mlsanityData to 7 Oct 2026

Simultaneous Detection and Segmentation

arXiv 1407.1808 ↗First posted 7 Jul 2014cs.CVcited by 158

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Abstract (arXiv)

We aim to detect all instances of a category in an image and, for each instance, mark the pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS). Unlike classical bounding box detection, SDS requires a segmentation and not just a box. Unlike classical semantic segmentation, we require individual object instances. We build on recent work that uses convolutional neural networks to classify category-independent region proposals (R-CNN [16]), introducing a novel architecture tailored for SDS. We then use category-specific, top- down figure-ground predictions to refine our bottom-up proposals. We show a 7 point boost (16% relative) over our baselines on SDS, a 5 point boost (10% relative) over state-of-the-art on semantic segmentation, and state-of-the-art performance in object detection. Finally, we provide diagnostic tools that unpack performance and provide directions for future work.

Key facts. Cited by 158 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jul 2014.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 41; 2022: 46; 2023: 29; 2024: 20; 2025: 15; 2026: 6.

158 papers.

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  1. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,148
  2. 2021Bottleneck Transformers for Visual Recognitioncited by 364
  3. 2022OneFormer: One Transformer to Rule Universal Image Segmentationcited by 327
  4. 2023Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIPcited by 199
  5. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
  6. 2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
  7. 2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116
  8. 2022Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentationcited by 114
  9. 2023CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Predictioncited by 114
  10. 2021Self-Guided and Cross-Guided Learning for Few-Shot Segmentationcited by 92
  11. 2021Weakly Supervised Object Localization and Detection: A Surveycited by 84
  12. 2021STEP: Segmenting and Tracking Every Pixelcited by 72
38 more
  1. 2024The Ingredients for Robotic Diffusion Transformerscited by 72
  2. 2022RbA: Segmenting Unknown Regions Rejected by Allcited by 67
  3. 2022Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentationcited by 65
  4. 2022CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentationcited by 60
  5. 2021DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervisioncited by 59
  6. 2022Feature-Proxy Transformer for Few-Shot Segmentationcited by 58
  7. 2022Waymo Open Dataset: Panoramic Video Panoptic Segmentationcited by 57
  8. 2021Learning Meta-class Memory for Few-Shot Semantic Segmentationcited by 56
  9. 2021ISTR: End-to-End Instance Segmentation with Transformerscited by 52
  10. 2021Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentationcited by 47
  11. 2022MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Modelscited by 47
  12. 2022A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Predictioncited by 46
  13. 2021Deep Reinforcement Learning in Computer Vision: A Comprehensive Surveycited by 42
  14. 2023On Advantages of Mask-level Recognition for Outlier-aware Segmentationcited by 42
  15. 2021Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervisioncited by 39
  16. 2021Few-shot Segmentation with Optimal Transport Matching and Message Flowcited by 36
  17. 2022Learning Pixel Trajectories with Multiscale Contrastive Random Walkscited by 36
  18. 2022BlobGAN: Spatially Disentangled Scene Representationscited by 35
  19. 2022TubeFormer-DeepLab: Video Mask Transformercited by 35
  20. 2023Loss Functions in the Era of Semantic Segmentation: A Survey and Outlookcited by 33
  21. 2021SOLO: A Simple Framework for Instance Segmentationcited by 32
  22. 2023Self-Calibrated Cross Attention Network for Few-Shot Segmentationcited by 32
  23. 2021Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsingcited by 31
  24. 2023DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Modelcited by 30
  25. 2023Semantic Image Segmentation: Two Decades of Researchcited by 26
  26. 2022Interclass Prototype Relation for Few-Shot Segmentationcited by 25
  27. 2023MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentationcited by 25
  28. 2024COCONut: Modernizing COCO Segmentationcited by 24
  29. 2022Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentationcited by 23
  30. 2021DeepLab2: A TensorFlow Library for Deep Labelingcited by 22
  31. 2022A Strong Baseline for Generalized Few-Shot Semantic Segmentationcited by 22
  32. 2022Mask Matching Transformer for Few-Shot Segmentationcited by 22
  33. 2023FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Networkcited by 22
  34. 2023Object Detection with Transformers: A Reviewcited by 22
  35. 2024Hybrid Mamba for Few-Shot Segmentationcited by 21
  36. 2024Eliminating Feature Ambiguity for Few-Shot Segmentationcited by 19
  37. 2023Deep Learning Technique for Human Parsing: A Survey and Outlookcited by 18
  38. 2023ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentationcited by 18

50 most cited of 158 All 158 (JSON)