91 citations · 361 across the 26 of their papers we have counts for
7 papers · 1 filter
Adaptive Shortcut Debiasing for Online Continual Learning
Doyoung Kim, Dongmin Park, Yooju Shin +3
We propose a novel framework DropTop that suppresses the shortcut bias in online continual learning (OCL) while being adaptive to the varying degree of the shortcut bias incurred b…
Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise
Hwanjun Song, Minseok Kim, Jae-Gil Lee
Multi-label classification poses challenges due to imbalanced and noisy labels in training data. We propose a unified data augmentation method, named BalanceMix, to address these c…
Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy
Dongmin Park, Seola Choi, Doyoung Kim +2
Data pruning, which aims to downsize a large training set into a small informative subset, is crucial for reducing the enormous computational costs of modern deep learning. Though…
One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning
Doyoung Kim, Susik Yoon, Dongmin Park +4
In real-world continual learning (CL) scenarios, tasks often exhibit intricate and unpredictable semantic shifts, posing challenges for fixed prompt management strategies which are…
Prompt-Guided Transformers for End-to-End Open-Vocabulary Object Detection
Hwanjun Song, Jihwan Bang
Prompt-OVD is an efficient and effective framework for open-vocabulary object detection that utilizes class embeddings from CLIP as prompts, guiding the Transformer decoder to dete…
Re-thinking Federated Active Learning based on Inter-class Diversity
SangMook Kim, Sangmin Bae, Hwanjun Song +1
Although federated learning has made awe-inspiring advances, most studies have assumed that the client's data are fully labeled. However, in a real-world scenario, every client may…
Q-HyViT: Post-Training Quantization of Hybrid Vision Transformers with Bridge Block Reconstruction for IoT Systems
Jemin Lee, Yongin Kwon, Sihyeong Park +3
Recently, vision transformers (ViTs) have superseded convolutional neural networks in numerous applications, including classification, detection, and segmentation. However, the hig…