arxiv: 2604.20329 · v1 · submitted 2026-04-22 · 💻 cs.CV · cs.AI

Recognition: unknown

Image Generators are Generalist Vision Learners

Howard Zhou, Huizhong Chen, Jean-Baptiste Alayrac, Jonathan T. Barron, Kaiming He, Karen Truong, Kyle Genova, Mandy Guo, Nithish Kannen, Oliver Wang, Paul Voigtlaender, Radu Soricut, Saining Xie, Shangbang Long, Sherry Ben, Shuyang Sun, Songyou Peng, Suhas Yogin, Thomas Funkhouser, Valentin Gabeur, Wenlei Zhou, Yanan Bao, Yandong Li, Yiming Gu, Zhicheng Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 01:10 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords image generationgeneralist vision modelsvisual representationsinstruction tuningRGB output parameterizationsegmentationdepth estimationfoundational vision models

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The pith

Image generation pretraining equips models with general visual understanding for state-of-the-art task performance.

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

The paper argues that pretraining models to generate images builds strong, reusable visual representations in a manner parallel to how language models acquire capabilities from text generation. It supports this by taking a pretrained generator and performing light instruction tuning on a combination of its original data and small amounts of vision task data. Outputs for tasks are produced as RGB images, turning problems like segmentation or depth estimation into variants of image synthesis. The resulting system reaches or exceeds specialist performance on multiple 2D and 3D benchmarks while keeping its generation skills intact. This positions image generation as a potential universal interface for both creating and understanding visual content.

Core claim

Image generation training serves a role similar to LLM pretraining, and lets models learn powerful and general visual representations that enable SOTA performance on various vision tasks. By parameterizing the output space of vision tasks as RGB images, perception is reframed as image generation. After instruction-tuning on a mixture of original training data and a small amount of vision task data, the model achieves SOTA results on tasks involving both 2D and 3D understanding, beating or rivaling zero-shot domain-specialists on segmentation and metric depth estimation without sacrificing generation capabilities.

What carries the argument

Parameterizing outputs of vision tasks as RGB images to reframe them as image generation inside a pretrained generator, then applying light instruction tuning on mixed generative and task data.

If this is right

A single model trained chiefly on generation can handle multiple distinct vision tasks at specialist level.
Segmentation and metric depth estimation can be solved at or above the level of dedicated models such as Segment Anything or Depth Anything series.
Generative ability remains available after the tuning step, so one system supports both creation and comprehension.
Computer vision could move toward foundational models whose core capability is learned through generative pretraining.

Where Pith is reading between the lines

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

Larger-scale generative pretraining alone, with even less task data, might further strengthen the understanding capabilities.
The same reframing could extend to video generators for learning temporal structure without separate temporal models.
Models built this way could integrate more naturally with language-based interfaces since both generation and understanding use a common output modality.
Interactive applications might benefit from a single model that can both describe scenes and synthesize new images in response.

Load-bearing premise

The performance gains on understanding tasks come mainly from the image generation pretraining rather than from the choice of tuning data mixture or the RGB output format.

What would settle it

Training a comparable non-generative model from scratch using only the vision task data and the same RGB output parameterization, then obtaining similar benchmark results, would indicate that generative pretraining is not the primary driver.

read the original abstract

Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent capabilities of language understanding and reasoning from generative pretraining. While it has long been conjectured that the ability to create visual content implies an ability to understand it, there has been limited evidence that generative vision models have developed strong understanding capabilities. In this work, we demonstrate that image generation training serves a role similar to LLM pretraining, and lets models learn powerful and general visual representations that enable SOTA performance on various vision tasks. We introduce Vision Banana, a generalist model built by instruction-tuning Nano Banana Pro (NBP) on a mixture of its original training data alongside a small amount of vision task data. By parameterizing the output space of vision tasks as RGB images, we seamlessly reframe perception as image generation. Our generalist model, Vision Banana, achieves SOTA results on a variety of vision tasks involving both 2D and 3D understanding, beating or rivaling zero-shot domain-specialists, including Segment Anything Model 3 on segmentation tasks, and the Depth Anything series on metric depth estimation. We show that these results can be achieved with lightweight instruction-tuning without sacrificing the base model's image generation capabilities. The superior results suggest that image generation pretraining is a generalist vision learner. It also shows that image generation serves as a unified and universal interface for vision tasks, similar to text generation's role in language understanding and reasoning. We could be witnessing a major paradigm shift for computer vision, where generative vision pretraining takes a central role in building Foundational Vision Models for both generation and understanding.

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.

Desk Editor's Note private letter to a colleague

The paper shows an image generator can be lightly tuned into a generalist that hits SOTA on several vision tasks by reframing them as RGB generation, but it does not isolate whether the generative pretraining is the actual driver.

read the letter

The main thing to know is that the authors take their Nano Banana Pro image generator, mix in a small amount of task data, reframe outputs for segmentation, depth, and similar tasks as RGB images, and report that the resulting Vision Banana model beats or matches specialists like SAM3 and Depth Anything while keeping generation quality. They frame this as evidence that image generation pretraining builds general visual representations the way LLM pretraining builds language ones, with generation as a universal interface. That specific construction and the multi-task coverage via generation is the concrete new piece. If the numbers are real, it is a clean empirical demonstration of the unification idea. The paper does a reasonable job laying out the conjecture and showing that lightweight tuning can preserve the base capabilities. The soft spots are exactly where the stress-test points: no ablations compare the same tuning recipe on a non-generative backbone, and there are no controls that separate the effect of the generative pretraining from the added task data, the RGB parameterization, or possible data scale differences versus the baselines. The abstract gives no quantitative details on data volumes, exact protocols, or significance, so it is impossible to tell whether the central claim holds or whether the results would appear with any strong backbone under the same recipe. This is aimed at people working on foundational vision models and unified architectures. A reader tracking generative approaches to understanding would pick up the practical setup and the task list, but would need the full experimental section and ablations before treating the strong conclusion as settled. It deserves a serious referee to check the controls and numbers, even if heavy revision is likely.

Referee Report

2 major / 2 minor

Summary. The paper claims that image generation pretraining plays a role analogous to LLM pretraining by developing powerful general visual representations. It introduces Vision Banana, created by instruction-tuning Nano Banana Pro on a mixture of its original generative data plus a small amount of vision task data, with all outputs reframed as RGB images. This yields a generalist model that achieves SOTA or near-SOTA results on 2D and 3D vision tasks (e.g., beating SAM3 on segmentation and rivaling Depth Anything on metric depth) while preserving the base model's image generation capabilities through lightweight tuning. The work positions image generation as a unified interface for vision tasks and suggests a paradigm shift toward generative pretraining for foundational vision models.

Significance. If the central empirical claims hold after proper controls, the result would support treating generative vision models as generalist learners comparable to LLMs, with the RGB-output reframing offering a practical unification of generation and perception. This could influence future work on foundational models that handle both understanding and synthesis without task-specific architectures.

major comments (2)

[Abstract / Experimental Setup] Abstract and experimental sections: the central claim that generative pretraining (rather than the instruction-tuning mixture or RGB parameterization) produces the generalist capabilities is not isolated by any ablation. No comparison is reported between (a) the generative Nano Banana Pro backbone plus task mixture versus (b) an equivalent non-generative backbone trained with the identical tuning recipe and data volumes. This directly undermines the assertion that 'image generation training serves a role similar to LLM pretraining.'
[Results / Experiments] Results sections: SOTA claims (e.g., vs. SAM3 on segmentation, Depth Anything on depth) are presented without quantitative details on evaluation protocols, exact data volumes for the 'small amount' of task data, baseline training data scales, or statistical significance. Without these, it is impossible to rule out that gains arise from undisclosed data advantages or evaluation differences rather than the generative pretraining itself.

minor comments (2)

[Method] Clarify the precise composition of the instruction-tuning mixture (which tasks, exact sample counts) and the training hyperparameters used for the lightweight tuning stage.
[Discussion] Add explicit discussion of potential data overlap between the original Nano Banana Pro training set and the added vision task data to address possible leakage.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important areas for strengthening the experimental rigor and clarity of our claims. We address each major point below and have revised the manuscript accordingly to improve transparency and address potential alternative explanations for our results.

read point-by-point responses

Referee: [Abstract / Experimental Setup] Abstract and experimental sections: the central claim that generative pretraining (rather than the instruction-tuning mixture or RGB parameterization) produces the generalist capabilities is not isolated by any ablation. No comparison is reported between (a) the generative Nano Banana Pro backbone plus task mixture versus (b) an equivalent non-generative backbone trained with the identical tuning recipe and data volumes. This directly undermines the assertion that 'image generation training serves a role similar to LLM pretraining.'

Authors: We acknowledge that a direct ablation isolating generative pretraining from the instruction-tuning mixture and RGB output parameterization would provide stronger causal evidence. Training a comparable non-generative backbone at the scale of Nano Banana Pro with identical data volumes and tuning is computationally prohibitive within the scope of this study. In the revised manuscript, we have added a dedicated limitations subsection discussing this gap, including comparisons to existing non-generative generalist models (e.g., from the literature) and preliminary small-scale experiments showing that the untuned generative backbone already exhibits stronger zero-shot task performance than similarly sized non-generative models. We have also moderated the language in the abstract and introduction to frame our contribution as demonstrating that generative models can serve as effective generalist learners rather than claiming strict uniqueness of the pretraining paradigm. revision: partial
Referee: [Results / Experiments] Results sections: SOTA claims (e.g., vs. SAM3 on segmentation, Depth Anything on depth) are presented without quantitative details on evaluation protocols, exact data volumes for the 'small amount' of task data, baseline training data scales, or statistical significance. Without these, it is impossible to rule out that gains arise from undisclosed data advantages or evaluation differences rather than the generative pretraining itself.

Authors: We agree that additional quantitative details are necessary for reproducibility and to rule out confounds. The revised manuscript now includes: (1) full evaluation protocols with exact metrics, datasets, and splits; (2) precise data volumes, specifying that the task-specific data constitutes less than 5% of the total tuning mixture by token count; (3) baseline training scales for all compared models; and (4) statistical significance via multiple random seeds with reported standard deviations. These additions confirm that performance advantages are not due to hidden data scale differences. We have also expanded the experimental setup section with a new table summarizing all hyperparameters and data compositions. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical results from training and evaluation

full rationale

The paper presents no mathematical derivations, equations, or fitted parameters that reduce to inputs by construction. Claims rest on experimental outcomes of instruction-tuning Nano Banana Pro on a data mixture and evaluating against external baselines like SAM3 and Depth Anything. No self-definitional loops, renamed predictions, or load-bearing self-citations that substitute for independent evidence are identifiable in the text. The central assertion that generative pretraining enables generalist vision is framed as an empirical finding supported by SOTA comparisons, not a tautological reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities are stated. The work implicitly assumes that reframing tasks as RGB image generation preserves task semantics and that the base generator already encodes sufficient visual structure.

pith-pipeline@v0.9.0 · 5691 in / 1123 out tokens · 48280 ms · 2026-05-10T01:10:18.387963+00:00 · methodology

discussion (0)

Forward citations

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