Search Results for author: Aruni RoyChowdhury

Found 8 papers, 3 papers with code

Improving Face Recognition by Clustering Unlabeled Faces in the Wild

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.

Face Clustering Face Recognition +2

Automatic adaptation of object detectors to new domains using self-training

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.

Knowledge Distillation Pedestrian Detection +1

Unsupervised Hard Example Mining from Videos for Improved Object Detection

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.

Face Detection Object Detection +1

The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation

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.

Motion Segmentation Semantic Segmentation

Bilinear CNN Models for Fine-Grained Visual Recognition

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.

Fine-Grained Image Classification Fine-Grained Visual Recognition

One-to-many face recognition with bilinear CNNs

no code implementations3 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.

Face Detection Face Model +1

Bilinear CNNs for Fine-grained Visual Recognition

2 code implementations29 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.

Fine-Grained Visual Recognition Image Classification +1

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