8 papers
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…
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.…
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…
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…