I^3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors

• URL: http://arxiv.org/abs/2103.13757v1
• Date: Thu, 25 Mar 2021 11:14:36 GMT
• Title: I^3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors
• Authors: Chaoqi Chen, Zebiao Zheng, Yue Huang, Xinghao Ding, Yizhou Yu
• Abstract summary: Implicit Instance-Invariant Network (I3Net) is tailored for adapting one-stage detectors. I3Net implicitly learns instance-invariant features via exploiting the natural characteristics of deep features in different layers. Experiments reveal that I3Net exceeds the state-of-the-art performance on benchmark datasets.
• Score: 64.93963042395976
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent works on two-stage cross-domain detection have widely explored the local feature patterns to achieve more accurate adaptation results. These methods heavily rely on the region proposal mechanisms and ROI-based instance-level features to design fine-grained feature alignment modules with respect to the foreground objects. However, for one-stage detectors, it is hard or even impossible to obtain explicit instance-level features in the detection pipelines. Motivated by this, we propose an Implicit Instance-Invariant Network (I^3Net), which is tailored for adapting one-stage detectors and implicitly learns instance-invariant features via exploiting the natural characteristics of deep features in different layers. Specifically, we facilitate the adaptation from three aspects: (1) Dynamic and Class-Balanced Reweighting (DCBR) strategy, which considers the coexistence of intra-domain and intra-class variations to assign larger weights to those sample-scarce categories and easy-to-adapt samples; (2) Category-aware Object Pattern Matching (COPM) module, which boosts the cross-domain foreground objects matching guided by the categorical information and suppresses the uninformative background features; (3) Regularized Joint Category Alignment (RJCA) module, which jointly enforces the category alignment at different domain-specific layers with a consistency regularization. Experiments reveal that I^3Net exceeds the state-of-the-art performance on benchmark datasets.

Related papers

• DATR: Unsupervised Domain Adaptive Detection Transformer with Dataset-Level Adaptation and Prototypical Alignment [7.768332621617199]
  We introduce a strong DETR-based detector named Domain Adaptive detection TRansformer ( DATR) for unsupervised domain adaptation of object detection. Our proposed DATR incorporates a mean-teacher based self-training framework, utilizing pseudo-labels generated by the teacher model to further mitigate domain bias. Experiments demonstrate superior performance and generalization capabilities of our proposed DATR in multiple domain adaptation scenarios.
  arXiv  Detail & Related papers  (2024-05-20T03:48:45Z)
• Adaptive Spot-Guided Transformer for Consistent Local Feature Matching [64.30749838423922]
  We propose Adaptive Spot-Guided Transformer (ASTR) for local feature matching. ASTR models the local consistency and scale variations in a unified coarse-to-fine architecture.
  arXiv  Detail & Related papers  (2023-03-29T12:28:01Z)
• Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment [59.831917206058435]
  Domain adaptive detection aims to improve the generalization of detectors on target domain. Recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning. We introduce a unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning.
  arXiv  Detail & Related papers  (2023-01-01T08:38:07Z)
• Multi-Granularity Alignment Domain Adaptation for Object Detection [33.32519045960187]
  Domain adaptive object detection is challenging due to distinctive data distribution between source domain and target domain. We propose a unified multi-granularity alignment based object detection framework towards domain-invariant feature learning.
  arXiv  Detail & Related papers  (2022-03-31T09:05:06Z)
• Domain Generalisation for Object Detection under Covariate and Concept Shift [10.32461766065764]
  Domain generalisation aims to promote the learning of domain-invariant features while suppressing domain-specific features. An approach to domain generalisation for object detection is proposed, the first such approach applicable to any object detection architecture.
  arXiv  Detail & Related papers  (2022-03-10T11:14:18Z)
• Domain Adaptive YOLO for One-Stage Cross-Domain Detection [4.596221278839825]
  Domain Adaptive YOLO (DA-YOLO) is proposed to improve cross-domain performance for one-stage detectors. We evaluate our proposed method on popular datasets like Cityscapes, KITTI, SIM10K and etc.
  arXiv  Detail & Related papers  (2021-06-26T04:17:42Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Aligning Pretraining for Detection via Object-Level Contrastive Learning [57.845286545603415]
  Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning. We argue that this could be sub-optimal and thus advocate a design principle which encourages alignment between the self-supervised pretext task and the downstream task. Our method, called Selective Object COntrastive learning (SoCo), achieves state-of-the-art results for transfer performance on COCO detection.
  arXiv  Detail & Related papers  (2021-06-04T17:59:52Z)
• Cross-domain Detection via Graph-induced Prototype Alignment [114.8952035552862]
  We propose a Graph-induced Prototype Alignment (GPA) framework to seek for category-level domain alignment. In addition, in order to alleviate the negative effect of class-imbalance on domain adaptation, we design a Class-reweighted Contrastive Loss. Our approach outperforms existing methods with a remarkable margin.
  arXiv  Detail & Related papers  (2020-03-28T17:46:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.