8 citations · 18 across the 12 of their papers we have counts for
14 papers
LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance
Jae Myung Kim, Stephan Alaniz, Cordelia Schmid +1
Despite recent advances in text-to-image generation, using synthetically generated data seldom brings a significant boost in performance for supervised learning. Oftentimes, synthe…
Feasibility with Language Models for Open-World Compositional Zero-Shot Learning
Jae Myung Kim, Stephan Alaniz, Cordelia Schmid +1
Humans can easily tell if an attribute (also called state) is realistic, i.e., feasible, for an object, e.g. fire can be hot, but it cannot be wet. In Open-World Compositional Zero…
Does Feasibility Matter? Understanding the Impact of Feasibility on Synthetic Training Data
Yiwen Liu, Jessica Bader, Jae Myung Kim
With the development of photorealistic diffusion models, models trained in part or fully on synthetic data achieve progressively better results. However, diffusion models still rou…
DataDream: Few-shot Guided Dataset Generation
Jae Myung Kim, Jessica Bader, Stephan Alaniz +2
While text-to-image diffusion models have been shown to achieve state-of-the-art results in image synthesis, they have yet to prove their effectiveness in downstream applications.…
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models
Nishad Singhi, Jae Myung Kim, Karsten Roth +1
Concept Bottleneck Models (CBMs) ground image classification on human-understandable concepts to allow for interpretable model decisions. Crucially, the CBM design inherently allow…
Waffling around for Performance: Visual Classification with Random Words and Broad Concepts
Karsten Roth, Jae Myung Kim, A. Sophia Koepke +3
The visual classification performance of vision-language models such as CLIP has been shown to benefit from additional semantic knowledge from large language models (LLMs) such as…
Exposing and Mitigating Spurious Correlations for Cross-Modal Retrieval
Jae Myung Kim, A. Sophia Koepke, Cordelia Schmid +1
Cross-modal retrieval methods are the preferred tool to search databases for the text that best matches a query image and vice versa. However, image-text retrieval models commonly…
Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification
Youngwook Kim, Jae Myung Kim, Jieun Jeong +3
Due to the expensive costs of collecting labels in multi-label classification datasets, partially annotated multi-label classification has become an emerging field in computer visi…
Likelihood Annealing: Fast Calibrated Uncertainty for Regression
Uddeshya Upadhyay, Jae Myung Kim, Cordelia Schmidt +2
Recent advances in deep learning have shown that uncertainty estimation is becoming increasingly important in applications such as medical imaging, natural language processing, and…
Large Loss Matters in Weakly Supervised Multi-Label Classification
Youngwook Kim, Jae Myung Kim, Zeynep Akata +1
Weakly supervised multi-label classification (WSML) task, which is to learn a multi-label classification using partially observed labels per image, is becoming increasingly importa…
Keep CALM and Improve Visual Feature Attribution
Jae Myung Kim, Junsuk Choe, Zeynep Akata +1
The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks. Its simplicity and effectiveness have led to wide applicati…
REST: Performance Improvement of a Black Box Model via RL-based Spatial Transformation
Jae Myung Kim, Hyungjin Kim, Chanwoo Park +1
In recent years, deep neural networks (DNN) have become a highly active area of research, and shown remarkable achievements on a variety of computer vision tasks. DNNs, however, ar…
Exploring linearity of deep neural network trained QSM: QSMnet+
Woojin Jung, Jaeyeon Yoon, Joon Yul Choi +4
Recently, deep neural network-powered quantitative susceptibility mapping (QSM), QSMnet, successfully performed ill conditioned dipole inversion in QSM and generated high-quality s…
Sampling-based Bayesian Inference with gradient uncertainty
Chanwoo Park, Jae Myung Kim, Seok Hyeon Ha +1
Deep neural networks(NNs) have achieved impressive performance, often exceed human performance on many computer vision tasks. However, one of the most challenging issues that still…