From the 1 of 7 linked papers with an AI index.
7 papers
DiTailed: Ensuring Visual Object Consistency in Text-Image-to-Image Flow Matching Models
Francesco Taioli, Daniel Coelho, Iaroslav Melekhov +4
The paper introduces a new dataset (ABO-Edit) for studying visual object consistency in text‑guided image editing and proposes FlowMirror, a parameter‑free auxiliary loss that supe…
GBT-SAM: A Parameter-Efficient Depth-Aware Model for Generalizable Brain tumour Segmentation on mp-MRI
Cecilia Diana-Albelda, Roberto Alcover-Couso, Ãlvaro GarcÃa-MartÃn +2
Gliomas are aggressive brain tumors that require accurate imaging-based diagnosis, with segmentation playing a critical role in evaluating morphology and treatment decisions. Manua…
Pathology-Aware Adaptive Watermarking for Text-Driven Medical Image Synthesis
Chanyoung Kim, Dayun Ju, Jinyeong Kim +3
As recent text-conditioned diffusion models have enabled the generation of high-quality images, concerns over their potential misuse have also grown. This issue is critical in the…
Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances
Javier Montalvo, Roberto Alcover-Couso, Pablo Carballeira +3
This paper introduces a novel synthetic dataset that captures urban scenes under a variety of weather conditions, providing pixel-perfect, ground-truth-aligned images to facilitate…
VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation
Roberto Alcover-Couso, Marcos Escudero-Viñolo, Juan C. SanMiguel +1
Segmentation models are typically constrained by the categories defined during training. To address this, researchers have explored two independent approaches: adapting Vision-Lang…
Layer-wise Model Merging for Unsupervised Domain Adaptation in Segmentation Tasks
Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-Viñolo +1
Merging parameters of multiple models has resurfaced as an effective strategy to enhance task performance and robustness, but prior work is limited by the high costs of ensemble cr…