12 papers
-Bench: A Multilingual Machine Unlearning Benchmark
Kyomin Hwang, Hyeonjin Kim, Hyunho Lee +3
Undesired information such as harmful content and private data propagates through Multilingual Large Language Models (LLMs) via direct training and indirect cross-linguistic spread…
LandingAgent: A Reference-Annotated Dataset and Agentic Generation Framework for Landing Pages
Injun Baek, HyeongSeok Lee, Yearim Kim +2
Landing pages are goal-oriented web interfaces that must communicate a target-specific value proposition while organizing information flow, visual hierarchy, and calls to action (C…
When Saying No Makes Better Videos: Designing Dual Gatekeeping for Pedagogically Grounded AI Content Creation
Yearim Kim, Injun Baek, Nojun Kwak
To prevent the adoption of aesthetically polished but pedagogically flawed AI content, we study a video authoring pipeline featuring two layers of structured refusal. The first lay…
From Local to Global to Mechanistic: An iERF-Centered Unified Framework for Interpreting Vision Models
Yearim Kim, Sangyu Han, Nojun Kwak
Modern vision models achieve remarkable accuracy, but explaining where evidence arises, what the model encodes, and how internal computations assemble that evidence remains fragmen…
VDPP: Video Depth Post-Processing for Speed and Scalability
Daewon Yoon, Injun Baek, Sangyu Han +2
Video depth estimation is essential for providing 3D scene structure in applications ranging from autonomous driving to mixed reality. Current end-to-end video depth models have es…
PedaCo-Gen: Scaffolding Pedagogical Agency in Human-AI Collaborative Video Authoring
Injun Baek, Yearim Kim, Nojun Kwak
While advancements in Text-to-Video (T2V) generative AI offer a promising path toward democratizing content creation, current models are often optimized for visual fidelity rather…
Causal Interpretation of Sparse Autoencoder Features in Vision
Sangyu Han, Yearim Kim, Nojun Kwak
Understanding what sparse auto-encoder (SAE) features in vision transformers truly represent is usually done by inspecting the patches where a feature's activation is highest. Howe…
Bi-ICE: An Inner Interpretable Framework for Image Classification via Bi-directional Interactions between Concept and Input Embeddings
Jinyung Hong, Yearim Kim, Keun Hee Park +3
Inner interpretability is a promising field aiming to uncover the internal mechanisms of AI systems through scalable, automated methods. While significant research has been conduct…
Decompose the model: Mechanistic interpretability in image models with Generalized Integrated Gradients (GIG)
Yearim Kim, Sangyu Han, Sangbum Han +1
In the field of eXplainable AI (XAI) in language models, the progression from local explanations of individual decisions to global explanations with high-level concepts has laid th…
Respect the model: Fine-grained and Robust Explanation with Sharing Ratio Decomposition
Sangyu Han, Yearim Kim, Nojun Kwak
The truthfulness of existing explanation methods in authentically elucidating the underlying model's decision-making process has been questioned. Existing methods have deviated fro…
Any-Way Meta Learning
Junhoo Lee, Yearim Kim, Hyunho Lee +1
Although meta-learning seems promising performance in the realm of rapid adaptability, it is constrained by fixed cardinality. When faced with tasks of varying cardinalities that w…
Fast Sun-aligned Outdoor Scene Relighting based on TensoRF
Yeonjin Chang, Yearim Kim, Seunghyeon Seo +2
In this work, we introduce our method of outdoor scene relighting for Neural Radiance Fields (NeRF) named Sun-aligned Relighting TensoRF (SR-TensoRF). SR-TensoRF offers a lightweig…