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arxiv: 2410.05217 · v7 · submitted 2024-10-07 · 💻 cs.CV

Organizing Unstructured Image Collections using Natural Language

Mingxuan Liu , Zhun Zhong , Jun Li , Gianni Franchi , Subhankar Roy , Elisa Ricci This is my paper

Pith reviewed 2026-05-23 19:36 UTC · model grok-4.3

classification 💻 cs.CV
keywords OpenSMCX-Clustersemantic clusteringimage organizationnatural language reasoningmultiple clusteringCOCO-4CFood-4C
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The pith

X-Cluster discovers multiple natural language criteria to automatically cluster unstructured image collections without human input.

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

The paper introduces the task of open-ended semantic multiple clustering, in which a system must find several distinct ways to group images by semantic meaning and apply those groupings, all without any predefined criteria or human guidance. X-Cluster implements this by scanning an entire collection at once, using text to propose candidate criteria such as activity or location, and then forming separate clusters for each criterion. A sympathetic reader would care because the approach removes the usual requirement that someone first specify what to look for, and the authors demonstrate it on new benchmarks while applying it to detect biases in image generators.

Core claim

X-Cluster treats text as a reasoning proxy: it concurrently scans the entire image collection, proposes candidate criteria in natural language, and groups images into meaningful clusters per criterion. This enables the discovery of several diverse semantic clustering criteria and the subsequent organization of images according to those criteria, without requiring any human input or predefined criteria, as evaluated on the new COCO-4C and Food-4C benchmarks.

What carries the argument

X-Cluster framework that uses natural language as a reasoning proxy to propose candidate criteria and form per-criterion clusters.

If this is right

  • The method produces meaningful partitions across several datasets including the newly introduced COCO-4C and Food-4C benchmarks.
  • It can be applied to uncover hidden biases in text-to-image generative models.
  • It supports analysis of image virality patterns on social media.
  • It operates without assuming predefined clustering criteria or a fixed number of clusters, unlike prior approaches.

Where Pith is reading between the lines

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

  • The same language-proxy scanning idea could be tested on video collections to discover multiple temporal or narrative groupings.
  • Repeated application across many collections might surface recurring criteria that could serve as a starting vocabulary for future clustering tasks.
  • The approach might reduce the manual effort needed to prepare large image datasets for training vision models by revealing multiple useful partitions automatically.

Load-bearing premise

An automated language-based system can reliably invent diverse, meaningful semantic criteria and correctly assign images to clusters for each criterion without any human-defined rules.

What would settle it

On the COCO-4C benchmark, if the clusters produced by X-Cluster show no better alignment with the four human-annotated criteria than random assignment, the central claim would fail.

Figures

Figures reproduced from arXiv: 2410.05217 by Elisa Ricci, Gianni Franchi, Jun Li, Mingxuan Liu, Subhankar Roy, Zhun Zhong.

Figure 1
Figure 1. Figure 1: Top: Open-ended Semantic Multiple Clustering (OpenSMC) deals with automatically organizing an unstructured image collection into semantically meaningful and human interpretable clusters, under multiple shared themes or criteria, without requiring any prior knowledge. Bottom: Our system enables various applications like discovering novel biases in text-to-image (T2I) generative models. See our project webpa… view at source ↗
Figure 2
Figure 2. Figure 2: OpenSMC benchmarks. We introduce two new challenging benchmarks: COCO-4c and Food-4c. We show all annotated criteria and the corresponding labels for the example images. s l k and a subset of images Dl k ⊂ D that share the same semantics. A criterion Rl refers to a theme for grouping images, such that all the clusters under Rl should align with the theme–i.e., as shown in [PITH_FULL_IMAGE:figures/full_fig… view at source ↗
Figure 3
Figure 3. Figure 3: X -Cluster overview. (Left) The Criteria Proposer reason over the entire image set via their textual proxies and deposits diverse, natural language grouping criteria in a Criteria Pool. (Middle) The Semantic Grouper draws each criterion from the pool, discovers semantic clusters at three levels of granularity, and assigns images to their proper clusters. (Right) Aggregating these assignments reveals the co… view at source ↗
Figure 5
Figure 5. Figure 5: Impact of Image Quantity on Criteria Discovery. We evaluate the TPR performance of the Caption-based Proposer at different image scales against the Hard ground-truth criteria set. tion, ranking models by their harmonic mean Har￾monic Mean (HM) of CAcc and SAcc. To gauge headroom we include a pseudo upper bound: CLIP ViT-L/14 in a zero-shot regime where the true cri￾terion, cluster names and count are all s… view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of Semantic Groupers. We report CAcc, SAcc, and their Harmonic Mean (HM) for different Semantic Groupers ( ) on the Basic criteria across six benchmarks. CLIP zero-shot classification ( ) serves as a pseudo upper bound, while KMeans ( ) with strong visual features is used as a CAcc baseline. Best performers are marked with . See App. G.2 for expanded numerical results and App. H for clustering v… view at source ↗
Figure 7
Figure 7. Figure 7: Study of multi-granularity refinement. 5.3 Comparison with TCMC Methods Tab. 2 pits our best model, i.e.the Caption-based Grouper, against four recent text criterion condi￾tioned multiple clustering systems: IC|TC (Kwon et al., 2024), SSD-LLM (Luo et al., 2024b), MMaP (Yao et al., 2024), and MSub (Yao et al., 2025). Unlike our fully automated X -Cluster, which discovers criteria and needs no preset cluster… view at source ↗
Figure 8
Figure 8. Figure 8: Bias Discovery in T2I-Generated Images. Bias intensity, dominant clusters, and example images are shown for few occupations. Full results for all studied occupations are provided in App. P.1. distributions for each occupation. To quantify bias, we measured the normalized entropy of each di￾mension’s distribution (D’Incà et al., 2024) as bias intensity and marked the dominant (largest) clus￾ter as the poten… view at source ↗
Figure 9
Figure 9. Figure 9: Social media image popularity analysis. We show the popularity score distributions for Top Trending (have highest average popularity score) and Top Main￾stream (contain most images) clusters, discovered by X -Cluster across three criteria. Findings: As shown in [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Example predicted clusters of COCO-4c [PITH_FULL_IMAGE:figures/full_fig_p033_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Example predicted clusters of Food-4c. MLLMs also play a crucial role in our system, as they are responsible for translating images into text for subsequent processing steps. MLLM hallu￾cination (Wang et al., 2024c) typically involves incorrectly identifying the existence of objects, at￾tributes, or spatial relationships within an image. However, since our proposed system operates at the dataset level rat… view at source ↗
Figure 12
Figure 12. Figure 12: Example predicted clusters of Action-3c. ing the LLM to assign image captions to clusters, we condition it to concentrate exclusively on the Criterion depicted in each image (see Tab. 17). To validate the effectiveness of these bias miti￾gation techniques, we conducted a fair clustering experiment. Specifically, following Kwon et al. (2024), we sampled images for four occupations (Craftsman, Laborer, Danc… view at source ↗
Figure 13
Figure 13. Figure 13: Example predicted clusters of Clevr-4c [PITH_FULL_IMAGE:figures/full_fig_p036_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Example predicted clusters of Card-2c. Criterion Location Granularity Fine Rural countryside Sports stadium Public park Forest area … Criterion Location Granularity Coarse Domestic environment Natural environment Criterion Location Granularity Middle Natural wilderness Natural water body … … [PITH_FULL_IMAGE:figures/full_fig_p037_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Example predicted clusters of COCO-4c at different granularities. experiments, we used LLaVA-NeXT-7B (Liu et al., 2024b) as the MLLM and Llama-3.1-8B (Meta, 2024a) as the LLM. As shown in Tab. 33, organizing 5,000 images based on all four discovered criteria can be com￾pleted by X -Cluster in 29.1 hours on a single A100 GPU or 16.7 hours on a single H100 GPU. More importantly, most steps in our framework,… view at source ↗
Figure 16
Figure 16. Figure 16: Failure case analysis. We show wrongly predicted images with their ground-truth label for four clusters [PITH_FULL_IMAGE:figures/full_fig_p038_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Further study on the influence of multi￾granularity clustering output. We evaluate the CAcc and SAcc of the multi-granularity grouping results at each predicted clustering granularity level against each ground-truth annotation granularity level for the Action and Location criteria of the Action-3c dataset. The Harmonic Mean of CAcc and SAcc is reported for each granularity pair. L1, L2, and L3 represent t… view at source ↗
Figure 18
Figure 18. Figure 18: Sensitivity analysis of different MLLMs and LLMs on the six OpenSMC benchmarks. Top (a): We fix the LLM to Llama-3.1-8B and study the impact of different MLLMs. Bottom (b): We fix the MLLM to LLaVA-NeXT-7B and study the impact of different LLMs. The average clustering accuracy(%) across different criteria is reported on the left, while the average semantic accuracy(%) is reported on the right. P Further D… view at source ↗
Figure 19
Figure 19. Figure 19: Bias quantification results and human evaluation for each occupation and criterion across the two studied T2I models, DALL·E3 and SDXL. The bias intensity score is reported. to generate 100 images for each occupation for our study. This resulted in a total of 1,800 images. For each occupation, we provide some examples of im￾ages generated by DALL·E3 in [PITH_FULL_IMAGE:figures/full_fig_p041_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Complete analysis of social media photo popularity on the SPID dataset. We display the Top Trending and Top Mainstream clusters, along with the popularity distribution of data points within these clusters across all ten discovered criteria (in Grey). Blond Not blond GT Distribution Pred Distribution Pred Distribution 13.4 % 94.3 % 86.5% 5.7 % 48.3 % 51.7% 50.8 % 49.2% Male Female GT Distribution [PITH_FU… view at source ↗
Figure 21
Figure 21. Figure 21: Results of dataset bias discovery and mit￾igation. Worst group and average accuracies(%) are reported [PITH_FULL_IMAGE:figures/full_fig_p043_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Samples of DALL·E3 generated images. For each occupation, the simple prompt “A portrait photo of a <OCCUPATION>”, that does not reference any potential bias dimensions such as gender, race or hair color, is fed to DALL·E3 to generate 100 images. We present a random sample of 30 generated images. 46 [PITH_FULL_IMAGE:figures/full_fig_p046_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Samples of SDXL generated images. For each occupation, the simple prompt “A portrait photo of a <OCCUPATION>”, that does not reference any potential bias dimensions such as gender, race or hair color, is fed to SDXL to generate 100 images. We present a random sample of 30 generated images. 47 [PITH_FULL_IMAGE:figures/full_fig_p047_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Example of the questionnaire for human evaluation study. 48 [PITH_FULL_IMAGE:figures/full_fig_p048_24.png] view at source ↗
read the original abstract

In this work, we introduce and study the novel task of Open-ended Semantic Multiple Clustering (OpenSMC). Given a large, unstructured image collection, the goal is to automatically discover several, diverse semantic clustering criteria (e.g., Activity or Location) from the images, and subsequently organize them according to the discovered criteria, without requiring any human input. Our framework, X-Cluster: eXploratory Clustering, treats text as a reasoning proxy: it concurrently scans the entire image collection, proposes candidate criteria in natural language, and groups images into meaningful clusters per criterion. This radically differs from previous works, which either assume predefined clustering criteria or fixed cluster counts. To evaluate X-Cluster, we create two new benchmarks, COCO-4C and Food-4C, each annotated with four distinct grouping criteria and corresponding cluster labels. Experiments show that X-Cluster can effectively reveal meaningful partitions on several datasets. Finally, we use X-Cluster to achieve various real-world applications, including uncovering hidden biases in text-to-image (T2I) generative models and analyzing image virality on social media. Project page: https://oatmealliu.github.io/xcluster.html

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

0 major / 3 minor

Summary. The paper introduces the novel Open-ended Semantic Multiple Clustering (OpenSMC) task: given an unstructured image collection, automatically discover multiple diverse semantic clustering criteria (e.g., activity, location) in natural language and partition the images accordingly, with no human-specified criteria or cluster counts. The X-Cluster framework uses VLMs/LLMs to scan the full collection, propose criteria via text-based reasoning, and produce per-criterion clusters. Two new benchmarks (COCO-4C, Food-4C) are created with four human-annotated criteria and labels each; the method is evaluated on these plus other datasets and applied to bias detection in T2I models and virality analysis.

Significance. If the empirical results hold, the work is significant for moving beyond conventional clustering that assumes fixed criteria or k. Treating text as an explicit reasoning proxy for criterion discovery is a distinctive technical choice, and the new benchmarks enable quantitative assessment of multi-criterion discovery. The manuscript supplies prompting, sampling, and clustering details, supporting reproducibility, and the downstream applications provide falsifiable use-case evidence.

minor comments (3)
  1. [Abstract] Abstract: the claim that 'experiments show that X-Cluster can effectively reveal meaningful partitions' would be strengthened by including one or two key quantitative metrics (e.g., alignment scores on COCO-4C/Food-4C) rather than leaving the statement purely qualitative.
  2. [Benchmarks] § on benchmark construction: state explicitly how the four criteria per dataset were selected and validated to ensure they are diverse and non-redundant; this directly affects the claim of 'diverse semantic clustering criteria.'
  3. [Experiments] Evaluation section: clarify the precise alignment metric between proposed clusters and human labels (e.g., adjusted Rand index, normalized mutual information) and report per-criterion as well as aggregate scores.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, significance assessment, and recommendation of minor revision. No major comments were raised in the report, so we have no specific points to address point-by-point. We will incorporate any minor suggestions during revision.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper defines the OpenSMC task and X-Cluster framework as using external VLM/LLM reasoning to propose natural-language criteria and form clusters on raw image collections, with no equations or parameters fitted to the target outputs. Evaluation relies on newly introduced external benchmarks (COCO-4C, Food-4C) with independent human annotations, plus downstream applications, rather than any self-referential fit or renamed input. No self-citation chains, uniqueness theorems, or ansatzes are invoked as load-bearing; the derivation chain remains self-contained against external benchmarks and does not reduce any claimed prediction to its own construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities with independent evidence are detailed in the provided text.

invented entities (1)
  • text as a reasoning proxy no independent evidence
    purpose: to propose candidate clustering criteria and group images
    Central mechanism described in the abstract but no independent evidence or falsifiable handle is provided.

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Reference graph

Works this paper leans on

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