Search Results for author: Jiwon Kim

Found 40 papers, 13 papers with code

D3T: Distinctive Dual-Domain Teacher Zigzagging Across RGB-Thermal Gap for Domain-Adaptive Object Detection

1 code implementation CVPR 2024 Dinh Phat Do, TaeHoon Kim, Jaemin Na, Jiwon Kim, Keonho Lee, Kyunghwan Cho, Wonjun Hwang

However, there are limited studies on adapting from the visible to the thermal domain, because the domain gap between the visible and thermal domains is much larger than expected, and traditional domain adaptation can not successfully facilitate learning in this situation.

Domain Adaptation object-detection +1

PANDAS: Prototype-based Novel Class Discovery and Detection

1 code implementation27 Feb 2024 Tyler L. Hayes, César R. de Souza, Namil Kim, Jiwon Kim, Riccardo Volpi, Diane Larlus

In this work, we look at ways to extend a detector trained for a set of base classes so it can i) spot the presence of novel classes, and ii) automatically enrich its repertoire to be able to detect those newly discovered classes together with the base ones.

Novel Class Discovery

SoK: Facial Deepfake Detectors

no code implementations9 Jan 2024 Binh M. Le, Jiwon Kim, Shahroz Tariq, Kristen Moore, Alsharif Abuadbba, Simon S. Woo

Our systematized analysis and experimentation lay the groundwork for a deeper understanding of deepfake detectors and their generalizability, paving the way for future research focused on creating detectors adept at countering various attack scenarios.

DeepFake Detection Face Swapping

Match me if you can: Semantic Correspondence Learning with Unpaired Images

no code implementations30 Nov 2023 Jiwon Kim, Byeongho Heo, Sangdoo Yun, Seungryong Kim, Dongyoon Han

Recent approaches for semantic correspondence have focused on obtaining high-quality correspondences using a complicated network, refining the ambiguous or noisy matching points.

Semantic correspondence

Deep Reinforcement Learning for Asset Allocation: Reward Clipping

no code implementations2 Jan 2023 Jiwon Kim, Moon-Ju Kang, KangHun Lee, HyungJun Moon, Bo-Kwan Jeon

Recently, there are many trials to apply reinforcement learning in asset allocation for earning more stable profits.

Portfolio Optimization reinforcement-learning +1

Trajectory Flow Map: Graph-based Approach to Analysing Temporal Evolution of Aggregated Traffic Flows in Large-scale Urban Networks

no code implementations6 Dec 2022 Jiwon Kim, Kai Zheng, Jonathan Corcoran, Sanghyung Ahn, Marty Papamanolis

First, we partition the entire network into a set of cells based on the spatial distribution of data points in individual trajectories, where the cells represent spatial regions between which aggregated traffic flows can be measured.

Graph Mining

A deep learning framework to generate realistic population and mobility data

no code implementations14 Nov 2022 Eren Arkangil, Mehmet Yildirimoglu, Jiwon Kim, Carlo Prato

Census and Household Travel Survey datasets are regularly collected from households and individuals and provide information on their daily travel behavior with demographic and economic characteristics.

Data Augmentation

ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization

1 code implementation18 Aug 2022 Jiwon Kim, Youngjo Min, Daehwan Kim, Gyuseong Lee, Junyoung Seo, Kwangrok Ryoo, Seungryong Kim

We present a novel semi-supervised learning framework that intelligently leverages the consistency regularization between the model's predictions from two strongly-augmented views of an image, weighted by a confidence of pseudo-label, dubbed ConMatch.

Pseudo Label

Estimating Link Flows in Road Networks with Synthetic Trajectory Data Generation: Reinforcement Learning-based Approaches

no code implementations26 Jun 2022 Miner Zhong, Jiwon Kim, Zuduo Zheng

To ensure the generated population vehicle trajectories are consistent with the observed traffic volume and trajectory data, two methods based on Inverse Reinforcement Learning and Constrained Reinforcement Learning are proposed.

Decision Making reinforcement-learning +1

Joint Learning of Feature Extraction and Cost Aggregation for Semantic Correspondence

no code implementations5 Apr 2022 Jiwon Kim, Youngjo Min, Mira Kim, Seungryong Kim

In this paper, we propose a novel framework for jointly learning feature extraction and cost aggregation for semantic correspondence.

Semantic correspondence

Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels

no code implementations CVPR 2022 Jiwon Kim, Kwangrok Ryoo, Junyoung Seo, Gyuseong Lee, Daehwan Kim, Hansang Cho, Seungryong Kim

In this paper, we present a simple, but effective solution for semantic correspondence that learns the networks in a semi-supervised manner by supplementing few ground-truth correspondences via utilization of a large amount of confident correspondences as pseudo-labels, called SemiMatch.

Data Augmentation Semantic correspondence +1

AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning

no code implementations25 Jan 2022 Jiwon Kim, Kwangrok Ryoo, Gyuseong Lee, Seokju Cho, Junyoung Seo, Daehwan Kim, Hansang Cho, Seungryong Kim

In this paper, we address this limitation with a novel SSL framework for aggregating pseudo labels, called AggMatch, which refines initial pseudo labels by using different confident instances.

Pseudo Label

A Computational Approach to Measure Empathy and Theory-of-Mind from Written Texts

1 code implementation26 Aug 2021 Yoon Kyung Lee, Inju Lee, Jae Eun Park, Yoonwon Jung, Jiwon Kim, Sowon Hahn

Sentences that successfully take the perspective of others (the highest ToM level) were the most difficult to predict.

Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach

no code implementations1 Aug 2021 Zhixiong Jin, Jiwon Kim, Hwasoo Yeo, Seongjin Choi

In many spatial trajectory-based applications, it is necessary to map raw trajectory data points onto road networks in digital maps, which is commonly referred to as a map-matching process.

Transfer Learning

Logit As Auxiliary Weak-supervision for More Reliable and Accurate Prediction

no code implementations1 Jan 2021 Duhyeon Bang, Yunho Jeon, Jin-Hwa Kim, Jiwon Kim, Hyunjung Shim

When a person identifies objects, he or she can think by associating objects to many classes and conclude by taking inter-class relations into account.

Data Augmentation

Associative Partial Domain Adaptation

no code implementations7 Aug 2020 Youngeun Kim, Sungeun Hong, Seunghan Yang, Sungil Kang, Yunho Jeon, Jiwon Kim

Our Associative Partial Domain Adaptation (APDA) utilizes intra-domain association to actively select out non-trivial anomaly samples in each source-private class that sample-level weighting cannot handle.

Partial Domain Adaptation

TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning

1 code implementation28 Jul 2020 Seongjin Choi, Jiwon Kim, Hwasoo Yeo

A generative model for urban vehicle trajectories can better generalize from training data by learning the underlying distribution of the training data and, thus, produce synthetic vehicle trajectories similar to real vehicle trajectories with limited observations.

Imitation Learning

Sample-based Regularization: A Transfer Learning Strategy Toward Better Generalization

no code implementations10 Jul 2020 Yunho Jeon, Yongseok Choi, Jaesun Park, Subin Yi, Dong-Yeon Cho, Jiwon Kim

However, this is likely to restrict the potential of the target model and some transferred knowledge from the source can interfere with the training procedure.

Transfer Learning

Restore from Restored: Single Image Denoising with Pseudo Clean Image

no code implementations9 Mar 2020 Seunghwan Lee, Dongkyu Lee, Donghyeon Cho, Jiwon Kim, Tae Hyun Kim

However, these methods have limitations in using internal information available in a given test image.

Image Denoising

Restore from Restored: Video Restoration with Pseudo Clean Video

no code implementations CVPR 2021 Seunghwan Lee, Donghyeon Cho, Jiwon Kim, Tae Hyun Kim

We analyze the restoration performance of the fine-tuned video denoising networks with the proposed self-supervision-based learning algorithm, and demonstrate that the FCN can utilize recurring patches without requiring accurate registration among adjacent frames.

Denoising Optical Flow Estimation +3

Self-Supervised Fast Adaptation for Denoising via Meta-Learning

no code implementations9 Jan 2020 Seunghwan Lee, Donghyeon Cho, Jiwon Kim, Tae Hyun Kim

Under certain statistical assumptions of noise, recent self-supervised approaches for denoising have been introduced to learn network parameters without true clean images, and these methods can restore an image by exploiting information available from the given input (i. e., internal statistics) at test time.

Denoising Meta-Learning

Fast Adaptation to Super-Resolution Networks via Meta-Learning

1 code implementation ECCV 2020 Seobin Park, Jinsu Yoo, Donghyeon Cho, Jiwon Kim, Tae Hyun Kim

In the training stage, we train the network via meta-learning; thus, the network can quickly adapt to any input image at test time.

Meta-Learning Super-Resolution

Robust Nucleus Detection from Microscopical Image with Partially Labeled Exemplars

1 code implementation23 Jul 2019 Linqing Feng, Jun Ho Song, Jiwon Kim, Soomin Jeong, Jin Sung Park, Jinhyun Kim

Quantitative analysis of cell nuclei in microscopic images is an essential yet still challenging source of biological and pathological information.

Region Proposal

Key Instance Selection for Unsupervised Video Object Segmentation

no code implementations18 Jun 2019 Donghyeon Cho, Sungeun Hong, Sungil Kang, Jiwon Kim

After M-th frame, we select K IDs based on video saliency and frequency of appearance; then only these key IDs are tracked through the remaining frames.

Object Segmentation +3

Discriminative Few-Shot Learning Based on Directional Statistics

no code implementations5 Jun 2019 Junyoung Park, Subin Yi, Yongseok Choi, Dong-Yeon Cho, Jiwon Kim

Metric-based few-shot learning methods try to overcome the difficulty due to the lack of training examples by learning embedding to make comparison easy.

Few-Shot Learning General Classification

Attention-based Recurrent Neural Network for Urban Vehicle Trajectory Prediction

no code implementations18 Dec 2018 Seongjin Choi, Jiwon Kim, Hwasoo Yeo

With the increasing deployment of diverse positioning devices and location-based services, a huge amount of spatial and temporal information has been collected and accumulated as trajectory data.

Trajectory Prediction

Learning to Embed Semantic Correspondence for Natural Language Understanding

no code implementations CONLL 2018 Sangkeun Jung, Jinsik Lee, Jiwon Kim

While learning embedding models has yielded fruitful results in several NLP subfields, most notably Word2Vec, embedding correspondence has relatively not been well explored especially in the context of natural language understanding (NLU), a task that typically extracts structured semantic knowledge from a text.

Natural Language Understanding Re-Ranking +2

Meta Continual Learning

no code implementations11 Jun 2018 Risto Vuorio, Dong-Yeon Cho, Daejoong Kim, Jiwon Kim

This ability is limited in the current deep neural networks by a problem called catastrophic forgetting, where training on new tasks tends to severely degrade performance on previous tasks.

Continual Learning

Auto-Meta: Automated Gradient Based Meta Learner Search

no code implementations11 Jun 2018 Jaehong Kim, Sangyeul Lee, Sungwan Kim, Moonsu Cha, Jung Kwon Lee, Youngduck Choi, Yongseok Choi, Dong-Yeon Cho, Jiwon Kim

Fully automating machine learning pipelines is one of the key challenges of current artificial intelligence research, since practical machine learning often requires costly and time-consuming human-powered processes such as model design, algorithm development, and hyperparameter tuning.

BIG-bench Machine Learning Meta-Learning +1

Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks

no code implementations31 Jul 2017 Taeksoo Kim, Byoungjip Kim, Moonsu Cha, Jiwon Kim

To address the issue, we propose an unsupervised method to learn to transfer visual attribute.

Attribute

End-to-end Learning of Image based Lane-Change Decision

1 code implementation26 Jun 2017 Seong-Gyun Jeong, Jiwon Kim, Sujung Kim, Jaesik Min

We propose an image based end-to-end learning framework that helps lane-change decisions for human drivers and autonomous vehicles.

Autonomous Vehicles object-detection +1

Continual Learning with Deep Generative Replay

5 code implementations NeurIPS 2017 Hanul Shin, Jung Kwon Lee, Jaehong Kim, Jiwon Kim

Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting.

Class Incremental Learning General Classification +2

Learning to Discover Cross-Domain Relations with Generative Adversarial Networks

19 code implementations ICML 2017 Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, Jiwon Kim

While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations.

Dual Memory Architectures for Fast Deep Learning of Stream Data via an Online-Incremental-Transfer Strategy

no code implementations15 Jun 2015 Sang-Woo Lee, Min-Oh Heo, Jiwon Kim, Jeonghee Kim, Byoung-Tak Zhang

The proposed architecture consists of deep representation learners and fast learnable shallow kernel networks, both of which synergize to track the information of new data.

Transfer Learning

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