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20242026
most citedEntropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors

5 citations · 9 across the 8 of their papers we have counts for

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7 papers · 1 filter

cs.CV2024★ 1 cited

Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization

Sanghyeob Song, Jaihyun Lew, Hyemi Jang +1

Estimating the homography between two images is crucial for mid- or high-level vision tasks, such as image stitching and fusion. However, using supervised learning methods is often…

cs.CV2024

Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection

Saehyung Lee, Jisoo Mok, Sangha Park +3

In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully par…

cs.CV2024

Normality Addition via Normality Detection in Industrial Image Anomaly Detection Models

Jihun Yi, Dahuin Jung, Sungroh Yoon

The task of image anomaly detection (IAD) aims to identify deviations from normality in image data. These anomalies are patterns that deviate significantly from what the IAD model…

cs.CV2024

Disentangled Motion Modeling for Video Frame Interpolation

Jaihyun Lew, Jooyoung Choi, Chaehun Shin +2

Video Frame Interpolation (VFI) aims to synthesize intermediate frames between existing frames to enhance visual smoothness and quality. Beyond the conventional methods based on th…

cs.CV2024★ 3 cited

SF(DA): Source-free Domain Adaptation Through the Lens of Data Augmentation

Uiwon Hwang, Jonghyun Lee, Juhyeon Shin +1

In the face of the deep learning model's vulnerability to domain shift, source-free domain adaptation (SFDA) methods have been proposed to adapt models to new, unseen target domain…

cs.CV2024★ 5 cited

Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors

Jonghyun Lee, Dahuin Jung, Saehyung Lee +4

Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online…

cs.CV2024

Efficient Diffusion-Driven Corruption Editor for Test-Time Adaptation

Yeongtak Oh, Jonghyun Lee, Jooyoung Choi +3

Test-time adaptation (TTA) addresses the unforeseen distribution shifts occurring during test time. In TTA, performance, memory consumption, and time consumption are crucial consid…