mlsanityData to 14 Oct 2026

SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection

arXiv 2307.01750 ↗First posted 4 Jul 2023cs.CV cs.LGcited by 28

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Abstract (arXiv)

This paper provides a novel framework for single-domain generalized object detection (i.e., Single-DGOD), where we are interested in learning and maintaining the semantic structures of self-augmented compound cross-domain samples to enhance the model's generalization ability. Different from DGOD trained on multiple source domains, Single-DGOD is far more challenging to generalize well to multiple target domains with only one single source domain. Existing methods mostly adopt a similar treatment from DGOD to learn domain-invariant features by decoupling or compressing the semantic space. However, there may have two potential limitations: 1) pseudo attribute-label correlation, due to extremely scarce single-domain data; and 2) the semantic structural information is usually ignored, i.e., we found the affinities of instance-level semantic relations in samples are crucial to model generalization. In this paper, we introduce Semantic Reasoning with Compound Domains (SRCD) for Single-DGOD. Specifically, our SRCD contains two main components, namely, the texture-based self-augmentation (TBSA) module, and the local-global semantic reasoning (LGSR) module. TBSA aims to eliminate the effects of irrelevant attributes associated with labels, such as light, shadow, color, etc., at the image level by a light-yet-efficient self-augmentation. Moreover, LGSR is used to further model the semantic relationships on instance features to uncover and maintain the intrinsic semantic structures. Extensive experiments on multiple benchmarks demonstrate the effectiveness of the proposed SRCD.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 58 of its 91 references that are in the map. First posted on arXiv 4 Jul 2023.

Builds on

58 of 91 references.

References · 58

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 10; 2025: 9; 2026: 5.

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  1. 2023Object-Aware Domain Generalization for Object Detectioncited by 21
  2. 2024Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognitioncited by 19
  3. 2024G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detectioncited by 17
  4. 2024Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognitioncited by 13
  5. 2024Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospectscited by 12
  6. 2024PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object Detectioncited by 11
  7. 2023GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learningcited by 8
  8. 2023Attribute-Aware Representation Rectification for Generalized Zero-Shot Learningcited by 6
  9. 2024TsCA: On the Semantic Consistency Alignment via Conditional Transport for Compositional Zero-Shot Learningcited by 6
  10. 2025Generalized Diffusion Detector: Mining Robust Features from Diffusion Models for Domain-Generalized Detectioncited by 6
  11. 2024Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detectioncited by 5
  12. 2024On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Surveycited by 5
16 more
  1. 2023ParsNets: A Parsimonious Orthogonal and Low-Rank Linear Networks for Zero-Shot Learningcited by 4
  2. 2024Dual Expert Distillation Network for Generalized Zero-Shot Learningcited by 4
  3. 2025UPRE: Zero-Shot Domain Adaptation for Object Detection via Unified Prompt and Representation Enhancementcited by 4
  4. 2024Epsilon: Exploring Comprehensive Visual-Semantic Projection for Multi-Label Zero-Shot Learningcited by 3
  5. 2025Style-Adaptive Detection Transformer for Single-Source Domain Generalized Object Detectioncited by 3
  6. 2025Single-Domain Generalized Object Detection by Balancing Domain Diversity and Invariancecited by 2
  7. 2025Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalizationcited by 2
  8. 2025Boosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and Transferabilitycited by 2
  9. 2025ODOV: Benchmark the Open-Domain Open-Vocabulary Object Detectioncited by 1
  10. 2025Towards Single-Source Domain Generalized Object Detection via Causal Visual Promptscited by 1
  11. 2026RT-SDGOD: Real-Time Single-Domain Generalized Object Detectioncited by 1
  12. 2025FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Datacited by 0
  13. 2026VFM4SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detectioncited by 0
  14. 2026Bridge: Basis-Driven Causal Inference Marries VFMs for Domain Generalizationcited by 0
  15. 2026ExDet: Open-Domain Open-Vocabulary Detection with Cross-modal Extrapolation and Rectificationcited by 0
  16. 2026Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detectioncited by 0