15 citations · 63 across the 13 of their papers we have counts for
13 papers
Rethinking Training for De-biasing Text-to-Image Generation: Unlocking the Potential of Stable Diffusion
Eunji Kim, Siwon Kim, Minjun Park +2
Recent advancements in text-to-image models, such as Stable Diffusion, show significant demographic biases. Existing de-biasing techniques rely heavily on additional training, whic…
Improving Visual Prompt Tuning for Self-supervised Vision Transformers
Seungryong Yoo, Eunji Kim, Dahuin Jung +2
Visual Prompt Tuning (VPT) is an effective tuning method for adapting pretrained Vision Transformers (ViTs) to downstream tasks. It leverages extra learnable tokens, known as promp…
Probabilistic Concept Bottleneck Models
Eunji Kim, Dahuin Jung, Sangha Park +2
Interpretable models are designed to make decisions in a human-interpretable manner. Representatively, Concept Bottleneck Models (CBM) follow a two-step process of concept predicti…
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization
Jungbeom Lee, Eunji Kim, Jisoo Mok +1
Obtaining accurate pixel-level localization from class labels is a crucial process in weakly supervised semantic segmentation and object localization. Attribution maps from a train…
Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization
Eunji Kim, Siwon Kim, Jungbeom Lee +2
Weakly supervised object localization aims to find a target object region in a given image with only weak supervision, such as image-level labels. Most existing methods use a class…
Weakly Supervised Semantic Segmentation using Out-of-Distribution Data
Jungbeom Lee, Seong Joon Oh, Sangdoo Yun +3
Weakly supervised semantic segmentation (WSSS) methods are often built on pixel-level localization maps obtained from a classifier. However, training on class labels only, classifi…
Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images
Sang-gil Lee, Eunji Kim, Jae Seok Bae +2
The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosi…
XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations
Eunji Kim, Siwon Kim, Minji Seo +1
Automated diagnosis using deep neural networks in chest radiography can help radiologists detect life-threatening diseases. However, existing methods only provide predictions witho…
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation
Jungbeom Lee, Eunji Kim, Sungroh Yoon
Weakly supervised semantic segmentation produces a pixel-level localization from a classifier, but it is likely to restrict its focus to a small discriminative region of the target…
Interpretation of NLP models through input marginalization
Siwon Kim, Jihun Yi, Eunji Kim +1
To demystify the "black box" property of deep neural networks for natural language processing (NLP), several methods have been proposed to interpret their predictions by measuring…
Information-Theoretic Visual Explanation for Black-Box Classifiers
Jihun Yi, Eunji Kim, Siwon Kim +1
In this work, we attempt to explain the prediction of any black-box classifier from an information-theoretic perspective. For each input feature, we compare the classifier outputs…
Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation
Jungbeom Lee, Eunji Kim, Sungmin Lee +2
When a deep neural network is trained on data with only image-level labeling, the regions activated in each image tend to identify only a small region of the target object. We prop…
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference
Jungbeom Lee, Eunji Kim, Sungmin Lee +2
The main obstacle to weakly supervised semantic image segmentation is the difficulty of obtaining pixel-level information from coarse image-level annotations. Most methods based on…