613 citations · 777 across the 19 of their papers we have counts for
20 papers
Rethinking the Use of Vision Transformers for AI-Generated Image Detection
NaHyeon Park, Kunhee Kim, Junsuk Choe +1
Rich feature representations derived from CLIP-ViT have been widely utilized in AI-generated image detection. While most existing methods primarily leverage features from the final…
What "Not" to Detect: Negation-Aware VLMs via Structured Reasoning and Token Merging
Inha Kang, Youngsun Lim, Seonho Lee +3
State-of-the-art vision-language models (VLMs) suffer from a critical failure in understanding negation, often referred to as affirmative bias. This limitation is particularly seve…
Sampling Bag of Views for Open-Vocabulary Object Detection
Hojun Choi, Junsuk Choe, Hyunjung Shim
Existing open-vocabulary object detection (OVD) develops methods for testing unseen categories by aligning object region embeddings with corresponding VLM features. A recent study…
OVS Meets Continual Learning: Towards Sustainable Open-Vocabulary Segmentation
Dongjun Hwang, Yejin Kim, Minyoung Lee +2
Open-Vocabulary Segmentation (OVS) aims to segment classes that are not present in the training dataset. However, most existing studies assume that the training data is fixed in ad…
NegMerge: Sign-Consensual Weight Merging for Machine Unlearning
Hyo Seo Kim, Dongyoon Han, Junsuk Choe
Machine unlearning aims to selectively remove specific knowledge from a trained model. Existing approaches, such as Task Arithmetic, fine-tune the model on the forget set to create…
Weakly Supervised Semantic Segmentation for Driving Scenes
Dongseob Kim, Seungho Lee, Junsuk Choe +1
State-of-the-art techniques in weakly-supervised semantic segmentation (WSSS) using image-level labels exhibit severe performance degradation on driving scene datasets such as City…