no code implementations • ICCV 2023 • Haofu Liao, Aruni RoyChowdhury, Weijian Li, Ankan Bansal, Yuting Zhang, Zhuowen Tu, Ravi Kumar Satzoda, R. Manmatha, Vijay Mahadevan
We present a new formulation for structured information extraction (SIE) from visually rich documents.
Ranked #2 on Entity Linking on FUNSD
1 code implementation • 27 Dec 2021 • Gopal Sharma, Bidya Dash, Aruni RoyChowdhury, Matheus Gadelha, Marios Loizou, Liangliang Cao, Rui Wang, Erik Learned-Miller, Subhransu Maji, Evangelos Kalogerakis
We present PriFit, a semi-supervised approach for label-efficient learning of 3D point cloud segmentation networks.
no code implementations • ECCV 2020 • Aruni RoyChowdhury, Xiang Yu, Kihyuk Sohn, Erik Learned-Miller, Manmohan Chandraker
While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reducing the cost of human annotation.
1 code implementation • ECCV 2020 • Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma, Evangelos Kalogerakis, Liangliang Cao, Erik Learned-Miller, Rui Wang, Subhransu Maji
The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years.
1 code implementation • CVPR 2019 • Aruni RoyChowdhury, Prithvijit Chakrabarty, Ashish Singh, SouYoung Jin, Huaizu Jiang, Liangliang Cao, Erik Learned-Miller
Our results demonstrate the usefulness of incorporating hard examples obtained from tracking, the advantage of using soft-labels via distillation loss versus hard-labels, and show promising performance as a simple method for unsupervised domain adaptation of object detectors, with minimal dependence on hyper-parameters.
no code implementations • ECCV 2018 • SouYoung Jin, Aruni RoyChowdhury, Huaizu Jiang, Ashish Singh, Aditya Prasad, Deep Chakraborty, Erik Learned-Miller
In this work, we show how large numbers of hard negatives can be obtained {\em automatically} by analyzing the output of a trained detector on video sequences.
no code implementations • CVPR 2018 • Pia Bideau, Aruni RoyChowdhury, Rakesh R. Menon, Erik Learned-Miller
Traditional methods of motion segmentation use powerful geometric constraints to understand motion, but fail to leverage the semantics of high-level image understanding.
no code implementations • ICCV 2015 • Tsung-Yu Lin, Aruni RoyChowdhury, Subhransu Maji
We propose bilinear models, a recognition architecture that consists of two feature extractors whose outputs are multiplied using outer product at each location of the image and pooled to obtain an image descriptor.
Ranked #62 on Fine-Grained Image Classification on CUB-200-2011
Fine-Grained Image Classification Fine-Grained Visual Recognition
no code implementations • 3 Jun 2015 • Aruni RoyChowdhury, Tsung-Yu Lin, Subhransu Maji, Erik Learned-Miller
We demonstrate the performance of the B-CNN model beginning from an AlexNet-style network pre-trained on ImageNet.
4 code implementations • 29 Apr 2015 • Tsung-Yu Lin, Aruni RoyChowdhury, Subhransu Maji
We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture.
Ranked #23 on Fine-Grained Image Classification on NABirds
Fine-Grained Image Classification Fine-Grained Visual Recognition +1