mlsanityData to 7 Oct 2026

RIO: A Benchmark for Reasoning Intention-Oriented Objects in Open Environments

arXiv 2310.17290 ↗First posted 26 Oct 2023cs.CVcited by 15

Not on the map. Why?

Abstract (arXiv)

Intention-oriented object detection aims to detect desired objects based on specific intentions or requirements. For instance, when we desire to "lie down and rest", we instinctively seek out a suitable option such as a "bed" or a "sofa" that can fulfill our needs. Previous work in this area is limited either by the number of intention descriptions or by the affordance vocabulary available for intention objects. These limitations make it challenging to handle intentions in open environments effectively. To facilitate this research, we construct a comprehensive dataset called Reasoning Intention-Oriented Objects (RIO). In particular, RIO is specifically designed to incorporate diverse real-world scenarios and a wide range of object categories. It offers the following key features: 1) intention descriptions in RIO are represented as natural sentences rather than a mere word or verb phrase, making them more practical and meaningful; 2) the intention descriptions are contextually relevant to the scene, enabling a broader range of potential functionalities associated with the objects; 3) the dataset comprises a total of 40,214 images and 130,585 intention-object pairs. With the proposed RIO, we evaluate the ability of some existing models to reason intention-oriented objects in open environments.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 43 of its 50 references that are in the map. First posted on arXiv 26 Oct 2023.

Builds on

43 of 50 references.

References · 43

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2020End-to-End Object Detection with Transformerscited by 7,384
  4. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
  5. 2018YOLOv3: An Incremental Improvementcited by 2,652
  6. 2016Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotationscited by 2,646
  7. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,219
  8. 2017Decoupled Weight Decay Regularizationcited by 1,722
  9. 2015Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Modelscited by 1,352
  10. 2016Modeling Context in Referring Expressionscited by 1,054
  11. 2019VL-BERT: Pre-training of Generic Visual-Linguistic Representationscited by 954
  12. 2015Generation and Comprehension of Unambiguous Object Descriptionscited by 897
31 more
  1. 2021MDETR -- Modulated Detection for End-to-End Multi-Modal Understandingcited by 811
  2. 2018MAttNet: Modular Attention Network for Referring Expression Comprehensioncited by 427
  3. 2019Cross-Modal Self-Attention Network for Referring Image Segmentationcited by 263
  4. 2021Vision-Language Transformer and Query Generation for Referring Segmentationcited by 239
  5. 2019UNITER: UNiversal Image-TExt Representation Learningcited by 225
  6. 2019A Fast and Accurate One-Stage Approach to Visual Groundingcited by 194
  7. 2023PolyFormer: Referring Image Segmentation as Sequential Polygon Generationcited by 147
  8. 2021Referring Transformer: A One-step Approach to Multi-task Visual Groundingcited by 143
  9. 2018Learning to Assemble Neural Module Tree Networks for Visual Groundingcited by 139
  10. 2022SeqTR: A Simple yet Universal Network for Visual Groundingcited by 138
  11. 2020Improving One-stage Visual Grounding by Recursive Sub-query Constructioncited by 137
  12. 2020Referring Image Segmentation via Cross-Modal Progressive Comprehensioncited by 131
  13. 2017Learning Two-Branch Neural Networks for Image-Text Matching Taskscited by 119
  14. 2019A Real-Time Cross-modality Correlation Filtering Method for Referring Expression Comprehensioncited by 111
  15. 2019Dynamic Graph Attention for Referring Expression Comprehensioncited by 111
  16. 2021Encoder Fusion Network with Co-Attention Embedding for Referring Image Segmentationcited by 111
  17. 2016Modeling Relationships in Referential Expressions with Compositional Modular Networkscited by 106
  18. 2018Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networkscited by 102
  19. 2019Learning to Compose and Reason with Language Tree Structures for Visual Groundingcited by 98
  20. 2017Grounding Referring Expressions in Images by Variational Contextcited by 82
  21. 2019Zero-Shot Grounding of Objects from Natural Language Queriescited by 79
  22. 2017Learning to Act Properly: Predicting and Explaining Affordances from Imagescited by 74
  23. 2017Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queriescited by 56
  24. 2018Real-Time Referring Expression Comprehension by Single-Stage Grounding Networkcited by 56
  25. 2021One-Shot Object Affordance Detection in the Wildcited by 40
  26. 2021One-Shot Affordance Detectioncited by 32
  27. 2022TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillationcited by 25
  28. 2019What Object Should I Use? - Task Driven Object Detectioncited by 22
  29. 2022Phrase-Based Affordance Detection via Cyclic Bilateral Interactioncited by 19
  30. 2022SiRi: A Simple Selective Retraining Mechanism for Transformer-based Visual Groundingcited by 12
  31. 2022Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Taskscited by 12

Built on by

Cited by year

Citing papers per year: 2024: 8; 2025: 7.

15 papers.

Sort:

By area:

  1. 2024GROUNDHOG: Grounding Large Language Models to Holistic Segmentationcited by 70
  2. 2025VideoWorld: Exploring Knowledge Learning from Unlabeled Videoscited by 42
  3. 2024MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMscited by 38
  4. 2025RefDrone: A Challenging Benchmark for Referring Expression Comprehension in Drone Scenescited by 17
  5. 2024Intent3D: 3D Object Detection in RGB-D Scans Based on Human Intentioncited by 16
  6. 2024VLTP: Vision-Language Guided Token Pruning for Task-Oriented Segmentationcited by 11
  7. 2025Visual Intention Grounding for Egocentric Assistantscited by 11
  8. 2024Text-driven Affordance Learning from Egocentric Visioncited by 9
  9. 2024Beyond Literal Descriptions: Understanding and Locating Open-World Objects Aligned with Human Intentionscited by 8
  10. 2025Afford-X: Generalizable and Slim Affordance Reasoning for Task-oriented Manipulationcited by 3
  11. 2024Visual-Geometric Collaborative Guidance for Affordance Learningcited by 2
  12. 2025Beyond Object Categories: Multi-Attribute Reference Understanding for Visual Groundingcited by 2
3 more
  1. 2025Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruningcited by 1
  2. 2025Visual Affordance Prediction: Survey and Reproducibilitycited by 1
  3. 2024Leverage Task Context for Object Affordance Rankingcited by 0