Search Results for author: SouYoung Jin

Found 6 papers, 1 papers with code

Cross-Modal Discrete Representation Learning

no code implementations ACL 2022 Alexander H. Liu, SouYoung Jin, Cheng-I Jeff Lai, Andrew Rouditchenko, Aude Oliva, James Glass

Recent advances in representation learning have demonstrated an ability to represent information from different modalities such as video, text, and audio in a single high-level embedding vector.

Cross-Modal Retrieval Quantization +3

Spoken Moments: Learning Joint Audio-Visual Representations from Video Descriptions

no code implementations CVPR 2021 Mathew Monfort, SouYoung Jin, Alexander Liu, David Harwath, Rogerio Feris, James Glass, Aude Oliva

With this in mind, the descriptions people generate for videos of different dynamic events can greatly improve our understanding of the key information of interest in each video.

Contrastive Learning Retrieval +1

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 +2

End-To-End Face Detection and Cast Grouping in Movies Using Erdos-Renyi Clustering

no code implementations ICCV 2017 SouYoung Jin, Hang Su, Chris Stauffer, Erik Learned-Miller

We introduce a novel verification method, rank-1 counts verification, that has this property, and use it in a link-based clustering scheme.

Face Detection

End-to-end Face Detection and Cast Grouping in Movies Using Erdős-Rényi Clustering

no code implementations7 Sep 2017 SouYoung Jin, Hang Su, Chris Stauffer, Erik Learned-Miller

We introduce a novel verification method, rank-1 counts verification, that has this property, and use it in a link-based clustering scheme.

Face Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.