Search Results for author: Chang D. Yoo

Found 33 papers, 11 papers with code

Learning Imbalanced Datasets with Maximum Margin Loss

1 code implementation11 Jun 2022 Haeyong Kang, Thang Vu, Chang D. Yoo

A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the trained model tends to predict the majority of classes rather than the minority ones.

Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo

1 code implementation CVPR 2022 Chaoning Zhang, Kang Zhang, Trung X. Pham, Axi Niu, Zhinan Qiao, Chang D. Yoo, In So Kweon

Contrastive learning (CL) is widely known to require many negative samples, 65536 in MoCo for instance, for which the performance of a dictionary-free framework is often inferior because the negative sample size (NSS) is limited by its mini-batch size (MBS).

Contrastive Learning

Hindsight Goal Ranking on Replay Buffer for Sparse Reward Environment

1 code implementation28 Oct 2021 Tung M. Luu, Chang D. Yoo

The actual sampling for large TD error is performed in two steps: first, an episode is sampled from the relay buffer according to the average TD error of its experiences, and then, for the sampled episode, the hindsight goal leading to larger TD error is sampled with higher probability from future visited states.

Active Learning: Sampling in the Least Probable Disagreement Region

no code implementations29 Sep 2021 Seong Jin Cho, Gwangsu Kim, Chang D. Yoo

This strategy is valid only when the sample's "closeness" to the decision boundary can be estimated.

Active Learning

Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold

1 code implementation23 Sep 2021 Junghyun Lee, Gwangsu Kim, Matt Olfat, Mark Hasegawa-Johnson, Chang D. Yoo

This paper defines fair principal component analysis (PCA) as minimizing the maximum mean discrepancy (MMD) between dimensionality-reduced conditional distributions of different protected classes.

Fairness

Self-supervised Learning with Local Attention-Aware Feature

no code implementations1 Aug 2021 Trung X. Pham, Rusty John Lloyd Mina, Dias Issa, Chang D. Yoo

In this work, we propose a novel methodology for self-supervised learning for generating global and local attention-aware visual features.

Self-Supervised Learning

Structured Co-reference Graph Attention for Video-grounded Dialogue

no code implementations24 Mar 2021 Junyeong Kim, Sunjae Yoon, Dahyun Kim, Chang D. Yoo

A video-grounded dialogue system referred to as the Structured Co-reference Graph Attention (SCGA) is presented for decoding the answer sequence to a question regarding a given video while keeping track of the dialogue context.

Graph Attention

Robust MAML: Prioritization task buffer with adaptive learning process for model-agnostic meta-learning

no code implementations15 Mar 2021 Thanh Nguyen, Tung Luu, Trung Pham, Sanzhar Rakhimkul, Chang D. Yoo

Model agnostic meta-learning (MAML) is a popular state-of-the-art meta-learning algorithm that provides good weight initialization of a model given a variety of learning tasks.

Meta-Learning Meta Reinforcement Learning

Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics Model

1 code implementation15 Mar 2021 Thanh Nguyen, Tung M. Luu, Thang Vu, Chang D. Yoo

Developing an agent in reinforcement learning (RL) that is capable of performing complex control tasks directly from high-dimensional observation such as raw pixels is yet a challenge as efforts are made towards improving sample efficiency and generalization.

Contrastive Learning Data Augmentation +2

Semantic Grouping Network for Video Captioning

1 code implementation1 Feb 2021 Hobin Ryu, Sunghun Kang, Haeyong Kang, Chang D. Yoo

This paper considers a video caption generating network referred to as Semantic Grouping Network (SGN) that attempts (1) to group video frames with discriminating word phrases of partially decoded caption and then (2) to decode those semantically aligned groups in predicting the next word.

Video Captioning

Least Probable Disagreement Region for Active Learning

no code implementations1 Jan 2021 Seong Jin Cho, Gwangsu Kim, Chang D. Yoo

Active learning strategy to query unlabeled samples nearer the estimated decision boundary at each step has been known to be effective when the distance from the sample data to the decision boundary can be explicitly evaluated; however, in numerous cases in machine learning, especially when it involves deep learning, conventional distance such as the $\ell_p$ from sample to decision boundary is not readily measurable.

Active Learning

SCNet: Training Inference Sample Consistency for Instance Segmentation

2 code implementations18 Dec 2020 Thang Vu, Haeyong Kang, Chang D. Yoo

This paper proposes an architecture referred to as Sample Consistency Network (SCNet) to ensure that the IoU distribution of the samples at training time is close to that at inference time.

Instance Segmentation object-detection +2

VLANet: Video-Language Alignment Network for Weakly-Supervised Video Moment Retrieval

no code implementations ECCV 2020 Minuk Ma, Sunjae Yoon, Junyeong Kim, Young-Joon Lee, Sunghun Kang, Chang D. Yoo

This paper explores methods for performing VMR in a weakly-supervised manner (wVMR): training is performed without temporal moment labels but only with the text query that describes a segment of the video.

Contrastive Learning Moment Retrieval

Modality Shifting Attention Network for Multi-modal Video Question Answering

no code implementations CVPR 2020 Junyeong Kim, Minuk Ma, Trung Pham, Kyung-Su Kim, Chang D. Yoo

To this end, MSAN is based on (1) the moment proposal network (MPN) that attempts to locate the most appropriate temporal moment from each of the modalities, and also on (2) the heterogeneous reasoning network (HRN) that predicts the answer using an attention mechanism on both modalities.

Question Answering Temporal Localization +1

Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution

2 code implementations NeurIPS 2019 Thang Vu, Hyunjun Jang, Trung X. Pham, Chang D. Yoo

This paper considers an architecture referred to as Cascade Region Proposal Network (Cascade RPN) for improving the region-proposal quality and detection performance by \textit{systematically} addressing the limitation of the conventional RPN that \textit{heuristically defines} the anchors and \textit{aligns} the features to the anchors.

Ranked #154 on Object Detection on COCO test-dev (using extra training data)

Object Detection Region Proposal

Gaining Extra Supervision via Multi-task learning for Multi-Modal Video Question Answering

no code implementations28 May 2019 Junyeong Kim, Minuk Ma, Kyung-Su Kim, Sungjin Kim, Chang D. Yoo

This paper proposes a method to gain extra supervision via multi-task learning for multi-modal video question answering.

Inductive Bias Metric Learning +3

Edge-labeling Graph Neural Network for Few-shot Learning

3 code implementations CVPR 2019 Jongmin Kim, Taesup Kim, Sungwoong Kim, Chang D. Yoo

In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning.

Few-Shot Image Classification

Learning to Augment Influential Data

no code implementations ICLR 2019 Donghoon Lee, Chang D. Yoo

The differentiable augmentation model and reformulation of the influence function allow the parameters of the augmented model to be directly updated by backpropagation to minimize the validation loss.

Data Augmentation

Progressive Attention Memory Network for Movie Story Question Answering

no code implementations CVPR 2019 Junyeong Kim, Minuk Ma, Kyung-Su Kim, Sungjin Kim, Chang D. Yoo

To overcome these challenges, PAMN involves three main features: (1) progressive attention mechanism that utilizes cues from both question and answer to progressively prune out irrelevant temporal parts in memory, (2) dynamic modality fusion that adaptively determines the contribution of each modality for answering the current question, and (3) belief correction answering scheme that successively corrects the prediction score on each candidate answer.

Question Answering Video Story QA +1

Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks

1 code implementation ECCV2018 2018 Thang Vu, Cao V. Nguyen, Trung X. Pham, Tung M. Luu, Chang D. Yoo

This paper considers a convolutional neural network for image quality enhancement referred to as the fast and efficient quality enhancement (FEQE) that can be trained for either image super-resolution or image enhancement to provide accurate yet visually pleasing images on mobile devices by addressing the following three main issues.

Image Enhancement Image Super-Resolution

Pivot Correlational Neural Network for Multimodal Video Categorization

no code implementations ECCV 2018 Sunghun Kang, Junyeong Kim, Hyun-Soo Choi, Sungjin Kim, Chang D. Yoo

The architecture is trained to maximizes the correlation between the hidden states as well as the predictions of the modal-agnostic pivot stream and modal-specific stream in the network.

A Resizable Mini-batch Gradient Descent based on a Multi-Armed Bandit

no code implementations ICLR 2019 Seong Jin Cho, Sunghun Kang, Chang D. Yoo

Determining the appropriate batch size for mini-batch gradient descent is always time consuming as it often relies on grid search.

Meta-Learning via Feature-Label Memory Network

no code implementations19 Oct 2017 Dawit Mureja, Hyunsin Park, Chang D. Yoo

The feature memory is used to store the features of input data samples and the label memory stores their labels.

Meta-Learning

Early Improving Recurrent Elastic Highway Network

no code implementations14 Aug 2017 Hyunsin Park, Chang D. Yoo

Expanding on the idea of adaptive computation time (ACT), with the use of an elastic gate in the form of a rectified exponentially decreasing function taking on as arguments as previous hidden state and input, the proposed model is able to evaluate the appropriate recurrent depth for each input.

Human Activity Recognition Language Modelling

Face Alignment Using Cascade Gaussian Process Regression Trees

no code implementations CVPR 2015 Donghoon Lee, Hyunsin Park, Chang D. Yoo

Without increasing prediction time, the prediction of cGPRT can be performed in the same framework as the cascade regression trees (CRT) but with better generalization.

Face Alignment

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