2 citations · 3 across the 11 of their papers we have counts for
13 papers
RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case
Baihui Xiao, Chengjian Feng, Zhijian Huang +3
Collecting real-world data for rare high-risk scenarios, long-tailed driving events, and complex interactions remains challenging, leading to poor performance of existing autonomou…
X-SAM: From Segment Anything to Any Segmentation
Hao Wang, Limeng Qiao, Zequn Jie +6
Large Language Models (LLMs) demonstrate strong capabilities in broad knowledge representation, yet they are inherently deficient in pixel-level perceptual understanding. Although…
DisTime: Distribution-based Time Representation for Video Large Language Models
Yingsen Zeng, Zepeng Huang, Yujie Zhong +4
Despite advances in general video understanding, Video Large Language Models (Video-LLMs) face challenges in precise temporal localization due to discrete time representations and…
AP-CAP: Advancing High-Quality Data Synthesis for Animal Pose Estimation via a Controllable Image Generation Pipeline
Lei Wang, Yujie Zhong, Xiaopeng Sun +5
The task of 2D animal pose estimation plays a crucial role in advancing deep learning applications in animal behavior analysis and ecological research. Despite notable progress in…
InstructVEdit: A Holistic Approach for Instructional Video Editing
Chi Zhang, Chengjian Feng, Feng Yan +5
Video editing according to instructions is a highly challenging task due to the difficulty in collecting large-scale, high-quality edited video pair data. This scarcity not only li…
Boosting Robotic Manipulation Generalization with Minimal Costly Data
Liming Zheng, Feng Yan, Fanfan Liu +3
The growing adoption of Vision-Language-Action (VLA) models in embodied AI intensifies the demand for diverse manipulation demonstrations. However, high costs associated with data…
RoboTron-Nav: A Unified Framework for Embodied Navigation Integrating Perception, Planning, and Prediction
Yufeng Zhong, Chengjian Feng, Feng Yan +3
In language-guided visual navigation, agents locate target objects in unseen environments using natural language instructions. For reliable navigation in unfamiliar scenes, agents…
RFSR: Improving ISR Diffusion Models via Reward Feedback Learning
Xiaopeng Sun, Qinwei Lin, Yu Gao +6
Generative diffusion models (DM) have been extensively utilized in image super-resolution (ISR). Most of the existing methods adopt the denoising loss from DDPMs for model optimiza…
RoboTron-Drive: All-in-One Large Multimodal Model for Autonomous Driving
Zhijian Huang, Chengjian Feng, Feng Yan +5
Large Multimodal Models (LMMs) have demonstrated exceptional comprehension and interpretation capabilities in Autonomous Driving (AD) by incorporating large language models. Despit…
RoboTron-Mani: All-in-One Multimodal Large Model for Robotic Manipulation
Feng Yan, Fanfan Liu, Liming Zheng +5
Recently, robotics has advanced significantly through the integration of larger models and large-scale datasets. However, challenges remain in applying these models to 3D spatial i…
OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion
Hao Wang, Pengzhen Ren, Zequn Jie +8
Open-vocabulary detection is a challenging task due to the requirement of detecting objects based on class names, including those not encountered during training. Existing methods…
RoboCAS: A Benchmark for Robotic Manipulation in Complex Object Arrangement Scenarios
Liming Zheng, Feng Yan, Fanfan Liu +3
Foundation models hold significant potential for enabling robots to perform long-horizon general manipulation tasks. However, the simplicity of tasks and the uniformity of environm…
RoboUniView: Visual-Language Model with Unified View Representation for Robotic Manipulation
Fanfan Liu, Feng Yan, Liming Zheng +3
Utilizing Vision-Language Models (VLMs) for robotic manipulation represents a novel paradigm, aiming to enhance the model's ability to generalize to new objects and instructions. H…