13 citations · 37 across the 10 of their papers we have counts for
10 papers
Graph World Models: Concepts, Taxonomy, and Future Directions
Jiawei Liu, Senqiao Yang, Mingjun Wang +2
As one of the mainstream models of artificial intelligence, world models allow agents to learn the representation of the environment for efficient prediction and planning. However,…
RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation
Jiaming Liu, Mengzhen Liu, Zhenyu Wang +7
A fundamental objective in robot manipulation is to enable models to comprehend visual scenes and execute actions. Although existing Vision-Language-Action (VLA) models for robots…
LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding
Senqiao Yang, Jiaming Liu, Ray Zhang +7
Recently, Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have shown promise in instruction following and 2D image understanding. While these models are p…
Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation
Jiaming Liu, Ran Xu, Senqiao Yang +5
Continual Test-Time Adaptation (CTTA) is proposed to migrate a source pre-trained model to continually changing target distributions, addressing real-world dynamism. Existing CTTA…
Distribution-Aware Continual Test-Time Adaptation for Semantic Segmentation
Jiayi Ni, Senqiao Yang, Ran Xu +6
Since autonomous driving systems usually face dynamic and ever-changing environments, continual test-time adaptation (CTTA) has been proposed as a strategy for transferring deploye…
PM-DETR: Domain Adaptive Prompt Memory for Object Detection with Transformers
Peidong Jia, Jiaming Liu, Senqiao Yang +3
The Transformer-based detectors (i.e., DETR) have demonstrated impressive performance on end-to-end object detection. However, transferring DETR to different data distributions may…
ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation
Jiaming Liu, Senqiao Yang, Peidong Jia +5
Since real-world machine systems are running in non-stationary environments, Continual Test-Time Adaptation (CTTA) task is proposed to adapt the pre-trained model to continually ch…
Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction
Senqiao Yang, Jiarui Wu, Jiaming Liu +6
The visual prompts have provided an efficient manner in addressing visual cross-domain problems. In previous works, Visual Domain Prompt (VDP) first introduces domain prompts to ta…
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier
Zelin Zang, Lei Shang, Senqiao Yang +4
Unsupervised domain adaptation (UDA) has proven to be highly effective in transferring knowledge from a label-rich source domain to a label-scarce target domain. However, the prese…
UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection
Zelin Zang, Yongjie Xu, Linyan Lu +3
Dimensional reduction~(DR) maps high-dimensional data into a lower dimensions latent space with minimized defined optimization objectives. The DR method usually falls into feature…