Deep Equilibrium Object Detection and Segmentation

Query-based object detectors and segmenters have made great progress in their respective tasks by employing an iterative refinement decoder. These query-based methods directly represent object instances with a set of learnable queries. These query vectors are progressively refined to stable, meaningful representations through a sequence of decoder layers and then used to directly predict object locations (mask or box) and categories with different heads. In this paper, we present a novel perspective on query-based object decoder design with infinite refinement (DEQ-Decoder) through a deep equilibrium model. Our DEQ-Decoder models the query vector refinement as the fixed point solving of an implicit layer. To be more specific to query refinement, we use a two-step unrolled equilibrium equation to explicitly capture the query vector refinement. Accordingly, we are able to incorporate refinement awareness into the DEQ-Decoder training with the inexact gradient back-propagation (RAG). In addition, to stabilize the training of our DEQ-Decoder and improve its generalization ability, we devise a deep supervision scheme on the optimization path of DEQ-Decoder with refinement-aware perturbation (RAP). To demonstrate the effectiveness of DEQ-Decoder, we apply it to object detection and instance segmentation. For object detection, we propose DEQDet based on our deep equilibrium decoder. DEQDet converges faster, consumes less memory, and achieves better results than the baseline counterpart (AdaMixer). In particular, our DEQDet with ResNet50 backbone and 300 queries achieves the 49.6 mAP and 33.9 APₛ on the MS COCO benchmark under 2× training scheme (24 epochs). For instance segmentation, Our DEQSeg achieves much better box mAP metrics and slightly better mask metrics for different mask decoding branches. Code and trained models are publicly available at https://github.com/MCG-NJU/DEQDet.