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REVIEW 3 major objections 4 minor 48 references

R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video

T0 review · 3 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Object memory beats text memory for egocentric video Q&A.

desk verdict A genuinely new memory representation for egocentric video QA, clearly described and honestly limited, but the evidence is too thin to prove the memory mechanism works. read the letter →

arxiv 2608.11017 v1 pith:O4MC4NUL submitted 2026-08-11 cs.CV cs.AIcs.HCcs.MM

Ke Ma , Yamin Mao , Weiming Li , Shuai Tan , Yijie Zhong , Hao Chen , Haofen Wang , Meng Wang This is my paper · ORCID
classification cs.CVcs.AIcs.HCcs.MM
keywords egocentricvideo3Dscenegraphtemporalmemoryobject-centricquestionansweringretrieval-augmentedgenerationRGB-onlyliftingpersistentobjecttrackinganchor-relativerepresentation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that a long egocentric RGB video can be converted into a compact, queryable memory organized around persistent objects and their movements relative to stable scene anchors, and that this memory supports object-centric question answering better than text- or caption-based retrieval. The proposed method, R4DSG, never builds a globally aligned 3D map; it only needs monocular, unposed RGB video, and it records where an object is relative to stable references such as a table or fridge, when that relation changed, and what local context explains the change. On 255 object-related questions from EgoLifeQA, the memory outperforms the text-based EgoRAG-Text baseline by 6.7 points overall and 12.5 points on when questions under question-only retrieval. A reader should care because wearable assistants and AR systems need exactly this kind of long-horizon object memory, and the result suggests scene-graph structure can be made practical without depth or posed camera input.

What carries the argument

The central object is the relative 4D scene graph memory: a time-indexed collection of anchor-relative object states rather than a globally consistent 4D reconstruction. Within it, the load-bearing identity is the temporal event $e_i=(p,[t_s,t_e],a^-,a^+,r_i)$, written when persistent object $p$ moves from source anchor $a^-$ to destination anchor $a^+$ over span $[t_s,t_e]$ with a local rationale $r_i$. The machinery that carries the argument is the combination of episode-conditioned promptable video segmentation (SAM 3-style), RGB-only 3D lifting (SAM 3D-style) to get coarse relative location and extent, conservative persistent-track association across frames, static-anchor inference, and the deterministic memory-writing operator $h$ that aggregates frame graphs and events into segment-level retrieval documents. This mechanism keeps errors local, avoids global alignment, and yields a memory that is compact enough to retrieve (0.58 MB for roughly seven hours of footage) and structured enough to answer object-state questions.

What would settle it

Manually label persistent object identities and anchor-change events on a subset of the same 828-clip Day1-A1-JAKE stream, then compare the predicted tracks and memory entries to those labels; if the tracks are frequently split or merged and the anchor-relative states are wrong, yet end-to-end QA accuracy stays at 39.6 percent, then the claimed mechanism of persistent identity plus anchor-relative transitions is not what drives the reported gains.

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Extended reading notes

Core claim

R4DSG's central discovery is that the right unit for long-horizon egocentric memory is not a clip, caption, or global 3D reconstruction but a temporal event recording a persistent object's transition from one stable anchor to another. Each frame contributes a small graph of semantic and relative 3D attributes; the system links these into persistent tracks using semantic compatibility, size and position continuity, and neighborhood consistency; and it writes a memory entry whenever a dynamic object's dominant anchor changes and the new state remains stable over time. The resulting memory stores time spans, places, activities, object states, and interaction cues in segment-level documents that can be retrieved directly for question answering. The authors argue that this anchor-relative, identity-preserving design is what lets the system answer where, when, and why questions that captions and transcripts do not preserve.

Load-bearing premise

The method assumes its automatic perception stack, video segmentation, RGB-only 3D lifting, and the hand-defined association rules for linking observations into persistent tracks, produces accurate enough object identities and anchor-relative states, since the paper never evaluates these modules against ground truth and only reports end-to-end QA accuracy.

Editorial extensions

If this is right

  • Under question-only retrieval, R4DSG scores 39.6 percent overall versus 32.9 percent for EgoRAG-Text on the 255-question object-centric subset, a 6.7-point gain, and 43.1 percent versus 30.6 percent on the 72 when questions, a 12.5-point gain.
  • Removing persistent cross-segment identity and anchor-transition writing (the No-Transition control) drops accuracy to 34.9 percent overall and 34.7 percent on when, indicating the graph transitions themselves carry the benefit rather than object/relation serialization alone.
  • The memory compresses 828 clips into 134 retrieval documents totaling 0.58 MB, making the representation practical as a persistent memory substrate for wearable assistants.
  • Option-blind retrieval with no explanation fields still outperforms the AMEGO-inspired active-memory baseline by 1.2 points overall and 5.6 points on when, showing the benefit does not depend on exposing answer options.
  • Adding explanation-oriented fields raises why-subset accuracy from 33.3 percent to 40.0 percent, though the authors flag this result as exploratory due to the 15-question subset size.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension: benchmark the upstream perception stack (mask quality, 3D lift error, track precision and recall) against manual persistent-object annotations on the same footage; the argument would be strengthened if QA accuracy tracks perception accuracy rather than staying flat when tracks are corrupted.
  • The anchor-relative design implies that wearable assistants do not need to solve simultaneous localization and mapping to answer where and when questions, which could make memory construction feasible on-device; a direct stress test would be running the pipeline on multi-day continuous capture and measuring cross-day identity continuity, which the paper lists as future work.
  • If the gains replicate beyond the single A1_JAKE subject, the memory format could also serve embodied multimedia agents that pool experiences across users, since the anchor-relative state is defined locally and does not require a shared world coordinate system.
  • One implicit reading is that caption-centric memory loses exactly the relational change signal that object-centric questions rely on; an even stronger test would compare R4DSG against an oracle that retrieves the correct clip, isolating whether the gains come from retrieval precision or from answer synthesis over structured evidence.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper presents R4DSG, a pipeline that converts long egocentric RGB video into a queryable memory of relative 4D scene graphs. It separates static anchors from dynamic objects, maintains persistent object tracks via hand-defined association rules, writes anchor-relative temporal events (Eq. 5) and segment-level retrieval documents (Eq. 7), and retrieves top-8 documents for a Qwen-based multiple-choice QA system. Evaluated on 255 object-centric questions from EgoLifeQA A1_JAKE, it reports a 6.7-point overall gain and a 12.5-point gain on when questions over EgoRAG-Text under question-only retrieval, plus ablations and an exploratory why-memory extension. The authors explicitly scope all claims to this single-subject split and acknowledge that the outputs are not a human-verified perception benchmark.

Significance. If the result holds, the paper would demonstrate a practical way to build object-centric memory from monocular wearable video without global alignment or depth input, compressing 828 clips into a 0.58-MB JSON memory. The contribution is a representation and a deterministic memory-writing operator rather than a trained model, which makes the pipeline reproducible in principle and separates the method from end-to-end learned approaches. The paper is honest about its limitations: it explicitly limits empirical claims to a single-subject split, describes the why-memory result as exploratory, and states that outputs are not a human-verified perception benchmark. The No-Transition control also provides a useful first attempt at isolating the effect of persistent identity and anchor transitions. However, the strength of the evidence is not yet commensurate with the central claim: the evaluation is small, the adapted baselines are not official reproductions, and the upstream perception stack that generates the memory documents is never validated against ground truth. The significance of the mechanism is therefore plausible but not established.

major comments (3)
  1. [Sec. 3.4.2–3.4.4, Eq. (5)] The load-bearing component of the pipeline is the automatic construction of persistent object tracks and anchor-change events, yet the manuscript never evaluates masks, 3D lifts, persistent IDs, static/dynamic role assignments, or the Eq. (5) anchor-change criterion against ground truth or even a manual inspection sample at scale. The QA model consumes only the memory documents written by this stack, so end-to-end accuracy cannot distinguish a faithful memory from one that produces fluent but false transitions. This is exactly the correctness-risk concern raised by the stress-test note, and it lands. Please add a quantitative validation of the perception/association modules—at minimum ID precision/recall and anchor-change precision/recall on a labeled subset of the released Day1 stream—and provide the operational thresholds used for the 'stable across time' criterion in Eq. (5) and for the association rules in Sec. 3.4.3.
  2. [Sec. 4.1, Table 2] The evaluation uses 255 questions from a single subject with no error bars, confidence intervals, or significance tests. A 6.7-point overall gain is roughly 17 questions, and the 12.5-point gain on the 72-question when subset is roughly 9 questions; these differences may be within chance variation. Report statistical significance, confidence intervals, or a per-day breakdown to establish that the gains are not noise. In addition, the adapted EMQA-style and AMEGO-inspired baselines are not official reproductions, and the implementation details are not given in the paper; please provide the exact adaptation procedure (or use official checkpoints) so that the comparison is verifiable and fair.
  3. [Sec. 4.2.3, Table 4; Sec. 4.1 No-Transition control] The No-Transition control is the only direct test of the paper's central mechanism, but its specification is incomplete. The text says it removes 'persistent cross-segment identity and anchor-transition writing' while sharing 'cached visual evidence and local relations,' but it is not clear how the control's retrieval documents are constructed without persistent identity or whether the retrieval protocol is identical to the main method. The memory-granularity ablation in Table 4 is explicitly described as not isolating the causal mechanism, which further weakens the connection between representation and gain. Please specify the No-Transition control in full—document schema, retrieval procedure, and how identity removal is implemented—so that the reader can verify that the comparison isolates anchor-transition memory from other differences.
minor comments (4)
  1. [Figure 2 and Sec. 3.4.2] The text alternates between 'SAM 3-style'/'SAM 3D-style' and 'SAM 3'/'SAM 3D'; make the naming consistent and clarify whether the actual released models are used or only style-inspired reimplementations, since the reproducibility section promises a concrete pipeline.
  2. [Sec. 4.1] State the version/release date of Qwen3.5-27B and confirm that the same prompt template and decoding settings are used across all methods; otherwise the comparison may be sensitive to prompt-formatting differences rather than memory design.
  3. [Sec. 3.3, Eq. (6)] The Y_k field is described as optional explanation-oriented, but the main Retrieval+ results in Table 2 presumably have Y_k empty; state this explicitly in the main text or in the table caption so that the reader knows which fields were active for the headline numbers.
  4. [Table 4] The Event-only row reports 27.8 on When, which is close to the Plain RAG number; add a sentence interpreting why Event-only underperforms Episodic-only, as this seems to undercut the intuitive claim that anchor-change events are the key to when questions.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: R4DSG's memory construction is a fixed pipeline evaluated against an external benchmark; unvalidated perception is a robustness concern, not a circular derivation.

full rationale

The derivation chain is not circular. The memory construction (Eqs. 2-7) is a deterministic transform of the input video using external RGB-only segmentation and lifting models, with no parameter fitted to the EgoLifeQA labels or answer options. Evaluation is externally anchored: 255 questions come from the public EgoLifeQA A1_JAKE split, retrieval is question-only, and answer options are withheld until after top-8 retrieval, so the memory cannot be tuned to the choices. The 'when' gain is the designed mechanism (memory explicitly stores time spans and anchor-change events via Eq. 5), not a quantity forced by construction. The paper's own limitation that outputs 'are not yet a human-verified perception benchmark' identifies an upstream fidelity risk, not circularity: mask, 3D-lift, or association errors would degrade the memory but do not make the QA result equal to an input. The ablation table also notes the granularity comparison 'does not isolate the causal mechanism,' which is honest scoping. Self-citations in the related work (e.g., refs. [20]-[22]) are contextual and not load-bearing.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities or forces. Its free parameters are mostly unspecified thresholds in the association and memory-writing rules, and its axioms are domain assumptions about the reliability of upstream perception models and the sufficiency of relative geometry. The strongest empirical claim depends on all of these, but none are validated independently.

free parameters (4)
  • retrieval top-k = 8
    The main branch retrieves the top 8 documents (Sec. 4.1, Sec. 3.4.5). This is a manually chosen hyperparameter and is not ablated across values.
  • anchor-relative stability window = unspecified
    Equation 5 writes an event only when the new anchor-relative state 'remains stable across time'. The duration that defines 'stable' is not specified, and it is a hand-chosen design parameter that affects how many events are emitted.
  • association thresholds for persistent identity = unspecified
    Sec. 3.4.3 says linking uses 'semantic compatibility, coarse 3D size continuity, relative 3D position continuity, and neighborhood consistency', but no quantitative thresholds or weighting are given. These choices determine track splitting and merging.
  • static anchor motion threshold = unspecified
    Static anchors are 'objects that recur with low motion inside location-consistent windows' (Sec. 3.4.3). The 'low motion' threshold is not defined, and it determines which objects become anchors.
assumptions (4)
  • domain assumption The SAM3-style segmentation and SAM3D-style lifting provide sufficiently accurate masks, tracklets, and relative 3D metadata for the memory to work.
    The entire pipeline rests on these RGB-only foundation models (Sec. 3.4.2), but the paper does not evaluate their output against ground truth for egocentric long video.
  • domain assumption A relative, anchor-constrained representation is sufficient for object-centric QA; global alignment is unnecessary.
    Sec. 3.1 explicitly discards a global world coordinate system. If some questions require global layout or cross-room spatial reasoning, the representation could be insufficient.
  • domain assumption The released EgoLifeQA segment records provide valid activity and location episodes with controlled object vocabularies.
    Sec. 3.4.1 uses these released records as ground truth for episode construction. Errors in these annotations would propagate to the memory.
  • domain assumption The adapted baselines (EMQA-style and AMEGO-inspired) are fair and correctly implemented approximations of their respective methods.
    Sec. 4.1 states these are 'not official reproductions'. The comparison depends on the authors' implementation choices, which are not fully specified.

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Cite this review

Pith. "Pith review of R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video." pith.science (2026). https://pith.science/paper/O4MC4NUL

@misc{pith2026260811017,
  author       = {Pith},
  title        = {Pith review of: R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O4MC4NUL}},
  note         = {Machine review of arXiv:2608.11017}
}
read the original abstract

Long-horizon egocentric video is a rich substrate for wearable AI assistants, but object-centric questions such as where an item was moved, when it last changed state, or why it was relocated remain difficult because caption- and transcript-based memories rarely preserve persistent object identity or structured spatial change. Existing long-video QA methods mainly emphasize temporal grounding and clip retrieval, while prior 3D scene-graph methods typically assume stronger geometry than free-motion wearable RGB video provides, including point clouds, RGB-D input, posed views, sparse reconstruction, or reconstructed scenes. R4DSG introduces a relative 4D scene graph memory for long egocentric video. Instead of storing raw graph sequences, R4DSG converts video into compact queryable memory entries indexed by time, place, persistent objects, anchor-relative change, and local interaction context. The main idea is to separate stable anchors from dynamic objects, maintain persistent object identity across frames, and represent object state through anchor-relative transitions rather than a globally aligned world model. Built on recent RGB-only advances in promptable video segmentation, temporal propagation, and relative 3D lifting, the method produces a retrieval-ready memory directly usable for long-horizon question answering. Evaluation on a 255-question object-related subset from EgoLifeQA shows, under question-only retrieval, a 6.7-point overall gain over EgoRAG-Text and a 12.5-point gain on when questions, which highlights the value of temporally organized object memory. These results position relative 4D scene graphs as a practical memory substrate for wearable assistants, AR systems, and embodied multimedia agents. GitHub Page: https://dualtransparency.github.io/R4DSG/.

Figures

Figures reproduced from arXiv: 2608.11017 by the authors.

Figure 1
Figure 1. R4DSG maps long egocentric video to a relative scene-memory for object-centric question answering. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. R4DSG pipeline. Semantic episodes guide RGB-only segmentation and lifting; persistent identities and anchor changes [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Bag-transfer example showing three anchor-relative states, the induced cart [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

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Reference graph

Works this paper leans on

48 extracted references · 29 canonical work pages

  1. [1]

    Runze Cai, Nuwan Janaka, Hyeongcheol Kim, Yang Chen, Shengdong Zhao, Yun Huang, and David Hsu. 2025. AiGet: Transforming Everyday Moments into Hidden Knowledge Discovery with AI Assistance on Smart Glasses. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. doi:10.1145/3706598.3713953

    arXiv 2025

  2. [2]

    Nicolas Carion, Laura Gustafson, Yuan-Ting Hu, Shoubhik Debnath, Ronghang Hu, Didac Suris, Chaitanya Ryali, Kalyan Vasudev Alwala, Haitham Khedr, An- drew Huang, et al. 2025. SAM 3: Segment Anything with Concepts.arXiv preprint arXiv:2511.16719(2025). doi:10.48550/arXiv.2511.16719

    arXiv doi:10.48550/arxiv.2511.16719 2025

  3. [3]

    Qirui Chen, Shangzhe Di, and Weidi Xie. 2025. Grounded Multi-Hop VideoQA in Long-Form Egocentric Videos. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 39. 2159–2167. doi:10.1609/aaai.v39i2.32214

    doi:10.1609/aaai.v39i2.32214 2025

  4. [4]

    Xingyu Chen, Fu-Jen Chu, Pierre Gleize, Kevin J Liang, Alexander Sax, Hao Tang, Weiyao Wang, Michelle Guo, Thibaut Hardin, Xiang Li, et al . 2025. SAM 3D: 3Dfy Anything in Images.arXiv preprint arXiv:2511.16624(2025). doi:10.48550/ arXiv.2511.16624

    arXiv doi:10.48550/arxiv.2511.16624 2025

  5. [5]

    Marks, and Jonathan Le Roux

    Anoop Cherian, Chiori Hori, Tim K. Marks, and Jonathan Le Roux. 2022. (2.5+1)D Spatio-Temporal Scene Graphs for Video Question Answering. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 444–453

    work page 2022

  6. [6]

    Meng Chu, Yicong Li, and Tat-Seng Chua. 2025. GraphVideoAgent: Enhancing Long-form Video Understanding with Entity Relation Graphs. InProceedings of the 33rd ACM International Conference on Multimedia. 4639–4648. doi:10.1145/ 3746027.3755537

    arXiv 2025

  7. [7]

    Samyak Datta, Sameer Dharur, Vincent Cartillier, Ruta Desai, Mukul Khanna, Dhruv Batra, and Devi Parikh. 2022. Episodic memory question answering. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 19097–19106

    work page 2022

  8. [8]

    Shangzhe Di and Weidi Xie. 2024. Grounded Question-Answering in Long Egocentric Videos. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 12934–12943

    2024

Show all 48 references
  1. [9]

    Jakob Engel, Kiran Somasundaram, Michael Goesele, Albert Sun, Alexander Gamino, et al. 2023. Project Aria: A New Tool for Egocentric Multi-Modal AI Research.arXiv preprint arXiv:2308.13561(2023). doi:10.48550/arXiv.2308.13561

    2023 doi arXiv

  2. [10]

    Michael Goesele, Daniel Andersen, Yujia Chen, Simon Green, Eddy Ilg, Chao Li, Johnson Liu, Grace Kuo, Logan Wan, and Richard Newcombe. 2025. Imaging for All-Day Wearable Smart Glasses.arXiv preprint arXiv:2504.13060(2025). doi:10.48550/arXiv.2504.13060

    2025 doi arXiv

  3. [11]

    Gabriele Goletto, Tushar Nagarajan, Giuseppe Averta, and Dima Damen. 2024. AMEGO: Active Memory from Long EGOcentric Videos. InComputer Vision – ECCV 2024. doi:10.1007/978-3-031-72624-8_6

    2024 doi

  4. [12]

    Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, et al. 2022. Ego4d: Around the world in 3,000 hours of egocentric video. In Proceedings of the IEEE/CVF conference on computer v...

    2022

  5. [13]

    Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi

    Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi. 2024. G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering.Advances in Neural Information Processing Systems37 (2024)

    2024

  6. [14]

    Nikita Karaev, Yuri Makarov, Jianyuan Wang, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. 2025. CoTracker3: Simpler and Better Point Tracking by Pseudo-Labeling Real Videos. In2025 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 1–10

    2025

  7. [15]

    Sebastian Koch, Narunas Vaskevicius, Mirco Colosi, Pedro Hermosilla, and Timo Ropinski. 2024. Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern ...

    2024

  8. [16]

    Rodriguez, and Jon E

    Jaewook Lee, Jun Wang, Elizabeth Brown, Liam Chu, Sebastian S. Rodriguez, and Jon E. Froehlich. 2024. GazePointAR: A Context-Aware Multimodal Voice Assistant for Pronoun Disambiguation in Wearable Augmented Reality. InPro- ceedings of the 2024 CHI Conference on Human Factors i...

    2024

  9. [17]

    Vincent Leroy, Yohann Cabon, and Jérôme Revaud. 2024. Grounding image matching in 3d with mast3r. InEuropean conference on computer vision. Springer, 71–91

    2024

  10. [18]

    Joshua Li, Fernando Jose Pena Cantu, Emily Yu, Alexander Wong, Yuchen Cui, and Yuhao Chen. 2025. SAMJAM: Zero-Shot Video Scene Graph Generation for Egocentric Kitchen Videos. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)....

    2025

  11. [19]

    Ke Ma and Jing Cao. 2019. Design Pattern as a Practical Tool for Designing Adaptive Interactions Connecting Human and Social Robots. InInternational Conference on Intelligent Human Systems Integration. Springer, 613–617. doi:10. 1007/978-3-030-11051-2_93

    2019

  12. [20]

    Ke Ma, Yizhou Fang, Jean-Baptiste Weibel, Shuai Tan, Xinggang Wang, Yang Xiao, Yi Fang, and Tian Xia. 2026. Phys-Liquid: A Physics-Informed Dataset for Estimating 3D Geometry and Volume of Transparent Deformable Liquids. In Proceedings of the AAAI Conference on Artificial Inte...

    2026 doi

  13. [21]

    Ke Ma, Cong Fu, Jianing Wang, Yifei Wang, Wenyuan Li, Xinggang Wang, Meng Wang, and Tian Xia. 2026. WPIS: From In-the-Wild Web Images to Physics- Aware 3D Scene Graphs for Physical Reasoning. InProceedings of the ACM Web Conference 2026. 1410–1421. doi:10.1145/3774904.3792591

    2026

  14. [22]

    Ke Ma, Yifei Wang, Meng Wang, and Tian Xia. 2026. TransBiolab: A Real-World Multi-View Dataset of Cluttered Transparent Biomedical Objects.arXiv preprint arXiv:2607.21071(2026). doi:10.48550/arXiv.2607.21071

    2026 doi arXiv

  15. [23]

    Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. 2023. EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Un- derstanding.Advances in Neural Information Processing Systems36 (2023), 46212– 46244

    2023

  16. [24]

    Costas Mavromatis and George Karypis. 2025. GNN-RAG: Graph Neural Retrieval for Efficient Large Language Model Reasoning on Knowledge Graphs. InFindings of the Association for Computational Linguistics: ACL 2025. 16682–16699. doi:10. 18653/v1/2025.findings-acl.856

    2025

  17. [25]

    Zhiyu Pan, Yinpeng Chen, Jiale Zhang, Hao Lu, Zhiguo Cao, and Weicai Zhong

  18. [26]

    Kevin Pu, Ting Zhang, Naveen Sendhilnathan, Sebastian Freitag, Raj Sodhi, and Tanya Jonker. 2025. ProMemAssist: Exploring Timely Proactive Assistance Through Working Memory Modeling in Multi-Modal Wearable Devices. InPro- ceedings of the 38th Annual ACM Symposium on User Inter...

    2025

  19. [27]

    Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman R"adle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Doll’ar, and Christoph Feichte...

    2024 doi arXiv

  20. [28]

    Ivan Rodin, Antonino Furnari, Kyle Min, Subarna Tripathi, and Giovanni Maria Farinella. 2024. Action Scene Graphs for Long-Form Understanding of Egocentric Videos. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 18622–18632

    2024

  21. [29]

    Jintian Shi and Ke Ma. 2017. Digital Touchpoints in Campus Slow Traffic Service System. InInternational Conference on Applied Human Factors and Ergonomics. Springer, 349–361

    2017

  22. [30]

    Aisha Urooj, Hilde Kuehne, Bo Wu, Kim Chheu, Walid Bousselham, Chuang Gan, Niels da Vitoria Lobo, and Mubarak Shah. 2023. Learning Situation Hyper-Graphs for Video Question Answering. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 1...

    2023

  23. [31]

    Johanna Wald, Helisa Dhamo, Nassir Navab, and Federico Tombari. 2020. Learn- ing 3D Semantic Scene Graphs From 3D Indoor Reconstructions. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 3961–3970

    2020

  24. [32]

    Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Re- vaud. 2024. Dust3r: Geometric 3d vision made easy. In2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 20697–20709

    2024

  25. [33]

    Xiayu Wang, Ke Ma, Ruiyun Zhong, Xinggang Wang, Yi Fang, Yang Xiao, and Tian Xia. 2024. Towards Dual Transparent Liquid Level Estimation in Biomedical Lab: Dataset, Methods and Practices. InEuropean Conference on Computer Vision. Springer, 198–214. doi:10.1007/978-3-031-73650-6_12

    2024 doi

  26. [34]

    Ziqin Wang, Bowen Cheng, Lichen Zhao, Dong Xu, Yang Tang, and Lu Sheng

  27. [35]

    Shun-Cheng Wu, Keisuke Tateno, Nassir Navab, and Federico Tombari. 2023. Incremental 3d semantic scene graph prediction from rgb sequences. In2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 5064–5074

    2023

  28. [36]

    In2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    Vl-sat: Visual-linguistic semantics assisted training for 3d semantic scene graph prediction in point cloud. In2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 21560–21569

  29. [37]

    Zizhao Wu, Haohan Li, Gongyi Chen, Zhou Yu, Xiaoling Gu, and Yigang Wang

  30. [38]

    Shun-Cheng Wu, Johanna Wald, Keisuke Tateno, Nassir Navab, and Federico Tombari. 2021. SceneGraphFusion: Incremental 3D Scene Graph Prediction From RGB-D Sequences. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 7515–7525

    2021

  31. [39]

    Chenyangguang Zhang, Alexandros Delitzas, Fangjinhua Wang, Ruida Zhang, Xiangyang Ji, Marc Pollefeys, and Francis Engelmann. 2025. Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern R...

    2025

  32. [40]

    Chaoyi Zhang, Xitong Yang, Ji Hou, Kris Kitani, Weidong Cai, and Fu-Jen Chu

  33. [41]

    Jingkang Yang, Shuai Liu, Hongming Guo, Yuhao Dong, Xiamengwei Zhang, Sicheng Zhang, Pengyun Wang, Zitang Zhou, Binzhu Xie, Ziyue Wang, Bei Ouyang, Zhengyu Lin, Marco Cominelli, Zhongang Cai, Yuanhan Zhang, Peiyuan Zhang, Fangzhou Hong, Joerg Widmer, Francesco Gringoli, Lei Ya...

    2025

  34. [42]

    Qicheng Zhao, Yu Li, Qi Sun, and Zheyu Yan. 2026. ResilPhase: Plug-and-Play Phase Mapping and Noise-Resilient Macro-Trajectory Extrapolation for Diffusion Acceleration.arXiv preprint arXiv:2606.26769(2026). doi:10.48550/arXiv.2606. 26769

    2026 doi arXiv

  35. [43]

    Yu Zhao, Hao Fei, Yixin Cao, Bobo Li, Meishan Zhang, Jianguo Wei, Min Zhang, and Tat-Seng Chua. 2023. Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling. InProceedings of the 31st ACM International Conference on Multimedia. 5281–5291. doi:10.114...

    2023

  36. [44]

    In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

    Egosg: Learning 3d scene graphs from egocentric rgb-d sequences. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2535–2545

    2024

  37. [45]

    Junrui Zhang, Jiaqi Li, Yachuan Huang, Yiran Wang, Jinghong Zheng, Liao Shen, and Zhiguo Cao. 2024. Towards Robust Monocular Depth Estimation in Non- Lambertian Surfaces. InComputer Vision – ECCV 2024 Workshops. 175–189

    2024

  38. [48]

    Xiangrong Zhu, Yuexiang Xie, Yi Liu, Yaliang Li, and Wei Hu. 2025. Knowledge graph-guided retrieval augmented generation. InProceedings of the 2025 Confer- ence of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies...

    2025

  39. [2023]

    InProceedings of the AAAI Conference on Artificial Intelligence, Vol

    Find Beauty in the Rare: Contrastive Composition Feature Clustering for Nontrivial Cropping Box Regression. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 2011–2019. doi:10.1609/aaai.v37i2.25293

    2011 doi

  40. [2024]

    InProceedings of the 32nd ACM International Conference on Multimedia

    3D Question Answering with Scene Graph Reasoning. InProceedings of the 32nd ACM International Conference on Multimedia. 1370–1378. doi:10.1145/ 3664647.3681517 MM ’26, November 10–14, 2026, Rio de Janeiro, Brazil. Ke Ma et al

    2026

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.