2 citations · 3 across the 10 of their papers we have counts for
10 papers
Virtual Multiplex Staining for Histological Images using a Marker-wise Conditioned Diffusion Model
Hyun-Jic Oh, Junsik Kim, Zhiyi Shi +5
Multiplex imaging is revolutionizing pathology by enabling the simultaneous visualization of multiple biomarkers within tissue samples, providing molecular-level insights that trad…
Spatial-Temporal Pre-Training for Embryo Viability Prediction Using Time-Lapse Videos
Zhiyi Shi, Junsik Kim, Helen Y. Yang +5
Automating embryo viability prediction for in vitro fertilization (IVF) is important but challenging due to the limited availability of labeled pregnancy outcome data, as only a sm…
DualEdit: Dual Editing for Knowledge Updating in Vision-Language Models
Zhiyi Shi, Binjie Wang, Chongjie Si +3
Model editing aims to efficiently update a pre-trained model's knowledge without the need for time-consuming full retraining. While existing pioneering editing methods achieve prom…
Affordance-Aware Object Insertion via Mask-Aware Dual Diffusion
Jixuan He, Wanhua Li, Ye Liu +3
As a common image editing operation, image composition involves integrating foreground objects into background scenes. In this paper, we expand the application of the concept of Af…
Multimodal Learning for Embryo Viability Prediction in Clinical IVF
Junsik Kim, Zhiyi Shi, Davin Jeong +8
In clinical In-Vitro Fertilization (IVF), identifying the most viable embryo for transfer is important to increasing the likelihood of a successful pregnancy. Traditionally, this p…
Is What You Ask For What You Get? Investigating Concept Associations in Text-to-Image Models
Salma Abdel Magid, Weiwei Pan, Simon Warchol +4
Text-to-image (T2I) models are increasingly used in impactful real-life applications. As such, there is a growing need to audit these models to ensure that they generate desirable,…
MoRA: LoRA Guided Multi-Modal Disease Diagnosis with Missing Modality
Zhiyi Shi, Junsik Kim, Wanhua Li +2
Multi-modal pre-trained models efficiently extract and fuse features from different modalities with low memory requirements for fine-tuning. Despite this efficiency, their applicat…
Aligning Sight and Sound: Advanced Sound Source Localization Through Audio-Visual Alignment
Arda Senocak, Hyeonggon Ryu, Junsik Kim +3
Recent studies on learning-based sound source localization have mainly focused on the localization performance perspective. However, prior work and existing benchmarks overlook a c…
Joint-Task Regularization for Partially Labeled Multi-Task Learning
Kento Nishi, Junsik Kim, Wanhua Li +1
Multi-task learning has become increasingly popular in the machine learning field, but its practicality is hindered by the need for large, labeled datasets. Most multi-task learnin…
-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding
Ye Liu, Jixuan He, Wanhua Li +4
Video temporal grounding (VTG) is a fine-grained video understanding problem that aims to ground relevant clips in untrimmed videos given natural language queries. Most existing VT…