Trapped in texture bias? A large scale comparison of deep instance
  segmentation
Johannes Theodoridis Jessica Hofmann Johannes Maucher Andreas Schilling
Do deep learning models for instance segmentation generalize to novel objects
in a systematic way? For classification, such behavior has been questioned. In
this study, we aim to understand if certain design decisions such as framework,
architecture or pre-training contribute to the semantic understanding of
instance segmentation. To answer this question, we consider a special case of
robustness and compare pre-trained models on a challenging benchmark for
object-centric, out-of-distribution texture. We do not introduce another method
in this work. Instead, we take a step back and evaluate a broad range of
existing literature. This includes Cascade and Mask R-CNN, Swin Transformer,
BMask, YOLACT(++), DETR, BCNet, SOTR and SOLOv2. We find that YOLACT++, SOTR
and SOLOv2 are significantly more robust to out-of-distribution texture than
other frameworks. In addition, we show that deeper and dynamic architectures
improve robustness whereas training schedules, data augmentation and
pre-training have only a minor impact. In summary we evaluate 68 models on 61
versions of MS COCO for a total of 4148 evaluations.