Trapped in texture bias? A large scale comparison of deep instance segmentation
We are happy to share that our recent work on robust vision, lead by our PhD student Johannes Theodoridis, got accepted at ECCV 2022 and will be presented in Tel Aviv, 23.-27. October 2022.
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.
Authors: Johannes Theodoridis, Jessica Hofmann, Johannes Maucher, Andreas Schilling
Paper: Trapped in texture bias? A large scale comparison of deep instance segmentation