86 citations · 250 across the 17 of their papers we have counts for
20 papers
AutoGUI-v2: A Comprehensive Multi-Modal GUI Functionality Understanding Benchmark
Hongxin Li, Xiping Wang, Jingran Su +4
Autonomous agents capable of navigating Graphical User Interfaces (GUIs) hold the potential to revolutionize digital productivity. However, achieving true digital autonomy extends…
GoClick: Lightweight Element Grounding Model for Autonomous GUI Interaction
Hongxin Li, Yuntao Chen, Zhaoxiang Zhang
Graphical User Interface (GUI) element grounding (precisely locating elements on screenshots based on natural language instructions) is fundamental for agents interacting with GUIs…
DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
Yingyan Li, Shuyao Shang, Weisong Liu +10
Scaling Vision-Language-Action (VLA) models on large-scale data offers a promising path to achieving a more generalized driving intelligence. However, VLA models are limited by a `…
DriveDPO: Policy Learning via Safety DPO For End-to-End Autonomous Driving
Shuyao Shang, Yuntao Chen, Yuqi Wang +2
End-to-end autonomous driving has substantially progressed by directly predicting future trajectories from raw perception inputs, which bypasses traditional modular pipelines. Howe…
UIPro: Unleashing Superior Interaction Capability For GUI Agents
Hongxin Li, Jingran Su, Jingfan Chen +4
Building autonomous agents that perceive and operate graphical user interfaces (GUIs) like humans has long been a vision in the field of artificial intelligence. Central to these a…
Unified Vision-Language-Action Model
Yuqi Wang, Xinghang Li, Wenxuan Wang +5
Vision-language-action models (VLAs) have garnered significant attention for their potential in advancing robotic manipulation. However, previous approaches predominantly rely on t…
AutoGUI: Scaling GUI Grounding with Automatic Functionality Annotations from LLMs
Hongxin Li, Jingfan Chen, Jingran Su +3
User interface understanding with vision-language models (VLMs) has received much attention due to its potential for enhancing software automation. However, existing datasets used…
DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers
Yuntao Chen, Yuqi Wang, Zhaoxiang Zhang
World model-based searching and planning are widely recognized as a promising path toward human-level physical intelligence. However, current driving world models primarily rely on…
OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction
Hongbo Zhao, Lue Fan, Yuntao Chen +6
In this paper, we propose OpenSatMap, a fine-grained, high-resolution satellite dataset for large-scale map construction. Map construction is one of the foundations of the transpor…
FreeVS: Generative View Synthesis on Free Driving Trajectory
Qitai Wang, Lue Fan, Yuqi Wang +2
Existing reconstruction-based novel view synthesis methods for driving scenes focus on synthesizing camera views along the recorded trajectory of the ego vehicle. Their image rende…
DrivingDojo Dataset: Advancing Interactive and Knowledge-Enriched Driving World Model
Yuqi Wang, Ke Cheng, Jiawei He +5
Driving world models have gained increasing attention due to their ability to model complex physical dynamics. However, their superb modeling capability is yet to be fully unleashe…
BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision
Chenyu Yang, Yuntao Chen, Hao Tian +9
We present a novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and better suits modern image backbones. Existing state-of-the-art BEV detect…
4D Unsupervised Object Discovery
Yuqi Wang, Yuntao Chen, Zhaoxiang Zhang
Object discovery is a core task in computer vision. While fast progresses have been made in supervised object detection, its unsupervised counterpart remains largely unexplored. Wi…
Unsupervised Object Detection with LiDAR Clues
Hao Tian, Yuntao Chen, Jifeng Dai +2
Despite the importance of unsupervised object detection, to the best of our knowledge, there is no previous work addressing this problem. One main issue, widely known to the commun…
Sequence Level Semantics Aggregation for Video Object Detection
Haiping Wu, Yuntao Chen, Naiyan Wang +1
Video objection detection (VID) has been a rising research direction in recent years. A central issue of VID is the appearance degradation of video frames caused by fast motion. Th…
Revisiting Feature Alignment for One-stage Object Detection
Yuntao Chen, Chenxia Han, Naiyan Wang +1
Recently, one-stage object detectors gain much attention due to their simplicity in practice. Its fully convolutional nature greatly reduces the difficulty of training and deployme…
SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition
Yuntao Chen, Chenxia Han, Yanghao Li +4
Object detection and instance recognition play a central role in many AI applications like autonomous driving, video surveillance and medical image analysis. However, training obje…
Scale-Aware Trident Networks for Object Detection
Yanghao Li, Yuntao Chen, Naiyan Wang +1
Scale variation is one of the key challenges in object detection. In this work, we first present a controlled experiment to investigate the effect of receptive fields for scale var…
Spectral Feature Transformation for Person Re-identification
Chuanchen Luo, Yuntao Chen, Naiyan Wang +1
With the surge of deep learning techniques, the field of person re-identification has witnessed rapid progress in recent years. Deep learning based methods focus on learning a feat…
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer
Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang
We have witnessed rapid evolution of deep neural network architecture design in the past years. These latest progresses greatly facilitate the developments in various areas such as…