Pith. sign in

REVIEW 4 major objections 6 minor 21 references

Experimental Evaluation of Static Image Sub-Region-Based Search Models Using CLIP

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Cutting candidate frames into fixed spatial tiles and matching text queries only to the relevant tiles improves CLIP recall at rank 10 and rank 100 by nearly 50 percent on homogeneous underwater footage.

desk verdict A careful, honestly-caveated evaluation of static grid sub-region CLIP retrieval on a new 741-annotation underwater dataset; the core trick is prior work but the analysis is solid, with the main caveat being the perfect-memory upper-bound setting that the authors themselves flag. read the letter →

arxiv 2506.06938 v1 pith:TXHKRCG6 submitted 2025-06-07 cs.MM cs.CV

Bastian Jäckl , Vojtěch Kloda , Daniel A. Keim , Jakub Lokoč This is my paper
classification cs.MMcs.CV
keywords CLIPsub-regionsearchstaticgridpartitioningunderwatervideoretrievalmarineimagetext-imageknown-itemboundingboxrobustness
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 asks whether a simple preprocessing step, cutting every candidate frame into fixed spatial tiles and matching a text query only against tiles that overlap the user's region-of-interest box, can rescue CLIP-based retrieval in visually homogeneous domains. Using 741 human annotations over underwater keyframes from the Marine Video Kit, the authors show that a 5-cell grid (four quadrants plus a center rectangle) and a 9-cell 3x3 grid each improve R@10 and R@100 by nearly 50 percent over whole-image retrieval, for both short and long queries. They also show that merely appending a positional phrase to the text query does not help, and that adding 10 to 20 percent overlap between grid cells makes the gains robust to shifted or resized boxes. The finding matters because vague text plus coarse location is often all a non-expert has in specialized domains, and static grids require no extra training or per-query cropping.

What carries the argument

The load-bearing object is the static grid: Static-5 (a 2x2 set of quadrants plus a center rectangle covering the middle half of the image) and Static-9 (a uniform 3x3 grid). Each grid cell receives a precomputed CLIP image embedding, and at query time the image score is the maximum cosine similarity over cells whose intersection-over-union with the annotation box is positive. The grid does two jobs: it isolates the content the text describes (better localization), and it prevents irrelevant textually similar content in other parts of candidate frames from inflating similarity (noise filtering). Appending a positional suffix such as "top right" to the text instead of using spatial cells fails, because the phrase matches false candidates as often as true ones.

What would settle it

Run a memory-based known-item search in which users see a target frame, then later write a description and place a region box without seeing the image, and compare Static-9 with overlap against Whole-Image on R@10 across a few hundred queries; if the gain fails to reach statistical significance, the practical claim of improvement collapses.

Watch

Extended reading notes

Core claim

The central claim is that static spatial partitioning of candidate images significantly improves retrieval in homogeneous domains such as underwater footage. The paper establishes this with a dataset of 741 localized text annotations: Static-5 and Static-9 improve R@10 and R@100 by nearly half over the Whole-Image baseline in both the skippable and non-skippable subsets. The improvement is not an artifact of filtering alone: when only the target image is embedded at grid-cell resolution while all other candidates use whole-image embeddings, ranks still improve substantially, and CLIP similarity to the target rises (Wilcoxon signed rank test, p < 0.001), with a strong correlation (-0.69) between per-query gains in similarity and gains in rank. A theoretical oracle that crops every candidate at the exact annotation box reaches only about 41% R@100, indicating both the potential and the ceiling of location-aware CLIP retrieval.

Load-bearing premise

All conclusions rest on the assumption that users can specify a region roughly like the pixel-accurate boxes drawn by annotators while looking at the image; the paper explicitly labels its numbers an upper bound because real users remember scenes imperfectly.

Editorial extensions

If this is right

  • Static partitioning can be layered onto any existing CLIP retrieval pipeline at indexing time, since embeddings are computed once per cell and no per-query cropping is needed.
  • In homogeneous collections, a system can offer users a five- or nine-cell canvas for indicating where something is and expect R@10 and R@100 gains of roughly half over whole-image text search.
  • Text prompts that merely name a location should be avoided: on this data they perform no better than, and usually worse than, plain whole-image queries.
  • Moderate cell overlap (10 to 20 percent of grid size) is a cheap insurance against imprecise region estimates, and finer grids benefit the most from it.
  • Even with a perfect oracle box, recall at rank 100 stays around 41 percent, so location alone does not solve the text-query ranking bottleneck in this domain.

Reading between the lines

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

  • The authors' own upper-bound caveat suggests the first decisive follow-up: collect queries from users who saw a target earlier and must draw the region box from memory, then compare Static-9 with overlap against Whole-Image; if the R@10 advantage shrinks below significance, the practical gain is smaller than this paper reports.
  • Because the target-only embedding experiment isolates fine-grained alignment, a natural extension is to replace static cells with semantic segments produced by an object detector or segmentation model; the oracle gap gives headroom to test whether adaptive partitions approach the Theoretical baseline.
  • The same grid recipe should transfer to other homogeneous, spatially structured domains such as aerial or medical imaging, where the signal is as much about where something is as what it is, though the paper does not test those domains.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. This paper reports an experimental evaluation of whether static spatial partitioning of candidate images improves CLIP-based text-to-image retrieval in a homogeneous domain (underwater MVK footage). The authors collected 741 human annotations, each with a short and a long text description and a bounding box, and compared whole-image retrieval with a five-region grid (Static-5), a nine-region grid (Static-9), textual position suffixes (Append-Short, Append-Long), and a Theoretical oracle that crops all candidates with the annotated box. The central finding is that both static grids improve R@10 and R@100 by roughly 40-50% over the whole-image baseline for both annotation subsets, while appending position words to the text hurts performance; overlapping grid cells are reported to improve robustness under simulated box perturbations, and a Pearson correlation of -0.69 between similarity differences and rank differences is used to argue that the gains are driven by finer text-image alignment. The paper is explicit in Section 6 that the protocol assumes 'perfect memory' (queries written while viewing the target image, pixel-accurate boxes) and that the findings therefore represent an upper-bound estimate; the abstract and the key findings, however, present the improvements without this qualification.

Significance. If the effects hold, the paper's main value is a carefully controlled quantification of the upper-bound potential of static-grid sub-region retrieval with CLIP in a hard, homogeneous domain. The design has real strengths: the Theoretical oracle baseline, the target-only fine-embedding isolation experiment, perturbation parameters fixed across variants with five seeds, and an explicit, honest limitation statement in Section 6. The negative result for textual position appending is a useful caution for system builders, and the -0.69 correlation plus the target-only experiment give the mechanism claim more support than a bare score table. In my reading, the stress-test concern does land: the experiment that would separate the perfect-memory upper bound from realistic use (memory-based query writing, coarse or biased region specification) is absent, so the abstract's wording ('significantly improves retrieval performance' and 'remain robust') should be re-scoped to a controlled upper-bound study.

major comments (4)
  1. [Abstract; §3.1, §4.3, §6] The headline claim that static partitioning 'significantly improves retrieval performance' and 'remains robust to perturbations' is presented without the central caveat that the experiments use a perfect-memory protocol: annotators wrote queries while viewing the target image and drew pixel-accurate boxes (§3.1), and at query time the scorer is given the union of all grid cells intersecting the ground-truth box (§4.3, the definition of B_I). Section 6 explicitly states that this assumption is unrealistic and that the findings are an upper-bound estimate, and the only tested user-facing mechanism for supplying the region (the Append-* textual suffixes) performs below the whole-image baseline in Table 2. The abstract and the key findings in §1 should be re-scoped to the upper-bound setting, or augmented with an experiment in which queries are written from memory and region information is biased or coarse, before the general claim can be supported.
  2. [§5.2] The similarity statistics reported in support of the alignment mechanism are internally inconsistent: the text reports µ(swhole) = 0.168 and µ(spart) = 0.159 and then states that a Wilcoxon signed-rank test finds spart significantly higher, although the printed means point in the opposite direction; §5.1 also reports a whole-image similarity of 0.159 for the long-suffix comparison. Because the -0.69 Pearson correlation between Δs and Δr is load-bearing evidence for the claim that the gains come from finer-grained alignment, the reported values or their labels need to be corrected and the test direction stated explicitly.
  3. [Table 2] The main retrieval results (R@k, MNR) are reported without confidence intervals or any significance test, although the abstract and the key findings use the wording 'significantly improve.' Given the modest absolute differences (e.g., skippable long queries, R@10 from 6 for Whole-Image to 9 for both grids), the authors should supplement Table 2 with paired significance tests on per-query ranks or bootstrap confidence intervals so that the 'nearly 50%' improvement claim can be separated from sampling noise.
  4. [§5.3] The perturbation analysis models user imprecision in a best-case way: shifts are zero-mean and centered on the true box, and after perturbation the method still scores by the maximum over all cells intersecting the perturbed box, which is more permissive than a user who must commit to a single cell or a coarse spatial phrase. The abstract's robustness claim should therefore be qualified, particularly since §6 itself reports ranking drops of up to 65% for the larger perturbation levels in the paper's own analysis.
minor comments (6)
  1. [§6] Table 2 does not support the statement that with perfect grid cells targets appear 'in less than 50% of the cases' in the first 1000 retrieved candidates: Theoretical R@1000 is 67%, 57%, 56%, and 41% across the four rows of the table, so the sentence should either refer to R@100 (41% for skippable long queries) or be corrected.
  2. [§5.3] The units of σs and σa are not stated; σs in {0.1, 0.25, 0.5} presumably means fractions of the image dimension and σa in {10, 25, 50} presumably means percentages, but this should be made explicit, and the handling of negative scaling factors (which can occur with N(1, σa) at the largest settings) should be described.
  3. [§2] The citation group '[7,2,7,20]' contains reference 7 twice; the duplicate should be removed.
  4. [Table 1] There are formatting errors in Table 1 ('T able 1', '19 .0 ± 19.3', '16 .4 ± 18.3'), and the paper never states the total number n of MVK keyframes in the search collection, which the reader needs in order to interpret the MNR and R@1000 values.
  5. [§5.2] The correlation of -0.13 between IoU and 'retrieval rank improvements' is not tied to a precisely defined quantity (change in mean rank or in a specific R@k), and the aggregation over queries should be stated.
  6. [Abstract] The phrase 'a 5-grid overlap' is ambiguous about whether it means the Static-5 layout with overlapping cells; the terminology used in the body ('Static-5 with additional overlap') would be clearer.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the evaluation compares fixed CLIP against independently defined baselines, and the oracle-style use of annotation boxes is explicitly labeled an upper bound rather than a predictive claim.

full rationale

The paper's central claim is an empirical measurement: static sub-region partitioning improves retrieval over a whole-image baseline on a collected annotation dataset. All methods use the same pretrained CLIP encoders, and the baselines (Whole-Image, Append-Short, Append-Long) are defined independently of the static-grid results. The use of annotation boxes to select grid cells (BI defined as cells with IoU > 0 with the annotation box in Section 4.3) is a deliberate protocol choice, and the paper explicitly identifies the collected boxes as 'optimal' and the results as an upper bound (Sections 3.2 and 6). No parameter is fitted to a subset and then re-predicted; the 'Theoretical' oracle crops candidate keyframes using annotation coordinates and is used solely as a bound, not as the claimed contribution. Self-citations to VBS-related work (e.g., reference [17], which shares authors) appear as background and motivation, and the central results do not depend on accepting any cited theorem. The robustness analysis perturbs boxes only around the true annotation, which the paper itself flags as a 'perfect memory' limitation; that is an external-validity caveat, not a circular step. Therefore no load-bearing step reduces to its own input by construction.

Assumptions & free parameters 0 free parameters · 6 assumptions · 0 invented entities

The central empirical claim is not a derivation, so there are no fitted constants; the grid layouts and perturbation magnitudes are experimenter choices, not free parameters fit to achieve the result. The main assumptions are that CLIP cosine similarity measures relevance, that the keyframe collection and human annotations are valid ground truth, and that the simulated perturbations resemble real user imprecision. The paper explicitly acknowledges the 'perfect memory' annotation setup as an upper-bound assumption.

assumptions (6)
  • domain assumption CLIP embeddings are normalized and cosine similarity is a valid ranking score.
    Section 4.1, Equation (1) uses cosine similarity between image and text embeddings as the retrieval score; the whole study assumes this reflects relevance.
  • domain assumption The MVK keyframe set is an appropriate retrieval collection and ground truth for the task.
    Section 3.1: keyframes previously used by VBS teams from Marine Video Kit; all candidate sets are drawn from this collection.
  • domain assumption Annotation bounding boxes and descriptions accurately mark the region of interest described.
    Section 3.2 treats the pixel-accurate boxes as 'optimal' and as the reference for IoU-based cell selection.
  • domain assumption IoU(box, cell) > 0 correctly identifies the subregions relevant to the query.
    Section 4.3 defines B_I via IoU(r_j, b) > 0; this relevance rule drives the static-grid ranking.
  • domain assumption The max over relevant cells is the appropriate way to aggregate subregion scores into an image score.
    Section 4.3 uses s_I = max over B_I; no alternative aggregation (mean, sum) is tested.
  • ad hoc to paper The perturbation distributions N(0, sigma_s) and N(1, sigma_a) model realistic user imprecision.
    Section 5.3 introduces these distributions without calibration to real user error; robustness conclusions depend on this modeling choice.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Experimental Evaluation of Static Image Sub-Region-Based Search Models Using CLIP." pith.science (2026). https://pith.science/paper/TXHKRCG6

@misc{pith2026250606938,
  author       = {Pith},
  title        = {Pith review of: Experimental Evaluation of Static Image Sub-Region-Based Search Models Using CLIP},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TXHKRCG6}},
  note         = {Machine review of arXiv:2506.06938}
}
read the original abstract

Advances in multimodal text-image models have enabled effective text-based querying in extensive image collections. While these models show convincing performance for everyday life scenes, querying in highly homogeneous, specialized domains remains challenging. The primary problem is that users can often provide only vague textual descriptions as they lack expert knowledge to discriminate between homogenous entities. This work investigates whether adding location-based prompts to complement these vague text queries can enhance retrieval performance. Specifically, we collected a dataset of 741 human annotations, each containing short and long textual descriptions and bounding boxes indicating regions of interest in challenging underwater scenes. Using these annotations, we evaluate the performance of CLIP when queried on various static sub-regions of images compared to the full image. Our results show that both a simple 3-by-3 partitioning and a 5-grid overlap significantly improve retrieval effectiveness and remain robust to perturbations of the annotation box.

Figures

Figures reproduced from arXiv: 2506.06938 by the authors.

Figure 1
Figure 1. Three static partitioning options: a) 2x2 grid with an additional center rect￾angle, b) simple 3x3 grid, and c) 2x2 grid with an additional center rectangle and overlapping regions [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The annotation tool used for our work. Annotators could draw bounding boxes on a canvas with images, and were asked to write a short and long textual descriptions. 3.2 Optimal Bounding Boxes In practice, users usually do not know the exact position of the region they are querying for. Even in tasks where the target frame is given, such as visual [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Heatmaps showing the spatial distribution of annotated rectangles across the dataset. From left to right: all rectangles, rectangles from skippable annotations, and rectangles from non-skippable annotations. Brighter regions indicate areas with higher annotation density [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Impact of perturbation factor and spatial overlap of grids on recall R@100 performance across different grid configurations. The reported "Overlap" is the en￾largement % relative to the whole-grid. variant with moderate overlaps maintains higher performance than other …

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

21 extracted references · 4 canonical work pages

  1. [17]

    In: ACM MMM

    Stroh, M., Kloda, V., Verner, B., Vopálková, Z., Buchmüller, R., Jäckl, B., Hajko, J., Lokoč, J.: Prak tool v3: Enhancing video item search using localized text and texture queries. In: ACM MMM. pp. 326–333. Springer (2025). https://doi.org/ 10.1007/978-981-96-2074-6_39

    doi:10.1007/978-981-96-2074-6_39 2025

  2. [1]

    In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A

    Blažek, A., Lokoč, J., Matzner, F., Skopal, T.: Enhanced signature-based video browser. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds.) ACM MMM. pp. 243–248. Springer International Publishing (2015). https://doi.org/10. 1007/978-3-319-14442-9_22

    2015

  3. [2]

    In: ACM MM

    Hinami, R., Matsui, Y., Satoh, S.: Region-based image retrieval revisited. In: ACM MM. p. 528–536. MM ’17, ACM (2017). https://doi.org/10.1145/3123266.3123312

    arXiv 2017

  4. [3]

    arXiv:2304.02643 (2023)

    Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., Dollár, P., Girshick, R.: Segment anything. arXiv:2304.02643 (2023). https://doi.org/10.48550/arXiv.2304.02643

    arXiv doi:10.48550/arxiv.2304.02643 2023

  5. [4]

    https://doi.org/10.1007/s11042-024-20509-z

    Korfhage, N., Mühling, M., Freisleben, B.: Search anything: segmentation-based similaritysearchviaregionprompts.In:MultimediaToolsandApplications(2024). https://doi.org/10.1007/s11042-024-20509-z

    doi:10.1007/s11042-024-20509-z 2024

  6. [5]

    In: Lokoč, J., Skopal, T., Schoeffmann, K., Mezaris, V., Li, X., Vrochidis, S., Patras, I

    Lokoč, J., Bátoryová, J., Smrz, D., Dobranský, M.: Video search with collage queries. In: Lokoč, J., Skopal, T., Schoeffmann, K., Mezaris, V., Li, X., Vrochidis, S., Patras, I. (eds.) ACM MMM. pp. 429–434. Lecture Notes in Computer Science, Springer (2021). https://doi.org/10.1007/978-3-030-67835-7_40

    work page doi:10.1007/978-3-030-67835-7_40 2021

  7. [6]

    In: Gurrin, C., Schoeffmann, K., Jónsson, B.Þ., Dang- Nguyen, D., Lokoč, J., Tran, M., Hürst, W., Rossetto, L., Healy, G

    Lokoč, J., Mejzlík, F., Veselý, P., Soucek, T.: Enhanced somhunter for known- item search in lifelog data. In: Gurrin, C., Schoeffmann, K., Jónsson, B.Þ., Dang- Nguyen, D., Lokoč, J., Tran, M., Hürst, W., Rossetto, L., Healy, G. (eds.) Proceed- ings of the 4th Annual on Lifelog Search Challenge, LSC. pp. 71–73. ACM (2021). https://doi.org/10.1145/3463948.3469074

    arXiv 2021

  8. [7]

    In: CVPR

    Mai, L., Jin, H., Lin, Z., Fang, C., Brandt, J., Liu, F.: Spatial-semantic image search by visual feature synthesis. In: CVPR. pp. 1121–1130 (2017). https://doi. org/10.1109/CVPR.2017.125

    doi:10.1109/cvpr.2017.125 2017

Show all 21 references
  1. [8]

    https://doi.org/10

    Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W.,Howes,R.,Huang,P.Y.,Li,S.W.,Misra,I.,Rabbat,M.,Sharma,V.,Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P....

    2024 doi arXiv

  2. [9]

    In: Anderst-Kotsis, G., Tjoa, A.M., Khalil, I., Elloumi, 14 Jäckl et al

    Peška, L., Trojanová, H.: Lineit: Similarity search and recommendation tool for photo lineup assembling. In: Anderst-Kotsis, G., Tjoa, A.M., Khalil, I., Elloumi, 14 Jäckl et al. M., Mashkoor, A., Sametinger, J., Larrucea, X., Fensel, A., Martinez-Gil, J., Moser, B., Seifert, C...

    2019 doi

  3. [10]

    https://doi.org/10.48550/ arXiv.2103.00020

    Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision (2021). https://doi.org/10.48550/ arXiv.2103.00020

    2021 doi arXiv

  4. [11]

    In: CVPR

    Ranasinghe, K., Shukla, S.N., Poursaeed, O., Ryoo, M.S., Lin, T.Y.: Learning to localize objects improves spatial reasoning in visual-llms. In: CVPR. pp. 12977– 12987 (2024). https://doi.org/10.1109/CVPR52733.2024.01233

    2024

  5. [12]

    In: International Conference on Multimedia Modeling

    Rossetto, L., Schuldt, H., Awad, G., Butt, A.A.: V3C - A research video collec- tion. In: International Conference on Multimedia Modeling. pp. 349–360. Springer (2019). https://doi.org/10.1007/978-3-030-05710-7_29

    2019 doi

  6. [13]

    TOMM (1) (Dec 2024)

    Sauter, L., Gasser, R., Schuldt, H., Bernstein, A., Rossetto, L.: Performance eval- uation in multimedia retrieval. TOMM (1) (Dec 2024). https://doi.org/10.1145/ 3678881

    2024

  7. [14]

    In: CVPR

    Shlapentokh-Rothman, M., Blume, A., Xiao, Y., Wu, Y., TV, S., Tao, H., Lee, J.Y., Torres, W., Wang, Y.X., Hoiem, D.: Region-based representations revisited. In: CVPR. pp. 17107–17116 (June 2024). https://doi.org/10.1109/CVPR52733. 2024.01619

    2024

  8. [15]

    In: Marchand-Maillet, S., Silva, Y.N., Chávez, E

    Skopal, T., Peška, L., Grošup, T.: Interactive product search based on global and local visual-semantic features. In: Marchand-Maillet, S., Silva, Y.N., Chávez, E. (eds.) Similarity Search and Applications. pp. 87–95. Springer International Pub- lishing, Cham (2018)

    2018

  9. [16]

    In: ACM MM

    Smith, J.R., Chang, S.F.: Visualseek: a fully automated content-based image query system. In: ACM MM. p. 87–98. ACM (1997). https://doi.org/10.1145/244130. 244151

    1997 doi

  10. [18]

    In: ACM MMM

    Truong, Q.T., Vu, T.A., Ha, T.S., Lokoč, J., Wong, Y.H., Joneja, A., Yeung, S.K.: Marine video kit: A new marine video dataset for content-based analysis and re- trieval. In: ACM MMM. pp. 539–550. Springer (2023). https://doi.org/10.1007/ 978-3-031-27077-2_42

    2023

  11. [19]

    Vadicamo, L., Arnold, R., Bailer, W., Carrara, F., Gurrin, C., Hezel, N., Li, X., Lokoč, J., Lubos, S., Ma, Z., Messina, N., Nguyen, T.N., Peska, L., Rossetto, L., Sauter, L., Schöffmann, K., Spiess, F., Tran, M.T., Vrochidis, S.: Evaluating per- formance and trends in interac...

    2024 doi

  12. [20]

    In: SIGIR

    Xu, H., Wang, J., Hua, X.S., Li, S.: Image search by concept map. In: SIGIR. p. 275–282. SIGIR ’10, ACM (2010). https://doi.org/10.1145/1835449.1835497

    2010

  13. [21]

    https://doi.org/10.48550/arXiv.2306.12156

    Zhao, X., Ding, W., An, Y., Du, Y., Yu, T., Li, M., Tang, M., Wang, J.: Fast segment anything (2023). https://doi.org/10.48550/arXiv.2306.12156

    2023 doi arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.