mlsanityData to 7 Oct 2026

Context in object detection: a systematic literature review

arXiv 2503.23249 ↗First posted 29 Mar 2025cs.CVcited by 3

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Abstract (arXiv)

Context is an important factor in computer vision as it offers valuable information to clarify and analyze visual data. Utilizing the contextual information inherent in an image or a video can improve the precision and effectiveness of object detectors. For example, where recognizing an isolated object might be challenging, context information can improve comprehension of the scene. This study explores the impact of various context-based approaches to object detection. Initially, we investigate the role of context in object detection and survey it from several perspectives. We then review and discuss the most recent context-based object detection approaches and compare them. Finally, we conclude by addressing research questions and identifying gaps for further studies. More than 265 publications are included in this survey, covering different aspects of context in different categories of object detection, including general object detection, video object detection, small object detection, camouflaged object detection, zero-shot, one-shot, and few-shot object detection. This literature review presents a comprehensive overview of the latest advancements in context-based object detection, providing valuable contributions such as a thorough understanding of contextual information and effective methods for integrating various context types into object detection, thus benefiting researchers.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 14 of its 86 references that are in the map. First posted on arXiv 29 Mar 2025.

Builds on

14 of 86 references.

References · 14

  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  2. 2018Deep Learning for Generic Object Detection: A Surveycited by 392
  3. 2021Anabranch Network for Camouflaged Object Segmentationcited by 227
  4. 2015UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Trackingcited by 98
  5. 2021Context-aware Cross-level Fusion Network for Camouflaged Object Detectioncited by 86
  6. 2020Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Modulecited by 39
  7. 2020Context-Transformer: Tackling Object Confusion for Few-Shot Detectioncited by 27
  8. 2023Context Understanding in Computer Vision: A Surveycited by 23
  9. 2021Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learningcited by 12
  10. 2022ARM3D: Attention-based relation module for indoor 3D object detectioncited by 11
  11. 2017Cascade Region Proposal and Global Context for Deep Object Detectioncited by 6
  12. 2020Global Context Aware RCNN for Object Detectioncited by 5
2 more
  1. 2022Transformer-based Context Condensation for Boosting Feature Pyramids in Object Detectioncited by 5
  2. 2016Deep Feature Based Contextual Model for Object Detectioncited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networkscited by 0
  2. 2026Predicting Blastocyst Formation in IVF: Integrating DINOv2 and Attention-Based LSTM on Time-Lapse Embryo Imagescited by 0
  3. 2026A Lightweight Multimodal Vision-Language Framework for Early-Stage Anatomical Green Fruit Classification in Commercial Orchardscited by 0