54 citations · 71 across the 14 of their papers we have counts for
8 papers · 1 filter
EPSegFZ: Efficient Point Cloud Semantic Segmentation for Few- and Zero-Shot Scenarios with Language Guidance
Jiahui Wang, Haiyue Zhu, Haoren Guo +3
Recent approaches for few-shot 3D point cloud semantic segmentation typically require a two-stage learning process, i.e., a pre-training stage followed by a few-shot training stage…
SingRef6D: Monocular Novel Object Pose Estimation with a Single RGB Reference
Jiahui Wang, Haiyue Zhu, Haoren Guo +3
Recent 6D pose estimation methods demonstrate notable performance but still face some practical limitations. For instance, many of them rely heavily on sensor depth, which may fail…
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention
Jiahui Wang, Haiyue Zhu, Haoren Guo +3
This paper presents an effective few-shot point cloud semantic segmentation approach for real-world applications. Existing few-shot segmentation methods on point cloud heavily rely…
Multi-Frequency-Aware Patch Adversarial Learning for Neural Point Cloud Rendering
Jay Karhade, Haiyue Zhu, Ka-Shing Chung +3
We present a neural point cloud rendering pipeline through a novel multi-frequency-aware patch adversarial learning framework. The proposed approach aims to improve the rendering r…
Incremental Few-Shot Learning via Implanting and Compressing
Yiting Li, Haiyue Zhu, Xijia Feng +5
This work focuses on tackling the challenging but realistic visual task of Incremental Few-Shot Learning (IFSL), which requires a model to continually learn novel classes from only…
Towards Generalized and Incremental Few-Shot Object Detection
Yiting Li, Haiyue Zhu, Jun Ma +4
Real-world object detection is highly desired to be equipped with the learning expandability that can enlarge its detection classes incrementally. Moreover, such learning from only…
Grasping Detection Network with Uncertainty Estimation for Confidence-Driven Semi-Supervised Domain Adaptation
Haiyue Zhu, Yiting Li, Fengjun Bai +6
Data-efficient domain adaptation with only a few labelled data is desired for many robotic applications, e.g., in grasping detection, the inference skill learned from a grasping da…
Incremental Few-Shot Object Detection for Robotics
Yiting Li, Haiyue Zhu, Sichao Tian +6
Incremental few-shot learning is highly expected for practical robotics applications. On one hand, robot is desired to learn new tasks quickly and flexibly using only few annotated…