Search Results for author: Minki Jeong

Found 9 papers, 4 papers with code

MAC-DO: Charge Based Multi-Bit Analog In-Memory Accelerator Compatible with DRAM Using Output Stationary Mapping

no code implementations16 Jul 2022 Minki Jeong, Wanyeong Jung

As the main memory is usually DRAM in many systems, a highly parallel multiply-accumulate (MAC) array within the DRAM can maximize the benefit of AiM by reducing both the distance and amount of data movement between the processor and the main memory.

speech-recognition Speech Recognition +2

Explore-And-Match: Bridging Proposal-Based and Proposal-Free With Transformer for Sentence Grounding in Videos

1 code implementation25 Jan 2022 Sangmin Woo, Jinyoung Park, Inyong Koo, Sumin Lee, Minki Jeong, Changick Kim

To our surprise, we found that training schedule shows divide-and-conquer-like pattern: time segments are first diversified regardless of the target, then coupled with each target, and fine-tuned to the target again.

Natural Language Queries Temporal Localization +1

Improving Few-shot Learning with Weakly-supervised Object Localization

no code implementations25 May 2021 Inyong Koo, Minki Jeong, Changick Kim

In this work, we propose a novel framework that generates class representations by extracting features from class-relevant regions of the images.

Few-Shot Learning Metric Learning +1

Few-shot Open-set Recognition by Transformation Consistency

1 code implementation CVPR 2021 Minki Jeong, Seokeon Choi, Changick Kim

Based on the transformation consistency, our method measures the difference between the transformed prototypes and a modified prototype set.

Few-Shot Learning Open Set Learning

Meta Batch-Instance Normalization for Generalizable Person Re-Identification

1 code implementation CVPR 2021 Seokeon Choi, Taekyung Kim, Minki Jeong, Hyoungseob Park, Changick Kim

To this end, we combine learnable batch-instance normalization layers with meta-learning and investigate the challenging cases caused by both batch and instance normalization layers.

Data Augmentation Domain Generalization +2

Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation

no code implementations1 Aug 2019 Jaehoon Choi, Minki Jeong, Taekyung Kim, Changick Kim

To learn target discriminative representations, using pseudo-labels is a simple yet effective approach for unsupervised domain adaptation.

Clustering Semi-Supervised Image Classification +1

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