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20202025
most citedClass-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation

11 citations · 11 across the 5 of their papers we have counts for

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7 papers · 1 filter

cs.CV2025

Emergence of Text Readability in Vision Language Models

Jaeyoo Park, Sanghyuk Chun, Wonjae Kim +2

We investigate how the ability to recognize textual content within images emerges during the training of Vision-Language Models (VLMs). Our analysis reveals a critical phenomenon:…

cs.CV2024

Hierarchical Visual Feature Aggregation for OCR-Free Document Understanding

Jaeyoo Park, Jin Young Choi, Jeonghyung Park +1

We present a novel OCR-free document understanding framework based on pretrained Multimodal Large Language Models (MLLMs). Our approach employs multi-scale visual features to effec…

cs.CV2023

Multi-Modal Representation Learning with Text-Driven Soft Masks

Jaeyoo Park, Bohyung Han

We propose a visual-linguistic representation learning approach within a self-supervised learning framework by introducing a new operation, loss, and data augmentation strategy. Fi…

cs.CV2023

Cross-Class Feature Augmentation for Class Incremental Learning

Taehoon Kim, Jaeyoo Park, Bohyung Han

We propose a novel class incremental learning approach by incorporating a feature augmentation technique motivated by adversarial attacks. We employ a classifier learned in the pas…

cs.CV2022

Class-Incremental Learning for Action Recognition in Videos

Jaeyoo Park, Minsoo Kang, Bohyung Han

We tackle catastrophic forgetting problem in the context of class-incremental learning for video recognition, which has not been explored actively despite the popularity of continu…

cs.CV2022

Learning to Adapt to Unseen Abnormal Activities under Weak Supervision

Jaeyoo Park, Junha Kim, Bohyung Han

We present a meta-learning framework for weakly supervised anomaly detection in videos, where the detector learns to adapt to unseen types of abnormal activities effectively when o…