19 citations · 57 across the 12 of their papers we have counts for
12 papers
GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo Labelers
Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen
Instance segmentation on 3D point clouds (3DIS) is a longstanding challenge in computer vision, where state-of-the-art methods are mainly based on full supervision. As annotating g…
ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution
Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen
Existing 3D instance segmentation methods are predominated by the bottom-up design -- manually fine-tuned algorithm to group points into clusters followed by a refinement network.…
PSENet: Progressive Self-Enhancement Network for Unsupervised Extreme-Light Image Enhancement
Hue Nguyen, Diep Tran, Khoi Nguyen +1
The extremes of lighting (e.g. too much or too little light) usually cause many troubles for machine and human vision. Many recent works have mainly focused on under-exposure cases…
Geodesic-Former: a Geodesic-Guided Few-shot 3D Point Cloud Instance Segmenter
Tuan Ngo, Khoi Nguyen
This paper introduces a new problem in 3D point cloud: few-shot instance segmentation. Given a few annotated point clouds exemplified a target class, our goal is to segment all ins…
Few-shot Object Counting and Detection
Thanh Nguyen, Chau Pham, Khoi Nguyen +1
We tackle a new task of few-shot object counting and detection. Given a few exemplar bounding boxes of a target object class, we seek to count and detect all objects of the target…
Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments
Khoi D. Nguyen, Quoc-Huy Tran, Khoi Nguyen +2
We present a novel method for few-shot video classification, which performs appearance and temporal alignments. In particular, given a pair of query and support videos, we conduct…
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples
Duong H. Le, Khoi D. Nguyen, Khoi Nguyen +3
In this work, we propose to use out-of-distribution samples, i.e., unlabeled samples coming from outside the target classes, to improve few-shot learning. Specifically, we exploit…
iFS-RCNN: An Incremental Few-shot Instance Segmenter
Khoi Nguyen, Sinisa Todorovic
This paper addresses incremental few-shot instance segmentation, where a few examples of new object classes arrive when access to training examples of old classes is not available…
A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation
Khoi Nguyen, Sinisa Todorovic
This paper addresses weakly supervised amodal instance segmentation, where the goal is to segment both visible and occluded (amodal) object parts, while training provides only grou…
Semi-Supervising Learning, Transfer Learning, and Knowledge Distillation with SimCLR
Khoi Nguyen, Yen Nguyen, Bao Le
Recent breakthroughs in the field of semi-supervised learning have achieved results that match state-of-the-art traditional supervised learning methods. Most successful semi-superv…
A Self-supervised GAN for Unsupervised Few-shot Object Recognition
Khoi Nguyen, Sinisa Todorovic
This paper addresses unsupervised few-shot object recognition, where all training images are unlabeled, and test images are divided into queries and a few labeled support images pe…
Feature Weighting and Boosting for Few-Shot Segmentation
Khoi Nguyen, Sinisa Todorovic
This paper is about few-shot segmentation of foreground objects in images. We train a CNN on small subsets of training images, each mimicking the few-shot setting. In each subset,…