7 citations · 28 across the 43 of their papers we have counts for
13 papers · 1 filter
Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Network
Ziqing Wang, Yuetong Fang, Jiahang Cao +2
Spiking Neural Networks (SNNs) are seen as an energy-efficient alternative to traditional Artificial Neural Networks (ANNs), but the performance gap remains a challenge. While this…
Not Just Text: Uncovering Vision Modality Typographic Threats in Image Generation Models
Hao Cheng, Erjia Xiao, Jiayan Yang +6
Current image generation models can effortlessly produce high-quality, highly realistic images, but this also increases the risk of misuse. In various Text-to-Image or Image-to-Ima…
Robust SG-NeRF: Robust Scene Graph Aided Neural Surface Reconstruction
Yi Gu, Dongjun Ye, Zhaorui Wang +3
Neural surface reconstruction relies heavily on accurate camera poses as input. Despite utilizing advanced pose estimators like COLMAP or ARKit, camera poses can still be noisy. Ex…
E2H: A Two-Stage Non-Invasive Neural Signal Driven Humanoid Robotic Whole-Body Control Framework
Yiqun Duan, Qiang Zhang, Jinzhao Zhou +9
Recent advancements in humanoid robotics, including the integration of hierarchical reinforcement learning-based control and the utilization of LLM planning, have significantly enh…
Spiking Neural Network as Adaptive Event Stream Slicer
Jiahang Cao, Mingyuan Sun, Ziqing Wang +4
Event-based cameras are attracting significant interest as they provide rich edge information, high dynamic range, and high temporal resolution. Many state-of-the-art event-based a…
Manipulation Facing Threats: Evaluating Physical Vulnerabilities in End-to-End Vision Language Action Models
Hao Cheng, Erjia Xiao, Yichi Wang +12
Recently, driven by advancements in Multimodal Large Language Models (MLLMs), Vision Language Action Models (VLAMs) are being proposed to achieve better performance in open-vocabul…
Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models
Jiahang Cao, Qiang Zhang, Jingkai Sun +11
Diffusion models have been widely employed in the field of 3D manipulation due to their efficient capability to learn distributions, allowing for precise prediction of action traje…
Spiking Diffusion Models
Jiahang Cao, Hanzhong Guo, Ziqing Wang +4
Recent years have witnessed Spiking Neural Networks (SNNs) gaining attention for their ultra-low energy consumption and high biological plausibility compared with traditional Artif…
Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement Learning
Jiahang Cao, Qiang Zhang, Ziqing Wang +8
Sequential modeling has demonstrated remarkable capabilities in offline reinforcement learning (RL), with Decision Transformer (DT) being one of the most notable representatives, a…
Transfer Attack for Bad and Good: Explain and Boost Adversarial Transferability across Multimodal Large Language Models
Hao Cheng, Erjia Xiao, Jiayan Yang +8
Multimodal Large Language Models (MLLMs) demonstrate exceptional performance in cross-modality interaction, yet they also suffer adversarial vulnerabilities. In particular, the tra…
Reinforcement Learning with Generalizable Gaussian Splatting
Jiaxu Wang, Qiang Zhang, Jingkai Sun +7
An excellent representation is crucial for reinforcement learning (RL) performance, especially in vision-based reinforcement learning tasks. The quality of the environment represen…
Spiking Wavelet Transformer
Yuetong Fang, Ziqing Wang, Lingfeng Zhang +3
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by emulating the event-driven processing manner of the brain. Incorporating Trans…
Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model
Hao Cheng, Erjia Xiao, Jindong Gu +6
Large Vision-Language Models (LVLMs) rely on vision encoders and Large Language Models (LLMs) to exhibit remarkable capabilities on various multi-modal tasks in the joint space of…