimodal content. However, securing these models against…
PiCo: Jailbreaking Multimodal Large Language Models via Pictorial Code Contextualization
Aofan Liu, Lulu Tang, Ting Pan +3
Multimodal Large Language Models (MLLMs), which integrate vision and other modalities into Large Language Models (LLMs), significantly enhance AI capabilities but also introduce ne…
You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale
Baorui Ma, Huachen Gao, Haoge Deng +4
Recent 3D generation models typically rely on limited-scale 3D `gold-labels' or 2D diffusion priors for 3D content creation. However, their performance is upper-bounded by constrai…
Tokenize Anything via Prompting
Ting Pan, Lulu Tang, Xinlong Wang +1
We present a unified, promptable model capable of simultaneously segmenting, recognizing, and captioning anything. Unlike SAM, we aim to build a versatile region representation in…