5 citations · 9 across the 8 of their papers we have counts for
9 papers
SSDi8: Accurate and Efficient 8-bit Quantization for State Space Duality
Hyunwoo Kim, Byoungchan Ko, Minseok Kang +5
Recent advances in sequence modeling have highlighted Mamba as a state space architecture offering efficient long-range dependency modeling and providing a viable alternative to Tr…
Know "No" Better: A Data-Driven Approach for Enhancing Negation Awareness in CLIP
Junsung Park, Jungbeom Lee, Jongyoon Song +3
While CLIP has significantly advanced multimodal understanding by bridging vision and language, the inability to grasp negation - such as failing to differentiate concepts like "pa…
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…
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…
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…
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…
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…
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…
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…