From the 1 of 10 linked papers with an AI index.
10 papers
CoDi -- an exemplar-conditioned diffusion model for low-shot counting
Grega Å uÅ¡tar, Jer Pelhan, Alan LukežiÄ +1
CoDi is a latent diffusion-based model that uses exemplar-conditioned conditioning to generate high-quality density maps for low-shot object counting, enabling accurate object loca…
Mitigating Objectness Bias and Region-to-Text Misalignment for Open-Vocabulary Panoptic Segmentation
Nikolay Kormushev, Josip Å ariÄ, Matej Kristan
Open-vocabulary panoptic segmentation remains hindered by two coupled issues: (i) mask selection bias, where objectness heads trained on closed vocabularies suppress masks of categ…
Generalized-Scale Object Counting with Gradual Query Aggregation
Jer Pelhan, Alan Lukezic, Matej Kristan
Few-shot detection-based counters estimate the number of instances in the image specified only by a few test-time exemplars. A common approach to localize objects across multiple s…
Distractor-Aware Memory-Based Visual Object Tracking
Jovana Videnovic, Matej Kristan, Alan Lukezic
Recent emergence of memory-based video segmentation methods such as SAM2 has led to models with excellent performance in segmentation tasks, achieving leading results on numerous b…
What Holds Back Open-Vocabulary Segmentation?
Josip Å ariÄ, Ivan MartinoviÄ, Matej Kristan +1
Standard segmentation setups are unable to deliver models that can recognize concepts outside the training taxonomy. Open-vocabulary approaches promise to close this gap through la…
DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-supervised Panoptic Segmentation
Ivan MartinoviÄ, Josip Å ariÄ, Marin OrÅ¡iÄ +2
Pixel-level annotation is expensive and time-consuming. Semi-supervised segmentation methods address this challenge by learning models on few labeled images alongside a large corpu…