4 citations · 20 across the 17 of their papers we have counts for
17 papers
Adapt2Reward: Adapting Video-Language Models to Generalizable Robotic Rewards via Failure Prompts
Yanting Yang, Minghao Chen, Qibo Qiu +5
For a general-purpose robot to operate in reality, executing a broad range of instructions across various environments is imperative. Central to the reinforcement learning and plan…
PD-APE: A Parallel Decoding Framework with Adaptive Position Encoding for 3D Visual Grounding
Chenshu Hou, Liang Peng, Xiaopei Wu +2
3D visual grounding aims to identify objects in 3D point cloud scenes that match specific natural language descriptions. This requires the model to not only focus on the target obj…
Towards Fundamentally Scalable Model Selection: Asymptotically Fast Update and Selection
Wenxiao Wang, Weiming Zhuang, Lingjuan Lyu
The advancement of deep learning technologies is bringing new models every day, motivating the study of scalable model selection. An ideal model selection scheme should minimally s…
Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection
Zhanwei Zhang, Minghao Chen, Shuai Xiao +7
Recent self-training techniques have shown notable improvements in unsupervised domain adaptation for 3D object detection (3D UDA). These techniques typically select pseudo labels,…
To be or not to be? an exploration of continuously controllable prompt engineering
Yuhan Sun, Mukai Li, Yixin Cao +4
As the use of large language models becomes more widespread, techniques like parameter-efficient fine-tuning and other methods for controlled generation are gaining traction for cu…
MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection
Junkai Xu, Liang Peng, Haoran Cheng +5
In the field of monocular 3D detection, it is common practice to utilize scene geometric clues to enhance the detector's performance. However, many existing works adopt these clues…