1 code implementation • NAACL 2022 • Seungju Han, Beomsu Kim, Jin Yong Yoo, Seokjun Seo, SangBum Kim, Enkhbayar Erdenee, Buru Chang
To better reflect the style of the character, PDP builds the prompts in the form of dialog that includes the character's utterances as dialog history.
1 code implementation • NLP4ConvAI (ACL) 2022 • Seungju Han, Beomsu Kim, Seokjun Seo, Enkhbayar Erdenee, Buru Chang
Extensive experiments demonstrate that our proposed training method alleviates the drawbacks of the existing exemplar-based generative models and significantly improves the performance in terms of appropriateness and informativeness.
1 code implementation • Findings (EMNLP) 2021 • Beomsu Kim, Seokjun Seo, Seungju Han, Enkhbayar Erdenee, Buru Chang
G2R consists of two distinct techniques of distillation: the data-level G2R augments the dialogue dataset with additional responses generated by the large-scale generative model, and the model-level G2R transfers the response quality score assessed by the generative model to the score of the retrieval model by the knowledge distillation loss.
2 code implementations • CVPR 2021 • Youngkyu Hong, Seungju Han, Kwanghee Choi, Seokjun Seo, Beomsu Kim, Buru Chang
Although this method surpasses state-of-the-art methods on benchmark datasets, it can be further improved by directly disentangling the source label distribution from the model prediction in the training phase.
Ranked #20 on Long-tail Learning on Places-LT
no code implementations • 19 Nov 2019 • Sungjoo Ha, Martin Kersner, Beomsu Kim, Seokjun Seo, Dongyoung Kim
When there is a mismatch between the target identity and the driver identity, face reenactment suffers severe degradation in the quality of the result, especially in a few-shot setting.
3 code implementations • 8 Apr 2019 • Seungwoo Choi, Seokjun Seo, Beomjun Shin, Hyeongmin Byun, Martin Kersner, Beomsu Kim, Dongyoung Kim, Sungjoo Ha
In addition, we release the implementation of the proposed and the baseline models including an end-to-end pipeline for training models and evaluating them on mobile devices.
Ranked #14 on Keyword Spotting on Google Speech Commands (Google Speech Commands V2 12 metric)
2 code implementations • 8 Apr 2019 • Seokjun Seo, Seungwoo Choi, Martin Kersner, Beomjun Shin, Hyungsuk Yoon, Hyeongmin Byun, Sungjoo Ha
We tackle the problem of automatic portrait matting on mobile devices.
no code implementations • 16 Nov 2017 • Sungmin Rhee, Seokjun Seo, Sun Kim
In this paper, we proposed a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN).