17 citations · 39 across the 26 of their papers we have counts for
29 papers
RoME: Robust Mixture of Low-Rank Experts against Multiple Adversarial Perturbations
Woo Jae Kim, Kyle Min, Suhyeon Ha +2
Multi-perturbation adversarial training (MAT) aims to achieve robustness against multiple perturbations but suffers from robustness trade-offs between different threats. T…
Same Concept, Different Directions: Cross-Modal Feature Heterogeneity in Sparse Autoencoders
Chungpa Lee, Jihoon Kwon, Kyle Min +1
Vision-language models map images and text into a joint embedding space. However, these embeddings often entangle multiple semantic features, which limits their interpretability an…
ProCompNav: Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries
Junhyuk Kwon, Seungjoon Lee, Hyejin Park +2
Natural-language instance navigation becomes challenging when the initial user request does not uniquely specify the target instance. A practical agent should reduce the user's bur…
Sink-Token-Aware Pruning for Fine-Grained Video Understanding in Efficient Video LLMs
Kibum Kim, Jiwan Kim, Kyle Min +4
Video Large Language Models (Video LLMs) incur high inference latency due to a large number of visual tokens provided to LLMs. To address this, training-free visual token pruning h…
EraseFlow: Learning Concept Erasure Policies via GFlowNet-Driven Alignment
Abhiram Kusumba, Maitreya Patel, Kyle Min +3
Erasing harmful or proprietary concepts from powerful text to image generators is an emerging safety requirement, yet current "concept erasure" techniques either collapse image qua…
Enhancing Compositional Reasoning in CLIP via Reconstruction and Alignment of Text Descriptions
Jihoon Kwon, Kyle Min, Jy-yong Sohn
Despite recent advances, vision-language models trained with standard contrastive objectives still struggle with compositional reasoning -- the ability to understand structured rel…