mlsanityData to 7 Oct 2026

Segmenting Object Affordances: Reproducibility and Sensitivity to Scale

arXiv 2409.01814 ↗First posted 3 Sep 2024cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

Visual affordance segmentation identifies image regions of an object an agent can interact with. Existing methods re-use and adapt learning-based architectures for semantic segmentation to the affordance segmentation task and evaluate on small-size datasets. However, experimental setups are often not reproducible, thus leading to unfair and inconsistent comparisons. In this work, we benchmark these methods under a reproducible setup on two single objects scenarios, tabletop without occlusions and hand-held containers, to facilitate future comparisons. We include a version of a recent architecture, Mask2Former, re-trained for affordance segmentation and show that this model is the best-performing on most testing sets of both scenarios. Our analysis shows that models are not robust to scale variations when object resolutions differ from those in the training set.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 40 of its 68 references that are in the map. First posted on arXiv 3 Sep 2024.

Builds on

40 of 68 references.

References · 40

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2020End-to-End Object Detection with Transformerscited by 7,384
  5. 2017Mask R-CNNcited by 7,259
  6. 2017Squeeze-and-Excitation Networkscited by 4,763
  7. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
  8. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  9. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  10. 2016Pyramid Scene Parsing Networkcited by 2,472
  11. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,338
  12. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
28 more
  1. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,148
  2. 2021Perceiver: General Perception with Iterative Attentioncited by 1,089
  3. 2018Dual Attention Network for Scene Segmentationcited by 1,001
  4. 2021Segmenter: Transformer for Semantic Segmentationcited by 855
  5. 2020ResNeSt: Split-Attention Networkscited by 445
  6. 2017Dilated Residual Networkscited by 293
  7. 2018Context Encoding for Semantic Segmentationcited by 289
  8. 2020Populating 3D Scenes by Learning Human-Scene Interactioncited by 164
  9. 2023BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objectscited by 144
  10. 2017AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detectioncited by 126
  11. 2018On Regularized Losses for Weakly-supervised CNN Segmentationcited by 121
  12. 2019FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentationcited by 70
  13. 2023HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructionscited by 47
  14. 2016Deep TEN: Texture Encoding Networkcited by 44
  15. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labellingcited by 37
  16. 2020Reactive Human-to-Robot Handovers of Arbitrary Objectscited by 25
  17. 2022A4T: Hierarchical Affordance Detection for Transparent Objects Depth Reconstruction and Manipulationcited by 23
  18. 2020Object-Independent Human-to-Robot Handovers using Real Time Robotic Visioncited by 17
  19. 2019Dynamic Feature Fusion for Semantic Edge Detectioncited by 16
  20. 2023Leveraging Next-Active Objects for Context-Aware Anticipation in Egocentric Videoscited by 16
  21. 2023Bayesian Deep Learning for Affordance Segmentation in imagescited by 13
  22. 2021Towards safe human-to-robot handovers of unknown containerscited by 11
  23. 2023STRAP: Structured Object Affordance Segmentation with Point Supervisioncited by 10
  24. 2021Are standard Object Segmentation models sufficient for Learning Affordance Segmentation?cited by 6
  25. 2021The CORSMAL benchmark for the prediction of the properties of containerscited by 6
  26. 2022A large scale multi-view RGBD visual affordance learning datasetcited by 6
  27. 2018Structured Label Inference for Visual Understandingcited by 4
  28. 2022A mixed-reality dataset for category-level 6D pose and size estimation of hand-occluded containerscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Visual Affordance Prediction: Survey and Reproducibilitycited by 1
  2. 2026Uncertainty Estimation in Instance Segmentation of Affordances via Bayesian Visual Transformerscited by 0
  3. 2026Reproducible Multimodal Affordance Predictioncited by 0