3 citations · 5 across the 11 of their papers we have counts for
11 papers
When Are Reasoning-Based Guardrails Not Efficient? ResponseGuard: A Fast Vision-Language Guard for Real-Time Moderation
Dongbin Na
A vision-language AI assistant returns its answer as a stream of generated tokens. Therefore, a safety guard that watches that answer has to keep up with the stream and stop a harm…
Do Safety Guardrails Need to Reason? LeanGuard: A Fast and Light Approach for Robust Moderation
Dongbin Na
In order to screen a prompt or a response, the recent guardrail methods generate a chain-of-thought (CoT) before they issue a verdict. This design follows a common belief that step…
Binary Tracking for Spatial QA and Navigation with Open Vision-Language Models
Dongbin Na, Chanwoo Kim, Soonbin Rho +3
This work addresses spatial question answering for service robots traversing long egocentric routes. Given a query such as "where can I find a dry cleaner on the way back home?", t…
Semantic Flip: Synthetic OOD Generation for Robust Refusal in Embodied Question Answering and Spatial Localization
Dongbin Na, Chanwoo Kim, Giyun Choi +1
Detecting unanswerable user queries remains essential for the reliable deployment of real-world embodied agents. However, modern vision-language models (VLMs) often generate overly…
Distribution-Level Feature Distancing for Machine Unlearning: Towards a Better Trade-off Between Model Utility and Forgetting
Dasol Choi, Dongbin Na
With the explosive growth of deep learning applications and increasing privacy concerns, the right to be forgotten has become a critical requirement in various AI industries. For e…
Towards Reliable AI Model Deployments: Multiple Input Mixup for Out-of-Distribution Detection
Dasol Choi, Dongbin Na
Recent remarkable success in the deep-learning industries has unprecedentedly increased the need for reliable model deployment. For example, the model should alert the user if the…
Towards Machine Unlearning Benchmarks: Forgetting the Personal Identities in Facial Recognition Systems
Dasol Choi, Dongbin Na
Machine unlearning is a crucial tool for enabling a classification model to forget specific data that are used in the training time. Recently, various studies have presented machin…
KoMultiText: Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online Services
Dasol Choi, Jooyoung Song, Eunsun Lee +3
With the growth of online services, the need for advanced text classification algorithms, such as sentiment analysis and biased text detection, has become increasingly evident. The…
A New Korean Text Classification Benchmark for Recognizing the Political Intents in Online Newspapers
Beomjune Kim, Eunsun Lee, Dongbin Na
Many users reading online articles in various magazines may suffer considerable difficulty in distinguishing the implicit intents in texts. In this work, we focus on automatically…
New Benchmarks for Asian Facial Recognition Tasks: Face Classification with Large Foundation Models
Jinwoo Seo, Soora Choi, Eungyeom Ha +2
The face classification system is an important tool for recognizing personal identity properly. This paper introduces a new Large-Scale Korean Influencer Dataset named KoIn. Our pr…
HOD: A Benchmark Dataset for Harmful Object Detection
Eungyeom Ha, Heemook Kim, Sung Chul Hong +1
Recent multi-media data such as images and videos have been rapidly spread out on various online services such as social network services (SNS). With the explosive growth of online…