46 citations · 279 across the 39 of their papers we have counts for
9 papers · 1 filter
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination
Minkyung Kim, Jongmin Yu, Junsik Kim +2
Most deep anomaly detection models are based on learning normality from datasets due to the difficulty of defining abnormality by its diverse and inconsistent nature. Therefore, it…
The Devil in the Details: Simple and Effective Optical Flow Synthetic Data Generation
Kwon Byung-Ki, Kim Sung-Bin, Tae-Hyun Oh
Recent work on dense optical flow has shown significant progress, primarily in a supervised learning manner requiring a large amount of labeled data. Due to the expensiveness of ob…
TextManiA: Enriching Visual Feature by Text-driven Manifold Augmentation
Moon Ye-Bin, Jisoo Kim, Hongyeob Kim +2
We propose TextManiA, a text-driven manifold augmentation method that semantically enriches visual feature spaces, regardless of class distribution. TextManiA augments visual data…
Automatic Tuning of Loss Trade-offs without Hyper-parameter Search in End-to-End Zero-Shot Speech Synthesis
Seongyeon Park, Bohyung Kim, Tae-hyun Oh
Recently, zero-shot TTS and VC methods have gained attention due to their practicality of being able to generate voices even unseen during training. Among these methods, zero-shot…
Prefix tuning for automated audio captioning
Minkyu Kim, Kim Sung-Bin, Tae-Hyun Oh
Audio captioning aims to generate text descriptions from environmental sounds. One challenge of audio captioning is the difficulty of the generalization due to the lack of audio-te…
Sound to Visual Scene Generation by Audio-to-Visual Latent Alignment
Kim Sung-Bin, Arda Senocak, Hyunwoo Ha +2
How does audio describe the world around us? In this paper, we propose a method for generating an image of a scene from sound. Our method addresses the challenges of dealing with t…
Unsupervised Pre-Training For Data-Efficient Text-to-Speech On Low Resource Languages
Seongyeon Park, Myungseo Song, Bohyung Kim +1
Neural text-to-speech (TTS) models can synthesize natural human speech when trained on large amounts of transcribed speech. However, collecting such large-scale transcribed data is…
ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation
Moon Ye-Bin, Dongmin Choi, Yongjin Kwon +2
We address a weakly-supervised low-shot instance segmentation, an annotation-efficient training method to deal with novel classes effectively. Since it is an under-explored problem…
Semi-Supervised Image Captioning by Adversarially Propagating Labeled Data
Dong-Jin Kim, Tae-Hyun Oh, Jinsoo Choi +1
We present a novel data-efficient semi-supervised framework to improve the generalization of image captioning models. Constructing a large-scale labeled image captioning dataset is…