31 citations · 33 across the 16 of their papers we have counts for
9 papers · 1 filter
LLaDA-VLA: Vision Language Diffusion Action Models
Yuqing Wen, Hebei Li, Kefan Gu +3
The rapid progress of auto-regressive vision-language models (VLMs) has inspired growing interest in vision-language-action models (VLA) for robotic manipulation. Recently, masked…
DASH: 4D Hash Encoding with Self-Supervised Decomposition for Real-Time Dynamic Scene Rendering
Jie Chen, Zhangchi Hu, Peixi Wu +3
Dynamic scene reconstruction is a long-term challenge in 3D vision. Existing plane-based methods in dynamic Gaussian splatting suffer from an unsuitable low-rank assumption, causin…
Efficient Event-Based Semantic Segmentation via Exploiting Frame-Event Fusion: A Hybrid Neural Network Approach
Hebei Li, Yansong Peng, Jiahui Yuan +4
Event cameras have recently been introduced into image semantic segmentation, owing to their high temporal resolution and other advantageous properties. However, existing event-bas…
FACM: Flow-Anchored Consistency Models
Yansong Peng, Kai Zhu, Yu Liu +4
Continuous-time Consistency Models (CMs) promise efficient few-step generation but face significant challenges with training instability. We argue this instability stems from a fun…
Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects
Wei Li, Hebei Li, Yansong Peng +3
Diffusion models have significantly advanced text-to-image generation, laying the foundation for the development of personalized generative frameworks. However, existing methods la…
Dome-DETR: DETR with Density-Oriented Feature-Query Manipulation for Efficient Tiny Object Detection
Zhangchi Hu, Peixi Wu, Jie Chen +5
Tiny object detection plays a vital role in drone surveillance, remote sensing, and autonomous systems, enabling the identification of small targets across vast landscapes. However…
Efficient Spiking Point Mamba for Point Cloud Analysis
Peixi Wu, Bosong Chai, Menghua Zheng +6
Bio-inspired Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. However, existing 3D SNNs have struggled with long-range depende…
COSMIC: Clique-Oriented Semantic Multi-space Integration for Robust CLIP Test-Time Adaptation
Fanding Huang, Jingyan Jiang, Qinting Jiang +3
Recent vision-language models (VLMs) face significant challenges in test-time adaptation to novel domains. While cache-based methods show promise by leveraging historical informati…
Spiking Point Transformer for Point Cloud Classification
Peixi Wu, Bosong Chai, Hebei Li +6
Spiking Neural Networks (SNNs) offer an attractive and energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their sparse binary activation. When SN…