Deep Equilibrium Object Detection

• URL: http://arxiv.org/abs/2308.09564v1
• Date: Fri, 18 Aug 2023 13:56:03 GMT
• Title: Deep Equilibrium Object Detection
• Authors: Shuai Wang, Yao Teng, Limin Wang
• Abstract summary: We present a new query-based object detector (DEQDet) by designing a deep equilibrium decoder. Our experiments demonstrate DEQDet converges faster, consumes less memory, and achieves better results than the baseline counterpart.
• Score: 24.69829309391189
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Query-based object detectors directly decode image features into 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 and categories with simple FFN heads. In this paper, we present a new query-based object detector (DEQDet) by designing a deep equilibrium decoder. Our DEQ decoder models the query vector refinement as the fixed point solving of an {implicit} layer and is equivalent to applying {infinite} steps of refinement. To be more specific to object decoding, 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 training with the inexact gradient back-propagation (RAG). In addition, to stabilize the training of our DEQDet and improve its generalization ability, we devise the deep supervision scheme on the optimization path of DEQ with refinement-aware perturbation~(RAP). Our experiments demonstrate 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 $49.5$ mAP and $33.0$ AP$_s$ on the MS COCO benchmark under $2\times$ training scheme (24 epochs).

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