7 citations · 14 across the 8 of their papers we have counts for
8 papers
The MixCount Dataset: Bridging the Data Gap for Open-Vocabulary Object Counting
Corentin Dumery, Niki Amini-Naieni, Shervin Naini +1
Object counting is a foundational vision task with over a decade of dedicated research, yet state-of-the-art models still fail systematically in the mixed-object setting that domin…
CountGD++: Generalized Prompting for Open-World Counting
Niki Amini-Naieni, Andrew Zisserman
The flexibility and accuracy of methods for automatically counting objects in images and videos are limited by the way the object can be specified. While existing methods allow use…
Open-World Object Counting in Videos
Niki Amini-Naieni, Andrew Zisserman
We introduce a new task of open-world object counting in videos: given a text description, or an image example, that specifies the target object, the objective is to enumerate all…
Benchmarking Vision Foundation Models for Input Monitoring in Autonomous Driving
Mert Keser, Halil Ibrahim Orhan, Niki Amini-Naieni +3
Deep neural networks (DNNs) remain challenged by distribution shifts in complex open-world domains like automated driving (AD): Robustness against yet unknown novel objects (semant…
Unveiling Ontological Commitment in Multi-Modal Foundation Models
Mert Keser, Gesina Schwalbe, Niki Amini-Naieni +2
Ontological commitment, i.e., used concepts, relations, and assumptions, are a corner stone of qualitative reasoning (QR) models. The state-of-the-art for processing raw inputs, th…
CountGD: Multi-Modal Open-World Counting
Niki Amini-Naieni, Tengda Han, Andrew Zisserman
The goal of this paper is to improve the generality and accuracy of open-vocabulary object counting in images. To improve the generality, we repurpose an open-vocabulary detection…