18 citations · 34 across the 13 of their papers we have counts for
13 papers
ImitDiff: Transferring Foundation-Model Priors for Distraction Robust Visuomotor Policy
Yuhang Dong, Haizhou Ge, Yupei Zeng +9
Visuomotor imitation learning policies enable robots to efficiently acquire manipulation skills from visual demonstrations. However, as scene complexity and visual distractions inc…
Training-Free Model Merging for Multi-target Domain Adaptation
Wenyi Li, Huan-ang Gao, Mingju Gao +3
In this paper, we study multi-target domain adaptation of scene understanding models. While previous methods achieved commendable results through inter-domain consistency losses, t…
Adaptive Surface Normal Constraint for Geometric Estimation from Monocular Images
Xiaoxiao Long, Yuhang Zheng, Yupeng Zheng +6
We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context. The difficulty of reliably capturing geometric…
Key Patch Proposer: Key Patches Contain Rich Information
Jing Xu, Beiwen Tian, Hao Zhao
In this paper, we introduce a novel algorithm named Key Patch Proposer (KPP) designed to select key patches in an image without additional training. Our experiments showcase KPP's…
Latency-aware Road Anomaly Segmentation in Videos: A Photorealistic Dataset and New Metrics
Beiwen Tian, Huan-ang Gao, Leiyao Cui +6
In the past several years, road anomaly segmentation is actively explored in the academia and drawing growing attention in the industry. The rationale behind is straightforward: if…
Delving into Shape-aware Zero-shot Semantic Segmentation
Xinyu Liu, Beiwen Tian, Zhen Wang +5
Thanks to the impressive progress of large-scale vision-language pretraining, recent recognition models can classify arbitrary objects in a zero-shot and open-set manner, with a su…
DQS3D: Densely-matched Quantization-aware Semi-supervised 3D Detection
Huan-ang Gao, Beiwen Tian, Pengfei Li +2
In this paper, we study the problem of semi-supervised 3D object detection, which is of great importance considering the high annotation cost for cluttered 3D indoor scenes. We res…
From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds
Huan-ang Gao, Beiwen Tian, Pengfei Li +5
Room layout estimation is a long-existing robotic vision task that benefits both environment sensing and motion planning. However, layout estimation using point clouds (PCs) still…
VIBUS: Data-efficient 3D Scene Parsing with VIewpoint Bottleneck and Uncertainty-Spectrum Modeling
Beiwen Tian, Liyi Luo, Hao Zhao +1
Recently, 3D scenes parsing with deep learning approaches has been a heating topic. However, current methods with fully-supervised models require manually annotated point-wise supe…
TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation
Pengfei Li, Beiwen Tian, Yongliang Shi +4
Current referring expression comprehension algorithms can effectively detect or segment objects indicated by nouns, but how to understand verb reference is still under-explored. As…
Language-guided Semantic Style Transfer of 3D Indoor Scenes
Bu Jin, Beiwen Tian, Hao Zhao +1
We address the new problem of language-guided semantic style transfer of 3D indoor scenes. The input is a 3D indoor scene mesh and several phrases that describe the target scene. F…
Unsupervised Cross-Task Generalization via Retrieval Augmentation
Bill Yuchen Lin, Kangmin Tan, Chris Miller +2
Humans can perform unseen tasks by recalling relevant skills acquired previously and then generalizing them to the target tasks, even if there is no supervision at all. In this pap…
Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck
Liyi Luo, Beiwen Tian, Hao Zhao +1
Semantic understanding of 3D point clouds is important for various robotics applications. Given that point-wise semantic annotation is expensive, in this paper, we address the chal…