32 citations · 72 across the 7 of their papers we have counts for
7 papers
Rethinking Visual Information Processing in Multimodal LLMs
Dongwan Kim, Viresh Ranjan, Takashi Nagata +2
Despite the remarkable success of the LLaVA architecture for vision-language tasks, its design inherently struggles to effectively integrate visual features due to the inherent mis…
Revisiting Machine Unlearning with Dimensional Alignment
Seonguk Seo, Dongwan Kim, Bohyung Han
Machine unlearning, an emerging research topic focusing on compliance with data privacy regulations, enables trained models to remove the information learned from specific data. Wh…
On the Stability-Plasticity Dilemma of Class-Incremental Learning
Dongwan Kim, Bohyung Han
A primary goal of class-incremental learning is to strike a balance between stability and plasticity, where models should be both stable enough to retain knowledge learned from pre…
Learning Semantic Segmentation from Multiple Datasets with Label Shifts
Dongwan Kim, Yi-Hsuan Tsai, Yumin Suh +4
With increasing applications of semantic segmentation, numerous datasets have been proposed in the past few years. Yet labeling remains expensive, thus, it is desirable to jointly…
Learning Debiased and Disentangled Representations for Semantic Segmentation
Sanghyeok Chu, Dongwan Kim, Bohyung Han
Deep neural networks are susceptible to learn biased models with entangled feature representations, which may lead to subpar performances on various downstream tasks. This is parti…
Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation
Seungmin Lee, Dongwan Kim, Namil Kim +1
Recent works on domain adaptation exploit adversarial training to obtain domain-invariant feature representations from the joint learning of feature extractor and domain discrimina…
Learning to Optimize Domain Specific Normalization for Domain Generalization
Seonguk Seo, Yumin Suh, Dongwan Kim +3
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to in…