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arxiv: 2607.00578 · v1 · pith:T3OMLUOYnew · submitted 2026-07-01 · 💻 cs.CV

Caption Bottleneck Models

Seref Baris Cagliyan , Umut Ozdemir , Merve Tapli , Emre Akbas This is my paper

Pith reviewed 2026-07-02 14:55 UTC · model grok-4.3

classification 💻 cs.CV
keywords Caption Bottleneck ModelsConcept Bottleneck ModelsInterpretabilityLarge Multimodal ModelsImage ClassificationLeakage PreventionConcept Discovery
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The pith

Caption Bottleneck Models train classifiers only on LMM captions to ensure leakage-free interpretability.

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

The paper introduces Caption Bottleneck Models as an alternative to traditional Concept Bottleneck Models. Instead of relying on predefined lists of concepts, CaBM generates free-form natural language captions for each image using large multimodal models. A text-based classifier is then trained exclusively on these captions. This design prevents information leakage from visual features that bypass the bottleneck. The approach also enables the automatic discovery of relevant concepts by examining the trained text classifier.

Core claim

By representing images via LMM-generated captions and training a classifier strictly on this text, CaBM ensures a leakage-free architecture by construction. Additionally, by analyzing the text classifier post-training, CaBM autonomously discovers high-quality, dataset-specific concepts.

What carries the argument

The caption bottleneck, which converts images to LMM-generated natural language descriptions before any classification occurs.

If this is right

  • CaBM achieves competitive accuracy on fine-grained and coarse-grained image classification benchmarks.
  • The architecture is leakage-free because predictions depend only on the caption text.
  • Dataset-specific concepts are discovered without expert annotations or static dictionaries.
  • Interpretability is maintained through the use of human-readable natural language.

Where Pith is reading between the lines

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

  • If LMM captions systematically omit certain visual cues, accuracy on tasks relying on those cues would suffer.
  • This method could extend to other vision tasks if suitable text-based models are available.
  • The discovered concepts might expose biases present in the underlying LMM caption generator.

Load-bearing premise

LMM-generated captions contain all the visual information necessary for accurate classification without missing details or introducing errors.

What would settle it

An experiment where CaBM accuracy drops significantly below a direct vision model on a dataset known to require visual features not easily captured in text captions.

Figures

Figures reproduced from arXiv: 2607.00578 by Emre Akbas, Merve Tapli, Seref Baris Cagliyan, Umut Ozdemir.

Figure 1
Figure 1. Figure 1: CaBM performs recognition through a caption bottleneck: a frozen LMM produces captions, a text classifier predicts labels from these captions, and open￾vocabulary concepts are extracted via post-hoc analysis. Concept Bottleneck Models (CBMs) provide a principled approach for in￾terpretable by design predictors: they first map an input to a set of human￾interpretable concepts, and then predict the label fro… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of CaBM. (1) A frozen LMM generates K diverse captions per image using a structured prompt P and multiple decoding temperatures; captions are deterministically censored by τ to remove class names and taxonomy terms. (2) A text encoder predicts per-caption logits, which are averaged for image-level inference. (3) Concepts are obtained post-hoc by proposing salient spans (gradient×embedding), scorin… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative concept comparison across methods on CUB200. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Concept importance across images. For four datasets, we select one ran￾dom class and sample nine random test images. For each class, we show the top-5 concepts and their importance scores (higher indicates stronger influence under the ranking criterion in Sec. 3.3) [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Human intervention example [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
read the original abstract

Concept Bottleneck Models (CBMs) provide interpretability by routing predictions through a layer of human-understandable concepts. However, defining an optimal concept set for a specific dataset remains an open challenge. Existing approaches rely on expensive expert annotations or LLM-generated lists based solely on class names. Even "open-vocabulary" variants typically depend on static concept sets, which restrict discovery and introduce label bias. Furthermore, traditional CBMs often suffer from information leakage, where unmodeled visual features bypass the bottleneck and compromise the integrity of the explanations. To overcome these limitations, we propose Caption Bottleneck Models (CaBM), a framework that circumvents the need for predefined concept sets by replacing rigid concept layers with free-form natural language. By representing images via LMM-generated captions and training a classifier strictly on this text, CaBM ensures a leakage-free architecture by construction. Additionally, by analyzing the text classifier post-training, CaBM autonomously discovers high-quality, dataset-specific concepts. Our results across fine- and coarse-grained benchmarks demonstrate that CaBM achieves competitive accuracy while preserving interpretability without the constraints of external dictionaries or manual labeling.

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

1 major / 1 minor

Summary. The paper proposes Caption Bottleneck Models (CaBM) to address limitations in Concept Bottleneck Models (CBMs), including the difficulty of defining optimal concept sets and information leakage. CaBM replaces rigid concept layers with free-form natural language captions generated by Large Multimodal Models (LMMs); a downstream classifier is trained exclusively on this text, which the authors state ensures a leakage-free architecture by construction. The method further claims to enable autonomous discovery of high-quality, dataset-specific concepts via post-training analysis of the text classifier. Results on fine- and coarse-grained benchmarks are stated to show competitive accuracy while preserving interpretability without external dictionaries or manual labeling.

Significance. If the empirical claims hold, the work would be significant for interpretable vision models: the architectural choice directly guarantees leakage prevention (a known weakness of standard CBMs) without circular fitting or additional parameters, and the post-hoc concept discovery reduces dependence on expert annotations or static lists. This construction-based guarantee and the potential for data-driven concept extraction are clear strengths relative to prior CBM variants.

major comments (1)
  1. [Abstract] Abstract: the central empirical claim that CaBM 'achieves competitive accuracy' on fine- and coarse-grained benchmarks is stated without any quantitative numbers, tables, ablation results, or error analysis. Because accuracy is required to establish that caption completeness does not systematically degrade performance, this omission is load-bearing for the overall contribution.
minor comments (1)
  1. The description of how the trained text classifier is analyzed to 'autonomously discover' concepts lacks concrete details on the extraction procedure (e.g., attention weights, feature importance, or clustering method).

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and positive recommendation for minor revision. We address the major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central empirical claim that CaBM 'achieves competitive accuracy' on fine- and coarse-grained benchmarks is stated without any quantitative numbers, tables, ablation results, or error analysis. Because accuracy is required to establish that caption completeness does not systematically degrade performance, this omission is load-bearing for the overall contribution.

    Authors: We agree that the abstract would be strengthened by including specific quantitative results. While the full manuscript contains detailed accuracy tables, ablations, and comparisons in the experiments section, the abstract currently states the claim at a high level. In the revised version we will update the abstract to report the key accuracy numbers (and brief baseline comparisons) from the fine- and coarse-grained benchmarks to directly support the claim that caption completeness does not systematically degrade performance. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper's central claim is that CaBM ensures leakage-free architecture by construction through an explicit architectural choice: mapping images to LMM captions and training a text-only classifier with no visual feature access. This is a definitional property of the proposed setup rather than a derivation that reduces to fitted parameters, self-citations, or ansatzes. No equations, uniqueness theorems, or load-bearing steps are shown that would make any prediction equivalent to its inputs by construction. The approach is self-contained as a modeling framework without circular reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only; no explicit free parameters, axioms, or invented entities are stated. The approach implicitly assumes caption sufficiency and LMM reliability without providing independent evidence for either.

axioms (1)
  • domain assumption LMM-generated captions contain all information necessary for the downstream classification task.
    Invoked by the claim that training strictly on text yields competitive accuracy without leakage.

pith-pipeline@v0.9.1-grok · 5731 in / 1116 out tokens · 20022 ms · 2026-07-02T14:55:44.325661+00:00 · methodology

discussion (0)

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