Segmentation

[ CVPR 2024 ] We present FreeDA, a novel training-free diffusion-augmented method for open-vocabulary segmentation, which leverages diffusion models to visually localize generated concepts and local-global similarities to match superpixel-based class-agnostic regions with semantic classes.

[ WACV 2024 ] We present FOSSIL, a novel Unsupervised Open-Vocabulary Semantic Segmentation model that enables a self-supervised visual backbone to perform open-vocabulary segmentation directly on the visual modality by retrieving a support set of generated synthetic references.

[ WACV 2024 ] We present a novel method for enhancing semantic segmentation models evaluation by categorizing errors, offering insights into false positives/negatives, and improving performance through the combination of model strengths.

[ BMVC 2023 ] We present a novel superpixel-based positional encoding technique that combines Vision Transformer (ViT) features with superpixels priors to improve the performance of semantic segmentation architectures.

[ ICIAP 2023 ] We propose a novel open-vocabulary semantic segmentation paradigm based on weakly supervised visual prototypes extracted from image-caption pairs and adopt a retrieval-based approach to combine visual and textual features to enhance segmentation performance.

[ CAIP 2021 | Oral ] We test and devise variants of both the Boundary and Active Boundary losses, two recent proposals which deal with the prediction of semantic boundaries.

[ IWANN 2021 | Oral ] We propose two loss functions that improve the semantic segmentation accuracy at the boundary level.