27 citations · 43 across the 8 of their papers we have counts for
8 papers
DualVLA: Building a Generalizable Embodied Agent via Partial Decoupling of Reasoning and Action
Zhen Fang, Zhuoyang Liu, Jiaming Liu +7
To build a generalizable Vision-Language-Action (VLA) model with strong reasoning ability, a common strategy is to first train a specialist VLA on robot demonstrations to acquire r…
MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines
Dongzhi Jiang, Renrui Zhang, Ziyu Guo +11
The advent of Large Language Models (LLMs) has paved the way for AI search engines, e.g., SearchGPT, showcasing a new paradigm in human-internet interaction. However, most current…
Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models
Zehui Chen, Kuikun Liu, Qiuchen Wang +5
Open-sourced Large Language Models (LLMs) have achieved great success in various NLP tasks, however, they are still far inferior to API-based models when acting as agents. How to i…
PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition
Chenhongyi Yang, Zehui Chen, Miguel Espinosa +4
We present PlainMamba: a simple non-hierarchical state space model (SSM) designed for general visual recognition. The recent Mamba model has shown how SSMs can be highly competitiv…
A Vanilla Multi-Task Framework for Dense Visual Prediction Solution to 1st VCL Challenge -- Multi-Task Robustness Track
Zehui Chen, Qiuchen Wang, Zhenyu Li +3
In this report, we present our solution to the multi-task robustness track of the 1st Visual Continual Learning (VCL) Challenge at ICCV 2023 Workshop. We propose a vanilla framewor…
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…
BEVUDA: Multi-geometric Space Alignments for Domain Adaptive BEV 3D Object Detection
Jiaming Liu, Rongyu Zhang, Xiaoqi Li +5
Vision-centric bird-eye-view (BEV) perception has shown promising potential in autonomous driving. Recent works mainly focus on improving efficiency or accuracy but neglect the cha…