3 citations · 3 across the 9 of their papers we have counts for
10 papers
DiTailed: Ensuring Visual Object Consistency in Text-Image-to-Image Flow Matching Models
Francesco Taioli, Daniel Coelho, Iaroslav Melekhov +4
Despite remarkable progress in text-guided image editing, generative models frequently fail to preserve visual object consistency, defined as the preservation of a subject's key at…
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…
GBT-SAM: A Parameter-Efficient Depth-Aware Model for Generalizable Brain tumour Segmentation on mp-MRI
Cecilia Diana-Albelda, Roberto Alcover-Couso, Álvaro García-Martín +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…
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…
Gradient-based Class Weighting for Unsupervised Domain Adaptation in Dense Prediction Visual Tasks
Roberto Alcover-Couso, Marcos Escudero-Viñolo, Juan C. SanMiguel +1
In unsupervised domain adaptation (UDA), where models are trained on source data (e.g., synthetic) and adapted to target data (e.g., real-world) without target annotations, address…
Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models
Pablo Marcos-Manchón, Roberto Alcover-Couso, Juan C. SanMiguel +1
Diffusion models represent a new paradigm in text-to-image generation. Beyond generating high-quality images from text prompts, models such as Stable Diffusion have been successful…
The Robust Semantic Segmentation UNCV2023 Challenge Results
Xuanlong Yu, Yi Zuo, Zitao Wang +34
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segment…
Soft labelling for semantic segmentation: Bringing coherence to label down-sampling
Roberto Alcover-Couso, Marcos Escudero-Vinolo, Juan C. SanMiguel +1
In semantic segmentation, training data down-sampling is commonly performed due to limited resources, the need to adapt image size to the model input, or improve data augmentation.…