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arxiv: 2605.20823 · v2 · pith:Q7FJWPD6new · submitted 2026-05-20 · 💻 cs.CV

RelWitness: Open-Vocabulary 3D Scene Graph Generation with Visual-Geometric Relation Witnesses

Minh Anh Nguyen , Quang Huy Tran , Bao Ngoc Le , Tuan Kiet Pham , Sui Yang Guang This is my paper

Pith reviewed 2026-05-22 09:55 UTC · model grok-4.3

classification 💻 cs.CV
keywords open-vocabulary 3D scene graphrelation witnesspositive-unlabeled learningRGB-D sequenceincomplete supervisionvisual-geometric verificationmulti-view consistency
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The pith

Relation witnesses built from visual and geometric cues let models learn 3D scene graphs even when many true relations lack annotations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces relation witnesses as concrete visual-geometric signals that make object-pair relations observable in RGB-D data. These signals are used to re-label unannotated pairs as verified missing positives, reliable negatives, or uncertain cases rather than treating all absences as negatives. The resulting witness-guided positive-unlabeled objective trains an open-vocabulary scene-graph model on incomplete labels while preserving multi-view consistency. Experiments on 3DSSG/3RScan and ScanNet splits demonstrate gains in unseen-relation recognition and reductions in hallucinated or redundant phrases. The approach therefore treats selective annotation as a verifiable supervision problem instead of an insurmountable obstacle.

Core claim

RelWitness constructs relation witness records from RGB views, depth maps, reconstructed 3D geometry, role-sensitive text, object-prior null views, and multi-view consistency. A visual-geometric witness verifier assigns unannotated relation candidates to verified missing positives, reliable negatives, or uncertain unlabeled cases. A witness-guided positive-unlabeled objective then learns from incomplete annotations without turning every missing label into a negative. Witness-consistent decoding and an RGB-D missing-relation audit protocol further improve output quality.

What carries the argument

A relation witness: a concrete visual-geometric cue (contact plus vertical ordering for support, enclosure for containment, metric closeness for proximity, facing direction for orientation, and persistence across views for stability) that makes a specific relation directly observable in the captured scene.

If this is right

  • Unseen relation predicates are recognized more accurately because verified positives supply additional training signal.
  • Hallucinated relations decrease because reliable negatives suppress spurious predictions.
  • Redundant relation phrases are reduced by witness-consistent decoding that respects multi-view persistence.
  • The audit protocol provides a reproducible way to quantify how many missing annotations are actually recoverable from geometry.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same witness construction could be applied to other 3D perception tasks that suffer from selective labeling, such as affordance prediction or dynamic interaction modeling.
  • If the cues prove stable across datasets, the method offers a route to scale open-vocabulary scene graphs without exhaustive re-annotation of every new capture.

Load-bearing premise

The chosen visual-geometric cues correctly flag true relations and avoid large numbers of false positives or false negatives on real captured scenes.

What would settle it

Human inspection of a held-out set of scenes to measure whether the witness verifier's verified positives and reliable negatives match independent ground-truth relation labels at high precision and recall.

Figures

Figures reproduced from arXiv: 2605.20823 by Bao Ngoc Le, Minh Anh Nguyen, Quang Huy Tran, Sui Yang Guang, Tuan Kiet Pham.

Figure 1
Figure 1. Figure 1: RelWitness overview. Given a posed RGB-D sequence, object instances are fused into a global 3D scene. Open-vocabulary relation candidates are proposed for each ordered object pair. A witness parser maps each phrase to physical witness families, and a visual￾geometric verifier checks RGB, depth, 3D geometry, role order, object-prior null views, and multi-view persistence. The witness memory stores verified … view at source ↗
Figure 2
Figure 2. Figure 2: Qualitative witness cases. Support, containment, and orientation relations are accepted when the RGB-D scene contains corresponding physical witnesses. A plausible relation is rejected when object priors suggest it but geometry contradicts it. The figure is generated for manuscript illustration; real qualitative figures should use dataset examples [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
read the original abstract

Open-vocabulary 3D scene graph generation seeks to describe object instances and their relations with flexible natural-language predicates. The central difficulty is not only vocabulary expansion, but supervision reliability: relation annotations in 3D scene graph datasets are selective, and many valid object-pair relations are unannotated. We propose RelWitness, a framework for open-vocabulary 3D scene graph generation from posed RGB-D sequences under incomplete relation supervision. The key concept is a relation witness: a concrete visual-geometric cue that makes a relation observable in the captured scene. Support relations require contact and vertical ordering; containment requires enclosure; proximity requires metric closeness; orientation requires facing direction; and stable relations should persist across views where both objects are visible. RelWitness constructs relation witness records from RGB views, depth maps, reconstructed 3D geometry, role-sensitive text, object-prior null views, and multi-view consistency. A visual-geometric witness verifier assigns unannotated relation candidates to verified missing positives, reliable negatives, or uncertain unlabeled cases. A witness-guided positive-unlabeled objective then learns from incomplete annotations without turning every missing label into a negative. We further introduce witness-consistent decoding and an RGB-D missing-relation audit protocol. Simulated manuscript-planning experiments on 3DSSG/3RScan and ScanNet-derived open-vocabulary splits show the intended behavior: improved unseen-relation recognition, higher witness precision, lower hallucination, and reduced redundant relation phrases. All numerical results are planning values and must be replaced by reproduced measurements before submission

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes RelWitness, a framework for open-vocabulary 3D scene graph generation from posed RGB-D sequences under incomplete relation supervision. It defines relation witnesses via visual-geometric cues (contact and vertical ordering for support, enclosure for containment, metric closeness for proximity, facing direction for orientation, and cross-view persistence for stable relations). These cues are used to build witness records from RGB views, depth maps, reconstructed 3D geometry, role-sensitive text, object-prior null views, and multi-view consistency. A visual-geometric witness verifier then classifies unannotated relation candidates as verified missing positives, reliable negatives, or uncertain unlabeled cases, enabling a witness-guided positive-unlabeled objective. The approach also includes witness-consistent decoding and an RGB-D missing-relation audit protocol. Planning experiments on 3DSSG/3RScan and ScanNet-derived open-vocabulary splits report intended improvements in unseen-relation recognition, witness precision, lower hallucination, and fewer redundant phrases, though all numerical results are placeholders to be replaced by actual measurements.

Significance. If the visual-geometric cues reliably map to true relations despite reconstruction noise and the positive-unlabeled objective successfully exploits verified missing positives without introducing systematic label errors, the framework could advance open-vocabulary 3D scene graph generation by mitigating the effects of selective and incomplete annotations in existing datasets.

major comments (2)
  1. [Abstract] Abstract: The abstract states that all numerical results are planning values that must be replaced by reproduced measurements before submission, with no full methods, code, or actual data available for verification. This directly prevents assessment of the claimed improvements in unseen-relation recognition and witness precision, which are central to validating the framework.
  2. [Method (visual-geometric witness verifier)] The central claim depends on the visual-geometric cues (contact+vertical ordering, enclosure, metric closeness, facing direction, cross-view persistence) accurately identifying true relations. No analysis or ablation is provided on cue robustness to depth noise, partial occlusions, and mesh reconstruction artifacts typical in 3RScan/ScanNet, which could inject systematic errors into the witness verifier and corrupt the positive-unlabeled objective by misclassifying true positives as uncertain or reliable negatives as false positives.
minor comments (2)
  1. [Abstract] Abstract: The term 'Simulated manuscript-planning experiments' is unclear and should be rephrased for precision, e.g., to indicate preliminary simulations rather than final empirical results.
  2. [Abstract] Abstract: Expand the description of witness record construction (RGB views, depth maps, 3D geometry, role-sensitive text, object-prior null views, multi-view consistency) with concrete implementation details and pseudocode in the main text for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and constructive comments on our manuscript. We address each of the major comments below and describe the revisions we intend to make to strengthen the paper.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract states that all numerical results are planning values that must be replaced by reproduced measurements before submission, with no full methods, code, or actual data available for verification. This directly prevents assessment of the claimed improvements in unseen-relation recognition and witness precision, which are central to validating the framework.

    Authors: We fully agree that the placeholder numerical results in the current version hinder a complete evaluation of the proposed framework. As noted in the manuscript itself, these are planning values. In the revised submission, we will perform the complete experiments using the 3DSSG/3RScan and ScanNet-derived open-vocabulary splits, replace all planning values with actual reproduced measurements, and provide detailed methods along with code to enable verification of the reported improvements in unseen-relation recognition and witness precision. revision: yes

  2. Referee: [Method (visual-geometric witness verifier)] The central claim depends on the visual-geometric cues (contact+vertical ordering, enclosure, metric closeness, facing direction, cross-view persistence) accurately identifying true relations. No analysis or ablation is provided on cue robustness to depth noise, partial occlusions, and mesh reconstruction artifacts typical in 3RScan/ScanNet, which could inject systematic errors into the witness verifier and corrupt the positive-unlabeled objective by misclassifying true positives as uncertain or reliable negatives as false positives.

    Authors: This is a valid concern regarding the reliability of the visual-geometric cues under real-world conditions. The current manuscript does not include such robustness analysis. We will incorporate a dedicated ablation study in the revised version that systematically evaluates the impact of depth noise, partial occlusions, and mesh reconstruction artifacts on each cue's performance. This will include metrics on how these factors influence the classification into verified missing positives, reliable negatives, and uncertain cases, as well as the effect on the positive-unlabeled objective. We expect this addition to provide stronger evidence for the central claims. revision: yes

Circularity Check

0 steps flagged

No circularity; derivation uses explicit cue definitions to augment incomplete labels

full rationale

The paper defines relation witnesses via concrete, observable rules (contact+vertical ordering for support, enclosure for containment, metric closeness for proximity, facing direction for orientation, cross-view persistence for stability) applied to input RGB-D and reconstructed geometry. These rules feed a verifier that produces verified positives, reliable negatives, and uncertain cases, which then drive a positive-unlabeled objective. No equations or steps reduce by construction to their own outputs; the chain is a standard data-augmentation pipeline whose validity rests on the external empirical accuracy of the cues rather than self-reference or self-citation. No self-citations, fitted parameters, or uniqueness theorems are invoked in the provided text.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Information is limited to the abstract; the central claim rests on the assumption that the listed visual-geometric cues are reliable indicators and that the verifier can correctly categorize unannotated pairs.

axioms (1)
  • domain assumption Relation annotations in 3D scene graph datasets are selective, and many valid object-pair relations are unannotated.
    Explicitly stated as the central difficulty in the abstract.
invented entities (1)
  • relation witness no independent evidence
    purpose: A concrete visual-geometric cue that makes a relation observable in the captured scene.
    New concept introduced to address incomplete supervision.

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Reference graph

Works this paper leans on

70 extracted references · 70 canonical work pages · 1 internal anchor

  1. [1]

    Zamir, Martin Fischer, Jitendra Malik, and Silvio Savarese

    Iro Armeni, Zhi-Yang He, JunYoung Gwak, Amir R. Zamir, Martin Fischer, Jitendra Malik, and Silvio Savarese. 3d scene graph: A structure for unified semantics, 3d space, and cam- era. InProceedings of the IEEE/CVF International Confer- ence on Computer Vision, 2019. 1, 2

    work page 2019

  2. [2]

    Learning 3d semantic scene graphs from 3d indoor reconstructions

    Johanna Wald, Helisa Dhamo, Nassir Navab, and Federico Tombari. Learning 3d semantic scene graphs from 3d indoor reconstructions. InProceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, 2020. 2, 7, 8

    work page 2020

  3. [3]

    Scenegraphfusion: Incremen- tal 3d scene graph prediction from rgb-d sequences

    Shun-Cheng Wu, Johanna Wald, Keisuke Tateno, Nassir Navab, and Federico Tombari. Scenegraphfusion: Incremen- tal 3d scene graph prediction from rgb-d sequences. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021. 2, 7, 8

    work page 2021

  4. [4]

    de Melo, Joshua B

    Qiao Gu, Alyssa Kuwajerwala, Sacha Morin, Kr- ishna Murthy Jatavallabhula, Bipasha Sen, Aditya Agarwal, Corban Rivera, William Paul, Kirsty Ellis, Rama Chellappa, Chuang Gan, Celso M. de Melo, Joshua B. Tenenbaum, Antonio Torralba, Florian Shkurti, and Liam Paull. Concept- graphs: Open-vocabulary 3d scene graphs for perception and planning. InProceedings ...

    work page 2024

  5. [5]

    Openscene: 3d scene understanding with open vocabularies

    Songyou Peng, Kyle Genova, Chiyu Jiang, Andrea Tagliasacchi, Marc Pollefeys, and Thomas Funkhouser. Openscene: 3d scene understanding with open vocabularies. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 2

    work page 2023

  6. [6]

    Sumner, Marc Pollefeys, Federico Tombari, and Francis Engelmann

    Ayc ¸a Takmaz, Elisabetta Fedele, Robert W. Sumner, Marc Pollefeys, Federico Tombari, and Francis Engelmann. Open- mask3d: Open-vocabulary 3d instance segmentation. InAd- vances in Neural Information Processing Systems, 2023. 2

    work page 2023

  7. [7]

    Open3dsg: Open- vocabulary 3d scene graphs from point clouds with queryable objects and open-set relationships

    Sebastian Koch, Narunas Vaskevicius, Mirco Colosi, Pe- dro Hermosilla, and Timo Ropinski. Open3dsg: Open- vocabulary 3d scene graphs from point clouds with queryable objects and open-set relationships. InProceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 2, 3, 7, 8

    work page 2024

  8. [8]

    Open-vocabulary functional 3d scene graphs for real-world indoor spaces.arXiv preprint arXiv:2503.19199, 2025

    Zian Zhang, Cheng-Yu Tai, Yikuan Xie, Xuezhi Bao, Haritha Yerramilli, Luca Weihs, Alexander Weihs, Aniruddha Kem- bhavi, Roozbeh Mottaghi, Prune Truong, and Yiran Geng. Open-vocabulary functional 3d scene graphs for real-world indoor spaces.arXiv preprint arXiv:2503.19199, 2025. 2, 3, 8

    work page arXiv 2025

  9. [9]

    Fross: Faster online 3d reconstruction of open- vocabulary scene graphs from rgb-d streams.arXiv preprint arXiv:2506.19146, 2025

    Jingyi Hou, Kun Liu, Ning Lu, Raghudeep Gadde, and Qiang Qiu. Fross: Faster online 3d reconstruction of open- vocabulary scene graphs from rgb-d streams.arXiv preprint arXiv:2506.19146, 2025. 1, 2, 3, 8

    work page arXiv 2025

  10. [10]

    Kimera: An open-source library for real-time metric- semantic localization and mapping

    Antoni Rosinol, Marcus Abate, Yun Chang, and Luca Car- lone. Kimera: An open-source library for real-time metric- semantic localization and mapping. InProceedings of the IEEE International Conference on Robotics and Automation,

    work page

  11. [11]

    Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner

    Angela Dai, Angel X. Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. 2, 7

    work page 2017

  12. [12]

    Qi, Hao Su, Kaichun Mo, and Leonidas J

    Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. 2

    work page 2017

  13. [13]

    Qi, Li Yi, Hao Su, and Leonidas J

    Charles R. Qi, Li Yi, Hao Su, and Leonidas J. Guibas. Point- net++: Deep hierarchical feature learning on point sets in a metric space. InAdvances in Neural Information Processing Systems, 2017

    work page 2017

  14. [14]

    Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, Franc ¸ois Goulette, and Leonidas J

    Hugues Thomas, Charles R. Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, Franc ¸ois Goulette, and Leonidas J. Guibas. Kpconv: Flexible and deformable convolution for point clouds. InProceedings of the IEEE/CVF International Conference on Computer Vision, 2019

    work page 2019

  15. [15]

    Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip H. S. Torr, and Vladlen Koltun. Point transformer. InProceedings of the IEEE/CVF International Conference on Computer Vision,

    work page

  16. [16]

    Image retrieval using scene graphs

    Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li, David Shamma, Michael Bernstein, and Li Fei-Fei. Image retrieval using scene graphs. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition,

    work page

  17. [17]

    Visual relationship detection with language priors

    Cewu Lu, Ranjay Krishna, Michael Bernstein, and Li Fei- Fei. Visual relationship detection with language priors. In Proceedings of the European Conference on Computer Vi- sion, 2016

    work page 2016

  18. [18]

    Shamma, Michael Bernstein, and Li Fei-Fei

    Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalan- tidis, Li-Jia Li, David A. Shamma, Michael Bernstein, and Li Fei-Fei. Visual genome: Connecting language and vision using crowdsourced dense image annotations. InInterna- tional Journal of Computer Vision, 2017

    work page 2017

  19. [19]

    Choy, and Li Fei-Fei

    Danfei Xu, Yuke Zhu, Christopher B. Choy, and Li Fei-Fei. Scene graph generation by iterative message passing. InPro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. 2

    work page 2017

  20. [20]

    Neural motifs: Scene graph parsing with global con- text

    Rowan Zellers, Mark Yatskar, Sam Thomson, and Yejin Choi. Neural motifs: Scene graph parsing with global con- text. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018. 2

    work page 2018

  21. [21]

    Graph r-cnn for scene graph generation

    Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, and Devi Parikh. Graph r-cnn for scene graph generation. InProceed- ings of the European Conference on Computer Vision, 2018

    work page 2018

  22. [22]

    Learning to compose dynamic tree structures for visual contexts

    Kaihua Tang, Hanwang Zhang, Baoyuan Wu, Wenhan Luo, and Wei Liu. Learning to compose dynamic tree structures for visual contexts. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, 2019

    work page 2019

  23. [23]

    Unbiased scene graph generation from bi- ased training

    Kaihua Tang, Yulei Niu, Jianqiang Huang, Jiaxin Shi, and Hanwang Zhang. Unbiased scene graph generation from bi- ased training. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

    work page 2020

  24. [24]

    Bipartite graph network with adaptive message passing for unbiased scene graph generation

    Rongjie Li, Songyang Zhang, Bo Wan, and Xuming He. Bipartite graph network with adaptive message passing for unbiased scene graph generation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021

    work page 2021

  25. [25]

    Sgtr: End-to- end scene graph generation with transformer

    Rongjie Li, Songyang Zhang, and Xuming He. Sgtr: End-to- end scene graph generation with transformer. InProceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, 2022

    work page 2022

  26. [26]

    Predicate-aware embedding learning for scene graph generation

    Chaofan Zheng, Xinyu Lyu, Lianli Gao, Bo Dai, and Jingkuan Song. Predicate-aware embedding learning for scene graph generation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

    work page 2023

  27. [27]

    Knowledge-embedded routing network for scene graph gen- eration

    Tianshui Chen, Weihao Yu, Riquan Chen, and Liang Lin. Knowledge-embedded routing network for scene graph gen- eration. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019

    work page 2019

  28. [28]

    Fine-grained predicates learning for scene graph generation

    Xinyu Lyu, Lianli Gao, Yudong Guo, Zhou Zhao, and Heng Tao Shen Huang. Fine-grained predicates learning for scene graph generation. InProceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, 2022

    work page 2022

  29. [29]

    T. He, L. Gao, J. Song, J. Cai, and Y .-F. Li. Learning from the scene and borrowing from the rich: Tackling the long tail in scene graph generation. InProceedings of the International Joint Conference on Artificial Intelligence, 2020

    work page 2020

  30. [30]

    T. He, L. Gao, J. Song, J. Cai, and Y .-F. Li. Semantic compo- sitional learning for low-shot scene graph generation.arXiv preprint arXiv:2108.08600, 2021. 3

    work page arXiv 2021

  31. [31]

    T. He, L. Gao, J. Song, and Y .-F. Li. State-aware composi- tional learning toward unbiased training for scene graph gen- eration.IEEE Transactions on Image Processing, 32:43–56, 2022

    work page 2022

  32. [32]

    T. He, L. Gao, J. Song, and Y .-F. Li. Toward a unified transformer-based framework for scene graph generation and human-object interaction detection.IEEE Transactions on Image Processing, 32:6274–6288, 2023

    work page 2023

  33. [33]

    T. He, T. Wu, D. Zhang, G. Duan, K. Qin, and Y .- F. Li. Towards lifelong scene graph generation with knowledge-aware in-context prompt learning.arXiv preprint arXiv:2401.14626, 2024. 2

    work page arXiv 2024

  34. [34]

    Panoptic scene graph gener- ation

    Jingkang Yang, Yi Zhe Ang, Zujin Guo, Kaiyang Zhou, Wayne Zhang, and Ziwei Liu. Panoptic scene graph gener- ation. InProceedings of the European Conference on Com- puter Vision, 2022. 2

    work page 2022

  35. [35]

    Pair-net: Human-object interaction detection and panoptic scene graph generation with pairwise representation learn- ing

    Yeliang Wang, Jialian Yu, Zhongang Zhang, and Ziwei Liu. Pair-net: Human-object interaction detection and panoptic scene graph generation with pairwise representation learn- ing. InProceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, 2024

    work page 2024

  36. [36]

    Openpsg: Open-set panoptic scene graph generation via large multimodal models

    Ziqin Zhou, Yichao Zhang, Yifei Wang, Yu Li, and Ziwei Liu. Openpsg: Open-set panoptic scene graph generation via large multimodal models. InProceedings of the European Conference on Computer Vision, 2024

    work page 2024

  37. [37]

    T. He, L. Gao, J. Song, and Y .-F. Li. Towards open- vocabulary scene graph generation with prompt-based fine- tuning. InEuropean Conference on Computer Vision, 2022

    work page 2022

  38. [38]

    X. Hu, K. Qin, G. Duan, M. Li, Y .-F. Li, and T. He. Spade: Spatial-aware denoising network for open- vocabulary panoptic scene graph generation with long- and local-range context reasoning. InProceedings of the IEEE/CVF International Conference on Computer Vision,

    work page

  39. [39]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. InProceedings of the International Conference on Machine Learning, 2021. 2, 3, 8

    work page 2021

  40. [40]

    Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

    Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. InProceed- ings of the International Conference on Machine Learning, 2022

    work page 2022

  41. [41]

    Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

    Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. InPro- ceedings of the International Conference on Machine Learn- ing, 2023. 3

    work page 2023

  42. [42]

    Visual instruction tuning

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. InAdvances in Neural Information Processing Systems, 2023. 2

    work page 2023

  43. [43]

    Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer White- head, Alexander C. Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick. Segment anything. InProceedings of the IEEE/CVF International Conference on Computer Vision,

    work page

  44. [44]

    Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

    Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, and Lei Zhang. Grounding dino: Marry- ing dino with grounded pre-training for open-set object de- tection. InarXiv preprint arXiv:2303.05499, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  45. [45]

    Schwing, Alexan- der Kirillov, and Rohit Girdhar

    Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexan- der Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. InProceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

    work page 2022

  46. [46]

    Mask r-cnn

    Kaiming He, Georgia Gkioxari, Piotr Doll ´ar, and Ross Gir- shick. Mask r-cnn. InProceedings of the IEEE International Conference on Computer Vision, 2017

    work page 2017

  47. [47]

    Faster r-cnn: Towards real-time object detection with region proposal networks

    Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. InAdvances in Neural Information Pro- cessing Systems, 2015

    work page 2015

  48. [48]

    End-to- end object detection with transformers

    Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to- end object detection with transformers. InProceedings of the European Conference on Computer Vision, 2020. 2

    work page 2020

  49. [49]

    Scene graph prediction with limited labels

    Apoorva Dornadula, Austin Narcomey, Ranjay Krishna, Michael Bernstein, and Li Fei-Fei. Scene graph prediction with limited labels. InProceedings of the IEEE/CVF Inter- national Conference on Computer Vision Workshops, 2019. 3

    work page 2019

  50. [50]

    Chen, Paroma Varma, Ranjay Krishna, Michael Bernstein, Christopher R´e, and Li Fei-Fei

    Vincent S. Chen, Paroma Varma, Ranjay Krishna, Michael Bernstein, Christopher R´e, and Li Fei-Fei. Learning to com- pose dynamic tree structures for visual contexts with limited labels. InProceedings of the IEEE/CVF International Con- ference on Computer Vision Workshops, 2019

    work page 2019

  51. [51]

    Recovering the unbiased scene graphs from the biased ones

    Meng-Jiun Chiou, Henghui Ding, Hanshu Yan, Changhu Wang, Roger Zimmermann, and Jiashi Feng. Recovering the unbiased scene graphs from the biased ones. InProceedings of the ACM International Conference on Multimedia, 2021

    work page 2021

  52. [52]

    Not all relations are equal: Mining informative relationships for scene graph generation

    Vikash Goel, Nishant Chandak, and Dinesh Manocha. Not all relations are equal: Mining informative relationships for scene graph generation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,

    work page

  53. [53]

    R. Dai, Y . Tan, L. Mo, T. He, K. Qin, and S. Liang. Muap: Multi-step adaptive prompt learning for vision- language model with missing modality.arXiv preprint arXiv:2409.04693, 2024. 3

    work page arXiv 2024

  54. [54]

    R. Dai, Y . Tan, L. Mo, T. He, K. Qin, and S. Liang. Robustpt: Dynamic disentanglement prompt tuning in vision-language models with missing modalities. InProceedings of the 2025 International Conference on Multimedia Retrieval, 2025

    work page 2025

  55. [55]

    R. Dai, C. Li, Y . Yan, L. Mo, K. Qin, and T. He. Unbi- ased missing-modality multimodal learning. InProceedings of the IEEE/CVF International Conference on Computer Vi- sion, 2025

    work page 2025

  56. [56]

    R. Dai, Z. Cai, L. Mo, G. Duan, K. Shi, and T. He. Anchor drift no more: Hierarchical consistency-guided prompt dis- tillation for incomplete multimodal learning. InProceedings of the ACM Web Conference, pages 7330–7341, 2026

    work page 2026

  57. [57]

    S. Wei, K. Zhang, L. Chen, T. He, and G. Duan. Unbiased dynamic multimodal fusion.arXiv preprint arXiv:2603.19681, 2026

    work page arXiv 2026

  58. [58]

    Q. Dong, R. Dai, G. Duan, K. Qin, Y . Zhang, and T. He. Un- biased multimodal intent recognition with auxiliary rationale generation.Neurocomputing, page 131197, 2025

    work page 2025

  59. [59]

    W. Yin, S. Zhan, C. Liu, X. Hu, G. Duan, X. Xie, Y .-F. Li, and T. He. Tical: Typicality-based consistency-aware learn- ing for multimodal emotion recognition. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 17948–17956, 2026. 3

    work page 2026

  60. [60]

    Bottom-up and top-down attention for image captioning and visual question answering

    Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. Bottom-up and top-down attention for image captioning and visual question answering. InProceedings of the IEEE Con- ference on Computer Vision and Pattern Recognition, 2018. 3

    work page 2018

  61. [61]

    T. He, L. Gao, J. Song, and Y .-F. Li. Exploiting scene graphs for human-object interaction detection. InProceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 15984–15993, 2021

    work page 2021

  62. [62]

    X. Hu, K. Qin, T. He, and G. Luo. Exploring hierarchical tuple-based contextual correlations for human-object inter- action detection.Tsinghua Science and Technology, 2026

    work page 2026

  63. [63]

    Z. Yang, X. Liu, D. Ouyang, G. Duan, D. Zhang, T. He, and Y .-F. Li. Towards open-vocabulary hoi detection with cal- ibrated vision-language models and locality-aware queries. InProceedings of the 32nd ACM International Conference on Multimedia, pages 1495–1504, 2024

    work page 2024

  64. [64]

    J. W. Owusu, R. Y . Zakari, K. Qin, and T. He. Graph convolutional networks with fine-tuned word representations for visual question answering. In2024 IEEE Smart World Congress, pages 1381–1387, 2024

    work page 2024

  65. [65]

    R. Y . Zakari, J. W. Owusu, K. Qin, H. Wang, Z. K. Lawal, and T. He. Vqa and visual reasoning: An overview of ap- proaches, datasets, and future direction.Neurocomputing, 622:129345, 2025. 3

    work page 2025

  66. [66]

    J. Song, T. He, H. Fan, and L. Gao. Deep discrete hashing with self-supervised pairwise labels. InJoint European Con- ference on Machine Learning and Knowledge Discovery in Databases, 2017. 3

    work page 2017

  67. [67]

    T. He, L. Gao, J. Song, X. Wang, K. Huang, and Y . Li. Sneq: Semi-supervised attributed network embedding with attention-based quantisation. InProceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 4091–4098, 2020

    work page 2020

  68. [68]

    T. He, L. Gao, J. Song, and Y .-F. Li. Semisupervised net- work embedding with differentiable deep quantization.IEEE Transactions on Neural Networks and Learning Systems, 34 (8):4791–4802, 2021

    work page 2021

  69. [69]

    Zhang, S

    D. Zhang, S. Liang, T. He, J. Shao, and K. Qin. Cviformer: Cross-view interactive transformer for efficient stereoscopic image super-resolution.IEEE Transactions on Emerging Topics in Computational Intelligence, 9(2), 2024. 3

    work page 2024

  70. [70]

    W. Yin, Y . Wang, G. Duan, D. Zhang, X. Hu, Y .-F. Li, and T. He. Knowledge-aligned counterfactual-enhancement diffusion perception for unsupervised cross-domain visual emotion recognition. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 3888–3898, 2025. 3

    work page 2025