104 citations · 110 across the 11 of their papers we have counts for
15 papers
ReState: Noise-Level-Aware State Correction for Training-Free Object Removal in Few-Step Diffusion Models
Donghwan Lee, Sanghyun Jo, Sihyun Kim +2
Training-free object removal commonly redirects self-attention away from the target region, but on few-step distilled diffusion models this often leaves object-shaped residuals. We…
Depth-to-RGB: Repurposing a Frozen Depth Estimator for Geometry-Guided Compositing
Sanghyun Jo, Chae Yeon Lim, Donghwan Lee +3
Reference-based object compositing inserts or replaces an object using a background image, a reference image, and a 2D compositing mask. These inputs guide appearance and placement…
PatchGate: Narrowing the Verbalization Gap with Intrinsic Object Inventories in Frozen Vision-Language Models
Jihyung Ko, Eunji Jung, Hyeongsub Kim +4
Reliable image captioning in Vision-Language Models (VLMs) requires captions to be both precise and complete, avoiding unsupported object mentions while covering visible objects. E…
One Click per Cell Type Suffices: Training-free Group Interaction for Cell Instance Segmentation
Sanghyun Jo, Seo Jin Lee, Seohyung Hong +4
Cell instance segmentation models trained on cell-specific datasets suffer severe performance drops on out-of-distribution cell types, while interactive foundation models overcome…
EraseLoRA: MLLM-Driven Foreground Exclusion and Background Subtype Aggregation for Dataset-Free Object Removal
Sanghyun Jo, Donghwan Lee, Eunji Jung +2
Object removal must prevent the masked target from reappearing and reconstruct the occluded background with structural and contextual fidelity, rather than merely filling a hole pl…
On the Collapse of Generative Paths: A Criterion and Correction for Diffusion Steering
Ziseok Lee, Minyeong Hwang, Wooyeol Lee +6
Inference-time steering adapts pretrained diffusion and flow models to new tasks without retraining, often utilizing ratio-of-densities constructions that reweight time-indexed mar…
ISAC: Training-Free Instance-to-Semantic Attention Control for Multi-Instance Generation
Sanghyun Jo, Wooyeol Lee, Ziseok Lee +3
Recent open-weight text-to-image (T2I) diffusion models still struggle with multi-instance prompts, often omitting or merging instances and mixing semantics among similar objects.…
Early Timestep Zero-Shot Candidate Selection for Instruction-Guided Image Editing
Joowon Kim, Ziseok Lee, Donghyeon Cho +4
Despite recent advances in diffusion models, achieving reliable image generation and editing remains challenging due to the inherent diversity induced by stochastic noise in the sa…
COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentation
Sanghyun Jo, Seo Jin Lee, Seungwoo Lee +3
Cell instance segmentation (CIS) is crucial for identifying individual cell morphologies in histopathological images, providing valuable insights for biological and medical researc…
TRACE: Your Diffusion Model is Secretly an Instance Edge Detector
Sanghyun Jo, Ziseok Lee, Wooyeol Lee +3
High-quality instance and panoptic segmentation has traditionally relied on dense instance-level annotations such as masks, boxes, or points, which are costly, inconsistent, and di…
TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
Sanghyun Jo, Soohyun Ryu, Sungyub Kim +2
We identify a critical bias in contemporary CLIP-based models, which we denote as single tag bias. This bias manifests as a disproportionate focus on a singular tag (word) while ne…
DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and Intra-Class Regions for Weakly-Supervised Semantic Segmentation
Sanghyun Jo, Fei Pan, In-Jae Yu +1
Weakly-supervised semantic segmentation (WSS) ensures high-quality segmentation with limited data and excels when employed as input seed masks for large-scale vision models such as…
MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic Segmentation
Sanghyun Jo, In-Jae Yu, Kyungsu Kim
Weakly-supervised semantic segmentation aims to reduce labeling costs by training semantic segmentation models using weak supervision, such as image-level class labels. However, mo…
RecurSeed and EdgePredictMix: Pseudo-Label Refinement Learning for Weakly Supervised Semantic Segmentation across Single- and Multi-Stage Frameworks
Sanghyun Jo, In-Jae Yu, Kyungsu Kim
Although weakly supervised semantic segmentation using only image-level labels (WSSS-IL) is potentially useful, its low performance and implementation complexity still limit its ap…
Puzzle-CAM: Improved localization via matching partial and full features
Sanghyun Jo, In-Jae Yu
Weakly-supervised semantic segmentation (WSSS) is introduced to narrow the gap for semantic segmentation performance from pixel-level supervision to image-level supervision. Most a…