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REVIEW 3 major objections 5 minor 54 references

EgoHieraLoc: A Cortically Inspired Hierarchical Segmentation-Guided Framework for Egocentric Visual Query Localization

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read EgoHieraLoc claims that a single hierarchical, segmentation-guided pipeline can outperform specialized systems on both 2D and 3D visual query localization, by grounding the query in mask-based foreground/background parsing, refining it…

desk verdict The 2D branch is a solid, incremental engineering contribution, but the 3D branch has a load-bearing depth-scale alignment problem that unsecures the VQ3D SOTA claim as written. read the letter →

arxiv 2608.09656 v1 pith:DKF7ZVPE submitted 2026-08-10 cs.CV

Yifei Cao , Guolong Wang , Mingliang Hou , Xiya Bu , Daming Liu , Yu Liu This is my paper · ORCID
classification cs.CV
keywords VisualQueryLocalizationEgocentricVideoSegmentation-GuidedDiscriminativeCorrelationFilterMulti-ViewFusionGeometric-SemanticJointConfidenceHierarchicalPerception
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

The paper argues that a single framework modeled on the human visual hierarchy can solve both 2D and 3D visual query localization better than the specialized pipelines that currently hold the records. Its pipeline screens the query crop into foreground and background using segmentation priors, attends to the target with a closed-form discriminative correlation filter, restores fine object boundaries through multi-scale feedback, and then fuses per-frame 3D back-projections using a multiplicative geometric-semantic confidence so that only credible viewpoints contribute. The experiments report the best published numbers on both Ego4D-VQ2D and Ego4D-VQ3D, which matters because it suggests the two tasks share a perceptual foundation and do not need to be built as separate systems. A careful reader should note that the 3D numbers rest on an assumption that the up-to-scale depth estimates are scale-compatible with the reconstruction used for camera poses.

What carries the argument

The machinery has four parts. The Discriminative Parsing Module (DPM) takes the SAM-derived binary mask of the query crop, partitions query features into foreground and background sets, computes top-K cosine similarities against search-region features, and outputs a foreground map plus a likelihood-channel map. The Query-Aware Module (QAM) builds a ridge-regression correlation filter with a closed-form frequency-domain solution, detects multiple response peaks via connected components, smooths instantaneous and cumulative deformations, and refines the peak with a dynamic ellipse prior. The Regional Adaptation Module (RAM) aggregates four-scale pyramidal-transformer features, with a dimensional-collapse block replacing full attention, to restore the final segmentation. The Geometric-Semantic Joint Confidence (GSJC) weight multiplicatively couples mask confidence statistics with depth variance, reprojection error, and triangulation baseline quality, then takes a weighted mean of back-projected 3D points. Each component enforces one stage of the hierarchy: screening, attention, feedback refinement, and credibility-weighted fusion.

What would settle it

On a validation sequence with ground-truth 3D annotations, back-project the query point through the pipeline's depth and poses for several frames, then fit the single scale factor s that best aligns these back-projected points to the ground-truth world coordinates; if the best-fitting s varies from frame to frame by more than roughly 5% around its sequence median, the consistent-scale premise is false and the reported L2 and Success values mix localization error with scale error.

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Extended reading notes

Core claim

Stated on the paper's own terms, the discovery is that the four competencies of human vision—foreground screening, attentive selection, feedback-based refinement, and reliability-weighted integration across viewpoints—can be instantiated as three cooperating 2D modules plus a geometry-aware 3D fusion scheme, and that this hierarchy is what allows a query object to be re-localized reliably in egocentric video where boundaries are ambiguous. The Discriminative Parsing Module uses SAM masks to derive foreground and background feature sets and produce a foreground map and a likelihood-channel map; the Query-Aware Module applies a correlation filter with multi-peak detection, deformation smoothing, and an elliptical shape prior; the Regional Adaptation Module aggregates multi-scale pyramid features to restore the final mask. In 3D, candidate frames are back-projected through estimated poses and monocular depth, and the Geometric-Semantic Joint Confidence weights each candidate multiplicatively by mask confidence, depth consistency, reprojection consistency, and baseline quality before averaging the 3D positions. The consequence is a unified VQL-2D and VQL-3D system that reports state-of-the-art results on Ego4D-VQ, with test-server tAP25 of 0.44 and stAP25 of 0.37 on the 2D task and Success of 87.50 and QwP of 90.91 on the 3D task.

Load-bearing premise

The 3D branch assumes that the up-to-scale depth maps from the monocular depth estimator are at the same scale as the structure-from-motion reconstruction that supplies camera poses, so the back-projected 3D points and the displacement vectors are measured in consistent metric units.

Editorial extensions

If this is right

  • Improving the 2D branch directly improves 3D placement, since the paper's ablations and failure analysis identify biased 2D responses as the dominant source of 3D displacement error.
  • Multiplicative reliability weighting outperforms uniform or single-cue aggregation: on the VQ3D validation set, the full GSJC scheme raises Success from 78.13% to 82.25% and lowers L2 error from 1.66 to 1.30 relative to no aggregation.
  • The same 2D branch transfers to egocentric tracking with only small adaptations and beats the previous state of the art on EgoTracks and TREK-150, indicating the design is not merely tuned to the VQL benchmark.
  • Because the correlation filter is solved in closed form, its loss decline during training reflects the backbone learning more discriminative features rather than the filter adapting, so the filter stays a lightweight, interpretable component.

Reading between the lines

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

  • In the paper's validation ablations, QwP stays pinned at 84.48% no matter which GSJC components are active, because it counts pose availability rather than localization accuracy; a natural extension is to couple GSJC with a pose-recovery module so the ceiling itself can be raised.
  • The up-to-scale depth assumption is testable directly: fitting one scale factor to align back-projected points with the ground-truth scan per sequence would reveal whether any 3D gains come from genuine localization or from scale goodwill; the paper never performs this control.
  • The paper's information flow is strictly one-way, so 3D consensus never corrects a 2D failure; a bidirectional loop, in which the multi-view agreement filters or re-fires 2D detections, would be the logical next step given the paper's own failure cases.
  • The paper credits segmentation priors for the gains over region-level and tracking baselines, but its ablations vary module presence rather than mask quality; replacing SAM masks with coarser box priors would isolate how much of the advantage is the pixel-level boundary information itself.
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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 / 5 minor

Summary. EgoHieraLoc proposes a unified framework for egocentric visual query localization in both 2D and 3D. The 2D branch combines a Discriminative Parsing Module (DPM) built on SAM masks, a Query-Aware Module (QAM) based on closed-form discriminative correlation filters with multi-peak detection and an elliptical deformable constraint, and a Regional Adaptation Module (RAM) that fuses multi-scale transformer features to refine masks. The 3D branch lifts the 2D mask centroid through camera poses and per-frame monocular depth from Depth Anything, then aggregates back-projected candidates with the Geometric-Semantic Joint Confidence (GSJC) weighting scheme, which multiplies segmentation confidence with local depth-consistency, reprojection-consistency, and triangulation-baseline terms. The paper reports state-of-the-art results on the Ego4D-VQ2D and VQ3D test servers (tAP25=0.44, stAP25=0.37; Succ=87.50, QwP=90.91), on the validation sets, and on the EgoTracks and TREK-150 tracking benchmarks, supported by extensive ablations, multi-run statistical significance tests, and a training-dynamics analysis.

Significance. If the 3D branch is technically sound, the paper would be a useful step toward unifying 2D and 3D visual query localization: the modular design is clearly motivated by a hierarchical perception story, the DCF component is presented with a closed-form frequency-domain solution, the ablations isolate contributions of each module, and the validation/test consistency suggests the VQ2D results are not overfit. The multi-run significance test in Appendix A and the explicit VISOR-vs-architecture decomposition in Appendix D.1 are commendable and should be credited. The main significance hinges on the VQ3D claim, however, and that claim currently rests on an unstated and unverified relationship between monocular depth scale and COLMAP pose scale.

major comments (3)
  1. [§3.5, Eq. (22), §3.6 Eqs. (31)-(32), Appendix C] The derivation that "absolute metric depth is neither necessary nor beneficial" conflates the scale of the COLMAP pose reconstruction with the scale of the per-frame depth map. Appendix C shows that model_aligner aligns COLMAP poses to the Matterport/Metric coordinate frame, but no step aligns the Depth Anything depth values to that same metric frame. Writing D_i = alpha_i * d_true,i + beta_i with frame-dependent scale and shift, the back-projection in Eq. (22) does not produce metric world points; even for constant alpha_i = alpha and beta_i = 0, the weighted mean in Eq. (31) estimates alpha * p_true + (1 - alpha) * c_bar, where c_bar is a weighted mean camera center. Consequently delta in Eq. (32) is not the metric displacement from the query camera unless alpha = 1, and the reported VQ3D L2 and Success metrics have no metric interpretation without an explicit alignment step. No such step appears in Sections 3.5, 3.6, or Appendix C. The authors should either add a per-frame scale/shift alignment to COLMAP sparse depth or scan depth, or empirically demonstrate on the validation set that the adopted Depth Anything confidence-weighted depth is already consistent with the COLMAP/Matterport scale; the latter requires reporting the distribution of fitted scale factors and the resulting residual metric error.
  2. [§3.5, Eq. (21), §3.6 Eq. (23)] The quantity pr_i(x,y) is load-bearing but never defined. In Eq. (21) it is called a per-pixel confidence score used to aggregate the depth value, and in Eq. (23) it is treated as a per-pixel probability whose average, threshold concentration, and maximum define the semantic confidence. If pr_i(x,y) is the RAM segmentation probability, this should be stated explicitly; if it is a separate depth-confidence term, its source and the relationship to the segmentation mask need to be specified. The current ambiguity makes Eqs. (21)-(24) impossible to reproduce and is directly relevant to the claimed benefit of GSJC.
  3. [§3.6, Eqs. (26)-(28)] The reprojection-consistency weight for candidate frame i is computed against the mean of the same candidate 3D points that are being weighted in Eq. (31). This self-reference creates a potential bias: a grossly inaccurate frame can pull the consensus point p_bar toward itself, reducing its own reprojection error and increasing its own weight. The authors should quantify this effect, for example by reporting a leave-one-out version of the reprojection weight, or should justify that the number of candidates N_C is large enough to dilute single-outlier contamination before the final aggregation.
minor comments (5)
  1. [§3.3, Eq. (16)] The distance metric in Eq. (16) adds a Euclidean pixel distance to a dimensionless ellipse-normalized term. Since the two terms have different units, the effect of lambda_e is resolution-dependent; please normalize the second term by a pixel-scale factor or define the whole metric in normalized coordinates.
  2. [§3.3 and Appendix D.6] The main text states that removing the elliptical constraint degrades performance by 1.19%, while Appendix D.6 reports a degradation from 62.30% to 60.39% (1.91%). These numbers should be reconciled.
  3. [Appendix H, Fig. G.17] The text contains an unresolved "Fig.??" reference in the FLOPs/parameter discussion; the figure citation should be fixed.
  4. [Appendix B, Table B.7] The dagger convention indicating fine-tuning on EgoTracks is applied to several baselines but not to EgoHieraLoc; the authors should state explicitly whether their model was fine-tuned on EgoTracks/TREK-150 or used as-is, so that the comparison protocol is unambiguous.
  5. [§3.6, Eq. (23)] The definition of P_thr divides by n, the number of pixels exceeding the threshold; if no pixel exceeds the threshold, n is zero. A small additive safeguard should be introduced.

Circularity Check

1 steps flagged · score 2.0 of 10

No circular derivation; only a mild self-referential multi-view weighting in GSJC.

  1. self definitional [Section 3.6, Eqs. (26)-(31)]
    "We compute their mean: p̄=1/NC Σ [x_j,y_j,z_j]^T ... The reprojection weight is: ϖreproj_i = exp(−∥[u_i,v_i]^T−[û_i,v̂_i]^T∥_2/ϵ_r) ... The final 3D position is computed as a confidence-weighted mean: [x̂,ŷ,ẑ]^T = Σ ϑ̃_j [x_j,y_j,z_j]^T / Σ ϑ̃_j."

    The reprojection-consistency weight is defined from p̄, the unweighted mean of the exact same back-projected points that Eq. (31) then averages. An outlier point can shift p̄ toward itself, which tends to reduce its own reprojection error in Eq. (28) and raise its own weight in Eq. (30). The 'geometric credibility' is therefore not independent evidence about the points being aggregated; it is a self-consistency score computed from the very estimate it is used to form. This is a mild self-reference rather than a full reduction: the final output is not a fitted parameter, the benchmark result is not forced, and the 2D branch and external comparisons remain informative.

full rationale

The central derivations are self-contained. The 2D pipeline (DPM, QAM, RAM) uses SAM masks, closed-form DCF filters, and multi-scale feature fusion; none of these are defined in terms of the benchmark targets, and VQL-2D is evaluated on held-out test data. The 3D branch back-projects Depth Anything depth through COLMAP poses and applies GSJC weighting; the reported state-of-the-art numbers are not obtained by fitting ground-truth labels. The only self-reference found is the GSJC reprojection-consistency factor, which is computed from the same back-projected points it weights—a mild consensus-filter bias, but not a reduction of the prediction to its input. The scale-compatibility assumption between relative depth and COLMAP poses (Section 3.5) is a genuine correctness risk, but it is not circular: Eqs. (22)-(32) do not become equivalent to their inputs by construction. No load-bearing self-citation was found; the same-author references are not used to justify the core derivation. Overall circularity is limited to the local GSJC self-reference, hence score 2.

Assumptions & free parameters 13 free parameters · 4 assumptions · 0 invented entities

The framework introduces no new physical entities. It relies on several free parameters tuned on the validation set, and on domain assumptions about the reliability of SAM, COLMAP, and Depth Anything. The most fragile assumption is the implicit depth-scale consistency between monocular relative depth and the COLMAP reconstruction.

free parameters (13)
  • K_DPM = 3
    Top-K aggregation size in the DPM (Eq. 3), set by hand.
  • alpha_QAM = 1.0
    Sensitivity parameter for the adaptive threshold in QAM (Sec. 3.3), tuned on the validation set.
  • gamma_QAM = 0.4
    Decay factor for cumulative deformation (Eq. 11), tuned on validation.
  • eta_QAM = 0.7
    Smoothing strength for ellipse semi-axes (Eq. 13), tuned on validation.
  • lambda_e = 0.5
    Weight of the elliptical constraint in the distance metric (Eq. 16), tuned on validation.
  • K_QAM = 5
    Number of candidate peaks retained in QAM, selected by ablation (Fig. 6c).
  • sigma_0 = dataset median depth variance
    Scale for depth consistency weight (Sec. 3.6), computed from validation frames.
  • tau_SAM = 0.6
    Threshold for triggering SAM fallback levels (Appendix E), calibrated on validation set.
  • beta_QAM = 1.5
    Weight gain applied to QAM in fallback Level 2 (Appendix E), chosen by hand.
  • loss weights eta_1..eta_4 = 0.1, 0.4, 0.1, 0.2
    Weights for the composite loss (Eq. 33), set empirically.
  • lambda_pdo = 0.3
    Down-weighting coefficient for pseudo-mask segmentation loss (Eq. 37), chosen by hand.
  • temporal peak threshold = 0.8
    Fraction of the maximum peak used to filter the temporal score sequence (Sec. 4.1 inference stage), chosen by hand.
  • epsilon_r = max(10, 0.02*bbox_diag)
    Reprojection tolerance in Eq. (28), a heuristic scale-dependent choice.
assumptions (4)
  • domain assumption SAM produces accurate foreground/background separation for query crops
    The DPM relies on the quality of SAM masks to construct foreground and background feature sets M_O and M_B (Eq. 1). A fallback mechanism exists but assumes the SAM confidence score s_SAM is a reliable indicator of mask quality.
  • standard math The closed-form DCF solution is valid and end-to-end training learns discriminative features
    The ridge regression objective (Eq. 4) has a standard frequency-domain closed-form solution (Eq. 5), following the differentiable-DCF paradigm. This is a standard mathematical result.
  • domain assumption COLMAP pose estimation and Sim3 alignment to the Matterport coordinate system are accurate
    All VQ3D results depend on the camera poses from COLMAP and the Sim3 alignment to the Matterport world frame (Appendix C). Pose errors directly affect the back-projection and the reported 3D metrics.
  • ad hoc to paper Depth Anything relative depth is scale-consistent with the COLMAP reconstruction
    Section 3.5 asserts that absolute metric depth is unnecessary because COLMAP imposes a consistent global scale. This assumes the monocular relative depth scale matches the SfM scale without any described alignment or calibration, which is load-bearing for metric 3D evaluation.

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Cite this review

Pith. "Pith review of EgoHieraLoc: A Cortically Inspired Hierarchical Segmentation-Guided Framework for Egocentric Visual Query Localization." pith.science (2026). https://pith.science/paper/DKF7ZVPE

@misc{pith2026260809656,
  author       = {Pith},
  title        = {Pith review of: EgoHieraLoc: A Cortically Inspired Hierarchical Segmentation-Guided Framework for Egocentric Visual Query Localization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DKF7ZVPE}},
  note         = {Machine review of arXiv:2608.09656}
}
read the original abstract

Visual query localization (VQL) aims to retrieve and re-localize a queried object in egocentric videos, yet remains challenging when object boundaries are ambiguous and global context cannot effectively guide fine-grained localization. Human vision handles such ambiguity through a hierarchical process: it rapidly screens foreground candidates, selectively attends to the target despite distractors, refines perception via feedback between global context and local detail, and, when a single view is unreliable, integrates evidence across viewpoints according to its credibility. Inspired by these competencies, we propose \textbf{EgoHieraLoc}, a unified framework for VQL-2D and VQL-3D. A Discriminative Parsing Module first extracts foreground-aware query representations using segmentation priors; a Query-Aware Module then performs robust target localization through discriminative correlation filtering with deformable modeling; and a Regional Adaptation Module feeds multi-scale context back into local regions to recover precise object boundaries. To extend this perceptual hierarchy to 3D localization, we introduce Geometric-Semantic Joint Confidence (GSJC), which multiplicatively couples segmentation confidence with local depth consistency, multi-view back-projection consistency, and triangulation-baseline quality, so that a viewpoint contributes to the 3D estimate only when it is credible both semantically and geometrically. Extensive experiments demonstrate state-of-the-art performance on both VQL-2D and -3D benchmarks.

Figures

Figures reproduced from arXiv: 2608.09656 by the authors.

Figure 1
Figure 1. The framework of the EgoHieraLoc. (a) represents the overall architecture of EgoHieraLoc, with the content of the dashed box indicating the 2D task branch. This branch consists of three key components: the Discriminative Parsing Module (b, blue block), the Query-Aware Module (QAM) (c, yellow block), and the Regional Adaptation Module (RAM) (see Fig.2). These modules collaboratively establish semantic associations be… view at source ↗
Figure 2
Figure 2. The architecture of RAM. The backbone adopts a pyramidal structure, containing four stages, each integrating an encoder module and progressively downsampling to reduce resolution via a dimensional collapse module (DCM). The RAM (bottom dashed box) receives the cascaded DPM and QAM outputs, fuses multi-level features through convolution and upsampling modules, and gradually restores the output to the original resolut… view at source ↗
Figure 3
Figure 3. Qualitative visualization results on Ego4D-VQ2D.. Each row shows a query, its corresponding response, and the temporal confidence curve of our work. Green, red, and blue boxes represent the ground truth, EgoHieraLoc, and RELOCATE, respectively. The green shaded region denotes the GT interval. Inference stage5 . The video is processed in clips, and the predictions are concatenated. Given that bbox generation primaril… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Visualization of VQ3D results across four reconstructed Matterport Scan scenes. Ground-Truth (white), EgoLoc (blue), and our predictions (red) are projected into 3D scenes reconstructed via COLMAP, with the reconstructed scan coordinate system and visual queries displa…
Figure 5
Figure 5. Figure 5: Qualitative analysis of heatmaps for key components. Each example illustrates four levels of in￾termediate representations across temporally consecutive egocentric video frames. Row 1: egocentric video frames. Row 2: response heatmaps generated by DPM, highlighting can…
Figure 6
Figure 6. Figure 6: Ablations on QAM components, k-frame skipping, and Top-KQAM. (a) Ablation studies of QAM modules on Ego4D-VQ2D. (b) Frame k-skipping ablation. Different k-values affect FPS/Success on VQ2D validation set. (c) Sensitivity analysis on the number of candidates K. On VQ2D,…

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Reference graph

Works this paper leans on

54 extracted references · 43 canonical work pages

  1. [1]

    Plizzari, G

    C. Plizzari, G. Goletto, A. Furnari, S. Bansal, F. Ragusa, G. M. Farinella, D. Damen, T. Tommasi, An outlook into the future of egocentric vision, Inter- national Journal of Computer Vision (2024) 1–57

    work page 2024

  2. [2]

    Grauman, A

    K. Grauman, A. Westbury, E. Byrne, Z. Chavis, A. Furnari, R. Girdhar, J. Ham- burger, H. Jiang, M. Liu, X. Liu, et al., Ego4d: Around the world in 3,000 hours of egocentric video, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 18995–19012

    work page 2022

  3. [3]

    Khosla, A

    S. Khosla, A. Schwing, D. Hoiem, et al., Relocate: A simple training-free base- line for visual query localization using region-based representations, in: Pro- ceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 3697–3706

    work page 2025

  4. [4]

    Y . Cao, Y . Liu, G. Wang, Z. Liu, K. Wang, X. Zhang, J. Yu, X. Tu, Eagle: Episodic appearance-and geometry-aware memory for unified 2d-3d visual query localiza- tion in egocentric vision, in: Proceedings of the AAAI Conference on Artificial Intelligence, V ol. 40, 2026, pp. 2634–2642

    work page 2026

  5. [5]

    J. Mai, C. Zhao, A. Hamdi, S. Giancola, B. Ghanem, Estimating more camera poses for ego-centric videos is essential for vq3d, arXiv preprint arXiv:2211.10284 (2022)

    work page Pith review arXiv 2022

  6. [6]

    EgoCOL: Egocentric Camera pose estimation for Open-world 3D object Localization @Ego4D challenge 2023

    C. Forigua, M. Escobar, J. Pont-Tuset, K.-K. Maninis, P. Arbeláez, Egocol: Ego- centric camera pose estimation for open-world 3d object localization@ ego4d challenge 2023, arXiv preprint arXiv:2306.16606 (2023)

    work page Pith review arXiv 2023

  7. [7]

    M. Xu, Y . Li, C.-Y . Fu, B. Ghanem, T. Xiang, J.-M. Pérez-Rúa, Where is my wallet? modeling object proposal sets for egocentric visual query localization, 29 in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 2593–2603

    work page 2023

  8. [8]

    J. Mai, A. Hamdi, S. Giancola, C. Zhao, B. Ghanem, Egoloc: Revisiting 3d object localization from egocentric videos with visual queries, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 45–57

    work page 2023

Show all 54 references
  1. [9]

    Jiang, S

    H. Jiang, S. K. Ramakrishnan, K. Grauman, Single-stage visual query localiza- tion in egocentric videos, Advances in Neural Information Processing Systems 36 (2024)

    2024

  2. [10]

    B. Fan, Y . Feng, Y . Tian, Y . Lin, Y . Huang, H. Fan, Prvql: Progressive knowledge- guided refinement for robust egocentric visual query localization, arXiv preprint arXiv:2502.07707 (2025)

    2025 arXiv

  3. [11]

    Chang, S

    J. Chang, S. Hong, H. Lee, S. J. Ha, D. Lee, S. T. Kim, J. Choi, Hero-vql: Hierar- chical, egocentric and robust visual query localization (2025). arXiv:2509.00385

    2025 arXiv

  4. [12]

    Y . Cao, G. Wang, M. Hou, J. Yu, X. Zhang, X. Bu, Z. Li, Y . Liu, Biovql: Brain- inspired one-stage egocentric visual query localization, in: Proceedings of the 2026 International Conference on Multimedia Retrieval, 2026, pp. 88–98

    2026

  5. [13]

    Kirillov, E

    A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y . Lo, et al., Segment anything, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 4015– 4026

    2023

  6. [14]

    D. S. Bolme, J. R. Beveridge, B. A. Draper, Y . M. Lui, Visual object tracking using adaptive correlation filters, in: 2010 IEEE computer society conference on computer vision and pattern recognition, IEEE, 2010, pp. 2544–2550

    2010

  7. [15]

    Dunnhofer, A

    M. Dunnhofer, A. Furnari, G. M. Farinella, C. Micheloni, Is first person vision challenging for object tracking?, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 2698–2710. 30

    2021

  8. [16]

    Dunnhofer, A

    M. Dunnhofer, A. Furnari, G. M. Farinella, C. Micheloni, Visual object tracking in first person vision, International Journal of Computer Vision 131 (1) (2023) 259–283

    2023

  9. [17]

    Zhang, Y

    S. Zhang, Y . Sui, S. Zhao, L. Zhang, Graph-regularized structured support vec- tor machine for object tracking, IEEE Transactions on Circuits and Systems for Video Technology 27 (6) (2015) 1249–1262

    2015

  10. [18]

    J. Zhu, X. Chen, P. Zhang, X. Wang, D. Wang, W. Zhao, H. Lu, Srrt: Exploring search region regulation for visual object tracking, IEEE Transactions on Circuits and Systems for Video Technology 34 (11) (2024) 10551–10563

    2024

  11. [19]

    C. Liu, J. Zhao, C. Bo, S. Li, D. Wang, H. Lu, Lgtrack: Exploiting local and global properties for robust visual tracking, IEEE Transactions on Circuits and Systems for Video Technology 34 (9) (2024) 8161–8171

    2024

  12. [20]

    Bertinetto, J

    L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, P. H. Torr, Fully- convolutional siamese networks for object tracking, in: Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II 14, Springer, 2016, pp. 850–865

    2016

  13. [21]

    B. Li, J. Yan, W. Wu, Z. Zhu, X. Hu, High performance visual tracking with siamese region proposal network, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 8971–8980

    2018

  14. [22]

    B. Li, W. Wu, Q. Wang, F. Zhang, J. Xing, J. Yan, Siamrpn++: Evolution of siamese visual tracking with very deep networks, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 4282–4291

    2019

  15. [23]

    Y . Xu, Z. Wang, Z. Li, Y . Yuan, G. Yu, Siamfc++: Towards robust and accurate visual tracking with target estimation guidelines, in: Proceedings of the AAAI conference on artificial intelligence, V ol. 34, 2020, pp. 12549–12556. 31

    2020

  16. [24]

    W. Hu, Q. Wang, L. Zhang, L. Bertinetto, P. H. Torr, Siammask: A framework for fast online object tracking and segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (3) (2023) 3072–3089

    2023

  17. [25]

    B. Yan, H. Peng, J. Fu, D. Wang, H. Lu, Learning spatio-temporal transformer for visual tracking, in: Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 10448–10457

    2021

  18. [26]

    J. Wu, Y . Jiang, S. Bai, W. Zhang, X. Bai, Seqformer: Sequential transformer for video instance segmentation, in: European Conference on Computer Vision, Springer, 2022, pp. 553–569

    2022

  19. [27]

    Lukezic, J

    A. Lukezic, J. Matas, M. Kristan, D3s-a discriminative single shot segmentation tracker, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 7133–7142

    2020

  20. [28]

    L. Yang, Y . Fan, N. Xu, Video instance segmentation, CoRR abs/1905.04804 (2019). URLhttps://arxiv.org/abs/1905.04804

    2019 arXiv

  21. [29]

    V oigtlaender, Y

    P. V oigtlaender, Y . Chai, F. Schroff, H. Adam, B. Leibe, L.-C. Chen, Feelvos: Fast end-to-end embedding learning for video object segmentation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 9481–9490

    2019

  22. [30]

    Y . Wang, Z. Xu, X. Wang, C. Shen, B. Cheng, H. Shen, H. Xia, End-to-end video instance segmentation with transformers, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 8741–8750

    2021

  23. [31]

    N. Ravi, V . Gabeur, Y .-T. Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. Rädle, C. Rol- land, L. Gustafson, et al., Sam 2: Segment anything in images and videos, arXiv preprint arXiv:2408.00714 (2024)

    2024 arXiv

  24. [32]

    V oigtlaender, J

    P. V oigtlaender, J. Luiten, P. H. Torr, B. Leibe, Siam r-cnn: Visual tracking by re-detection, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 6578–6588. 32

    2020

  25. [33]

    Xu, C.-Y

    M. Xu, C.-Y . Fu, Y . Li, B. Ghanem, J.-M. Perez-Rua, T. Xiang, Nega- tive frames matter in egocentric visual query 2d localization, arXiv preprint arXiv:2208.01949 (2022)

    2022 arXiv

  26. [34]

    Ullman, The interpretation of structure from motion, Proceedings of the Royal Society of London

    S. Ullman, The interpretation of structure from motion, Proceedings of the Royal Society of London. Series B. Biological Sciences 203 (1153) (1979) 405–426

    1979

  27. [35]

    Patra, K

    S. Patra, K. Gupta, F. Ahmad, C. Arora, S. Banerjee, Ego-slam: A robust monocu- lar slam for egocentric videos, in: 2019 IEEE Winter Conference on Applications of Computer Vision (W ACV), IEEE, 2019, pp. 31–40

    2019

  28. [36]

    Tschernezki, D

    V . Tschernezki, D. Larlus, A. Vedaldi, Neuraldiff: Segmenting 3d objects that move in egocentric videos, in: 2021 International Conference on 3D Vision (3DV), IEEE, 2021, pp. 910–919

    2021

  29. [37]

    Mildenhall, P

    B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, R. Ng, Nerf: Representing scenes as neural radiance fields for view synthesis, Commu- nications of the ACM 65 (1) (2021) 99–106

    2021

  30. [38]

    Q. Gu, Z. Lv, D. Frost, S. Green, J. Straub, C. Sweeney, Egolifter: Open-world 3d segmentation for egocentric perception, arXiv preprint arXiv:2403.18118 (2024)

    2024 arXiv

  31. [39]

    S. Wang, V . Leroy, Y . Cabon, B. Chidlovskii, J. Revaud, Dust3r: Geometric 3d vision made easy, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 20697–20709

    2024

  32. [40]

    Zhang, G

    D. Zhang, G. Li, J. Li, M. Bressieux, O. Hilliges, M. Pollefeys, L. Van Gool, X. Wang, Egogaussian: Dynamic scene understanding from egocentric video with 3d gaussian splatting, arXiv preprint arXiv:2406.19811 (2024)

    2024 arXiv

  33. [41]

    Kerbl, G

    B. Kerbl, G. Kopanas, T. Leimkühler, G. Drettakis, 3d gaussian splatting for real- time radiance field rendering., ACM Trans. Graph. 42 (4) (2023) 139–1

    2023

  34. [42]

    L. Yang, B. Kang, Z. Huang, X. Xu, J. Feng, H. Zhao, Depth anything: Unleash- ing the power of large-scale unlabeled data, in: Proceedings of the IEEE/CVF 33 Conference on Computer Vision and Pattern Recognition, 2024, pp. 10371– 10381

    2024

  35. [43]

    Danelljan, G

    M. Danelljan, G. Bhat, F. S. Khan, M. Felsberg, Atom: Accurate tracking by overlap maximization, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 4660–4669

    2019

  36. [44]

    G. Bhat, M. Danelljan, L. V . Gool, R. Timofte, Learning discriminative model prediction for tracking, in: Proceedings of the IEEE/CVF international confer- ence on computer vision, 2019, pp. 6182–6191

    2019

  37. [45]

    W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, L. Shao, Pyramid vision transformer: A versatile backbone for dense prediction without convolutions, in: Proceedings of the IEEE/CVF international conference on com- puter vision, 2021, pp. 568–578

    2021

  38. [46]

    Darkhalil, D

    A. Darkhalil, D. Shan, B. Zhu, J. Ma, A. Kar, R. Higgins, S. Fidler, D. Fouhey, D. Damen, Epic-kitchens visor benchmark: Video segmentations and object re- lations, Advances in Neural Information Processing Systems 35 (2022) 13745– 13758

    2022

  39. [47]

    Loshchilov, F

    I. Loshchilov, F. Hutter, Decoupled weight decay regularization, in: International Conference on Learning Representations

  40. [48]

    J. L. Schonberger, J.-M. Frahm, Structure-from-motion revisited, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 4104–4113

    2016

  41. [49]

    K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778

    2016

  42. [50]

    H. Tang, K. J. Liang, K. Grauman, M. Feiszli, W. Wang, Egotracks: A long- term egocentric visual object tracking dataset, Advances in Neural Information Processing Systems 36 (2024). 34

    2024

  43. [51]

    K. Dai, Y . Zhang, D. Wang, J. Li, H. Lu, X. Yang, High-performance long- term tracking with meta-updater, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 6298–6307

    2020

  44. [52]

    Huang, X

    L. Huang, X. Zhao, K. Huang, Globaltrack: A simple and strong baseline for long-term tracking, in: Proceedings of the AAAI conference on artificial intelli- gence, V ol. 34, 2020, pp. 11037–11044

    2020

  45. [53]

    Mayer, M

    C. Mayer, M. Danelljan, G. Bhat, M. Paul, D. P. Paudel, F. Yu, L. Van Gool, Transforming model prediction for tracking, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 8731–8740

    2022

  46. [54]

    re-awaken

    Y . Cui, C. Jiang, L. Wang, G. Wu, Mixformer: End-to-end tracking with iterative mixed attention, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 13608–13618. 35 Supplementary Material Appendix A. Statistical Significance Valida...

    2022

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.