  Foundation models are a strong trend in deep learning and computer vision.
These models serve as a base for applications as they require minor or no
further fine-tuning by developers to integrate into their applications.
Foundation models for zero-shot object segmentation such as Segment Anything
(SAM) output segmentation masks from images without any further object
information. When they are followed in a pipeline by an object identification
model, they can perform object detection without training. Here, we focus on
training such an object identification model. A crucial practical aspect for an
object identification model is to be flexible in input size. As object
identification is an image retrieval problem, a suitable method should handle
multi-query multi-gallery situations without constraining the number of input
images (e.g. by having fixed-size aggregation layers). The key solution to
train such a model is the centroid triplet loss (CTL), which aggregates image
features to their centroids. CTL yields high accuracy, avoids misleading
training signals and keeps the model input size flexible. In our experiments,
we establish a new state of the art on the ArmBench object identification task,
which shows general applicability of our model. We furthermore demonstrate an
integrated unseen object detection pipeline on the challenging HOPE dataset,
which requires fine-grained detection. There, our pipeline matches and
surpasses related methods which have been trained on dataset-specific data.
