66 citations · 89 across the 9 of their papers we have counts for
10 papers
SNAP: Towards Segmenting Anything in Any Point Cloud
Aniket Gupta, Hanhui Wang, Charles Saunders +3
Interactive 3D point cloud segmentation enables efficient annotation of complex 3D scenes through user-guided prompts. However, current approaches are typically restricted in scope…
Point-MoE: Large-Scale Multi-Dataset Training with Mixture-of-Experts for 3D Semantic Segmentation
Xuweiyi Chen, Wentao Zhou, Aruni RoyChowdhury +1
While massively scaling both data and models have become central in NLP and 2D vision, their benefits for 3D point cloud understanding remain limited. We study the initial step of…
DocTr: Document Transformer for Structured Information Extraction in Documents
Haofu Liao, Aruni RoyChowdhury, Weijian Li +6
We present a new formulation for structured information extraction (SIE) from visually rich documents. It aims to address the limitations of existing IOB tagging or graph-based for…
PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation
Gopal Sharma, Bidya Dash, Aruni RoyChowdhury +7
We present PriFit, a semi-supervised approach for label-efficient learning of 3D point cloud segmentation networks. PriFit combines geometric primitive fitting with point-based rep…
Improving Face Recognition by Clustering Unlabeled Faces in the Wild
Aruni RoyChowdhury, Xiang Yu, Kihyuk Sohn +2
While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reduci…
Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions
Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma +5
The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years. Both of these problems, however, suffer fr…
Automatic adaptation of object detectors to new domains using self-training
Aruni RoyChowdhury, Prithvijit Chakrabarty, Ashish Singh +4
This work addresses the unsupervised adaptation of an existing object detector to a new target domain. We assume that a large number of unlabeled videos from this domain are readil…
Unsupervised Hard Example Mining from Videos for Improved Object Detection
SouYoung Jin, Aruni RoyChowdhury, Huaizu Jiang +4
Important gains have recently been obtained in object detection by using training objectives that focus on {\em hard negative} examples, i.e., negative examples that are currently…
One-to-many face recognition with bilinear CNNs
Aruni RoyChowdhury, Tsung-Yu Lin, Subhransu Maji +1
The recent explosive growth in convolutional neural network (CNN) research has produced a variety of new architectures for deep learning. One intriguing new architecture is the bil…
Bilinear CNNs for Fine-grained Visual Recognition
Tsung-Yu Lin, Aruni RoyChowdhury, Subhransu Maji
We present a simple and effective architecture for fine-grained visual recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a po…