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REVIEW 3 major objections 6 minor 1 cited by

Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A vision-language model fine-tuned on plain-language descriptions of image-to-image transformations can carry out vision tasks it never trained on, including turning edge maps, depth maps, and outlines into photographs.

desk verdict A useful new dataset and a plausible but unproven central claim; the missing terminological-instruction control is load-bearing. read the letter →

arxiv 2412.18525 v3 pith:S5224KQ3 submitted 2024-12-24 cs.CV

Yang Shen , Xiu-Shen Wei , Yifan Sun , Yuxin Song , Tao Yuan , Jian Jin , Heyang Xu , Yazhou Yao
show 1 more author
Errui Ding
This is my paper · ORCID
classification cs.CV
keywords explanatoryinstructionszero-shottaskgeneralizationvision-languagemodelautoregressiveimagegenerationinstructionfollowingimage-to-imagetranslationunifiedvisiontaskscontrollable
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 computer vision has not reached the zero-shot task generalization of NLP because vision tasks are defined by discrete labels such as 'semantic segmentation' rather than by what the task actually does to an image. It introduces Explanatory Instructions, detailed linguistic descriptions of the transformation from an input image to an output image, and builds a 12-million-triplet dataset of image, instruction, and output pairs. Fine-tuning a 7B autoregressive vision-language model on these triplets is claimed to produce instruction-level zero-shot ability on seen tasks and task-level zero-shot generalization on unseen tasks such as edge-to-image, canny-to-image, and depth-to-image. If this holds, a written description of a vision objective is enough to make a model execute a task it has never seen.

What carries the argument

The load-bearing object is the Explanatory Instruction, defined as a detailed linguistic description of the transformation from an input image to an output image, written without task jargon and usually provided in both directions. Around it sits DECVT, a dataset of about 12 million 'image input to explanatory instruction to output' triplets assembled from terminological tasks such as restoration, detection, depth estimation, pose estimation, segmentation, and controllable generation, plus open-ended image-pair editing data, with instructions generated by a large multimodal model and by manual templates. The model is a vanilla token-based autoregressive vision-language model: a VQ-VAE image tokenizer converts images to discrete tokens, the tokens are concatenated with instruction text, and a decoder-only transformer trained with next-token prediction generates the output image, initialized from a pretrained multimodal generative model rather than trained from scratch. A supporting observation is that in a PCA-reduced feature space, explanatory instructions form continuous, overlapping distributions across tasks, whereas terminological instructions cluster discretely, which the paper identifies as the driver of zero-shot generalization.

What would settle it

Fine-tune the same pretrained model on the same data with only terminological labels instead of explanatory instructions and test on the same held-out tasks; if that control also produces faithful edge-to-image and depth-to-image outputs, the explanatory format is not the cause.

Watch

Extended reading notes

Core claim

The central discovery claimed is that representing a vision task as an explanatory instruction, a natural-language description of the transformations between an input image and an output image, lets an autoregressive vision-language model generalize beyond the terminological task categories it was trained on. After supervised fine-tuning on the DECVT dataset, the model performs previously seen tasks from instructions it has not encountered, and, when entire tasks such as HED-to-Image, Canny-to-Image, and Depth-to-Image are held out of training, it can still produce plausible outputs when given a textual explanation of the desired transformation. The paper reports that generalization is asymmetric: the model succeeds on generation tasks that turn edges, maps, or masks into images, and on low-level tasks such as low-light enhancement and deblurring, but fails on the inverse tasks such as Image-to-Canny and Image-to-Depth, a failure the authors attribute to missing alignment between the image tokenizer and text modality in the pretrained initialization. On standard benchmarks the fine-tuned model still lags behind task-specific and specialist generalist models, so the claimed contribution is the new form of task specification and the zero-shot capability it enables, not state-of-the-art generation quality.

Load-bearing premise

The load-bearing premise is that the zero-shot behavior shown for unseen tasks comes from the explanatory-instruction fine-tuning and not from abilities already present in the pretrained model used to initialize training; the paper does not compare against a control fine-tuned on the same data with terminological-only instructions.

Editorial extensions

If this is right

  • If the claim holds, a vision model's ability to do a new task depends on being told what the task does, not on having seen that task category in training.
  • The same model can be steered by arbitrary user-written descriptions of image transformations, making instruction following a general interface for image generation and editing.
  • Task-level zero-shot generalization is expected mainly for tasks whose output distribution the pretrained image tokenizer can already represent, such as photographs, and fails for outputs like depth maps and Canny edges that the tokenizer was not aligned to produce.
  • Combining multiple tasks in one fine-tuning run with explanatory instructions supports mixed and combined task objectives, rather than one instruction format per task.
  • Explanatory instructions also help on seen tasks for unseen categories or descriptions: a model can follow 'the creature on the leaf' even when the category name was never trained.

Reading between the lines

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

  • If explanatory instructions rather than pretrained abilities cause the reported behavior, the same dataset format could be applied to video, 3D, or audio-visual transformations, where task vocabularies are even less standardized.
  • The paper's own explanation of failures suggests a concrete test: aligning the image tokenizer with text-image data before fine-tuning should extend task-level zero-shot to inverse tasks such as image-to-depth and image-to-canny; this is an inference, not a paper claim.
  • A control experiment that fine-tunes the same initialization on the same data with terminological labels would isolate whether the explanatory format, rather than data scale and diversity, drives zero-shot generalization.
  • The latent-space continuity argument implies that generalization strength should track the overlap of instruction embeddings between training and test tasks, a prediction the paper does not test.
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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 / 6 minor

Summary. The paper proposes "Explanatory Instructions": free-form linguistic descriptions of image-to-image transformations, as an alternative to short terminological task labels such as "semantic segmentation" or "depth estimation." The authors construct DECVT, a dataset of roughly 12 million "input image → explanatory instruction → output image" triplets, and fine-tune the 7B autoregressive multimodal model Lumina-mGPT-7B on subsets of this data (Sections 3, 4.1). The central claim is that after fine-tuning, the model acquires (a) instruction-level zero-shot generalization to unseen paraphrases of instructions and (b) task-level zero-shot generalization to entire vision tasks omitted from training, such as HED-to-Image, Canny-to-Image, and Depth-to-Image (Section 4.2, Figures 6-8). The quantitative sections (Tables 1, 2, and Appendix B) report results on Canny-to-Image, HED-to-Image, inpainting/outpainting, dense prediction, and low-level restoration tasks, showing that the fine-tuned model is competitive with the vanilla Lumina-mGPT initialization but generally well below task-specific and vision-generalist baselines. The paper also includes explicit limitation statements in Section 5 and Appendix D, acknowledging that task-level zero-shot is unstable and fails for Image-to-Canny and Image-to-Depth.

Significance. If the causal claim were established—that a textual explanation of a transformation is what enables a vision-language model to execute a task it never trained on—this would be a meaningful step toward task-level zero-shot generalization in computer vision, analogous to instruction following in NLP. The paper has several strengths: the DECVT dataset is large and integrates many tasks and inverse directions; the method is simple and reproducible in principle; the authors do not claim state-of-the-art performance; and the limitation sections are unusually candid about instabilities and poor cases. The main weakness is evidential: the paper's headline phenomenon is supported almost entirely by three selected qualitative examples in Section 4.2, and the experimental design lacks the control condition needed to attribute the behavior to explanatory instructions rather than to the pretrained initialization or to generic image-to-image fine-tuning. The same GPT-4o pipeline is used to generate both training and evaluation instructions, which further weakens the "zero-shot instruction" interpretation.

major comments (3)
  1. [Section 4.2 and Figures 6-8] The central causal claim—that explanatory instructions, rather than the underlying image-to-image fine-tuning, enable task-level zero-shot generalization—is not tested because the design lacks a control condition. The model is initialized from Lumina-mGPT-7B-768 (Section 3), and Table 1 shows that this initialization already performs the held-out tasks weakly: Canny-to-Image F1=10.09 and HED-to-Image FID=69.49 under fixed terminological instructions. A control model fine-tuned on the same 1.5M-pair subset with only terminological instructions (e.g., "canny edge map to image" or "depth map to image") is necessary to rule out that the observed generalization comes from (a) the pretrained model's latent ability to naturalize edge/depth inputs or (b) generic supervised fine-tuning on diverse image-to-image pairs, independent of instruction wording. Appendix D item 7 states that additional validation experiments could not be run due to resource constraints; this is exactly the missing experiment. Without it, the paper's headline claim remains unverified.
  2. [Section 4.2, Section 5, Tables 1-2] The claimed "strong zero-shot generalization for unseen CV tasks" rests on three qualitative examples (Figures 6-8), and the exact held-out setting of Section 4.2 is never evaluated quantitatively. Tables 1 and 2 use the full-DECVT model from Section 4.1, not the Section 4.2 model from which HED-to-Image, Canny-to-Image, and Depth-to-Image were excluded. Meanwhile, Section 5 states that task-level zero-shot fails for Image-to-Canny and Image-to-Depth, and Appendix D item 7 and the caption of Figure 48 describe the behavior as "unstable" and of "relatively low" success rate. The authors should provide quantitative metrics (e.g., F1, FID, SSIM, or a success-rate estimate) on a random sample of held-out inputs for the Section 4.2 model, and should report failure cases alongside successes; otherwise the strength of the generalization claim cannot be assessed.
  3. [Section 4.1, Appendix A.1, Figures 33-43] Instruction-level zero-shot is evaluated with instructions generated by the same GPT-4o prompt pipeline used to produce the training instructions. The test instructions are therefore drawn from the same distribution as the training instructions, so "unseen" means token-level novelty within one generator distribution rather than distributional novelty. This circularity also affects the task-level instructions in Section 4.2, which are constructed with the same template. The authors should either test with out-of-distribution instruction sources (human-written instructions, a different generation model, or expert paraphrases) or soften the claim from "zero-shot instruction generalization" to "robustness to paraphrases within the GPT-4o instruction distribution."
minor comments (6)
  1. [Section 2 heading] The heading contains a typo: "Computer Vison Tasks" should be "Computer Vision Tasks."
  2. [Abstract and Introduction] The abstract and Section 1 state that code and dataset will be or have been released, but no repository link is included in the manuscript; please add the URL or note that it will be added in the camera-ready version.
  3. [Tables 1 and 2] The labels "T. Z.-s." and "I. Z.-s." are easy to misread: for example, HED-to-Image is marked "I. Z.-s." while Canny-to-Image is marked "T. Z.-s.," even though both are evaluated with unseen instructions. Clarify that Table 1 uses the full-DECVT model, and state explicitly which tasks are seen or unseen for that model.
  4. [Appendix D item 2 and Figure 57] The latent-space visualization claims that explanatory instructions form a continuum while terminological instructions cluster discretely, but it uses only one text encoder and no quantitative separation measure; please add a metric or a second encoder to support the claim.
  5. [Section 3, Inference] The top-k value of 2048 for image generation is a free hyperparameter, and Appendix D notes that its effect is task-dependent; a brief ablation or a discussion of sensitivity would improve reproducibility.
  6. [Appendix D item 5] The hypothesis of an optimal instruction-complexity threshold is not tested; consider labeling it explicitly as a conjecture, since the current phrasing reads as a finding.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the zero-shot claims are empirical, evaluated against external benchmarks; the missing terminological control is a validity concern, not a circular derivation.

full rationale

The paper makes no formal derivation: its central claims are empirical demonstrations that fine-tuning an AR-based VLM on image-to-instruction-to-image triplets yields instruction-level and task-level generalization. I checked each load-bearing step. (1) DECVT construction (Sec. 2 and Appendix A) is an external data-construction pipeline; instructions are generated by GPT-4o from image pairs, not defined in terms of the model's outputs, so there is no self-definitional loop. (2) The task-level zero-shot protocol (Sec. 4.2) excludes HED-to-Image, Depth-to-Image, and related tasks from training and then tests on those tasks; the test instructions are newly generated, and the paper reports quantitative results against external benchmarks such as MultiGen-20M, ADE20K, and NYU-Depth V2, so the evaluation is not a rename of the training objective. (3) Initialization from Lumina-mGPT and citations to Chameleon and PixWizard are architectural or related-work citations; no load-bearing argument reduces to a self-citation. (4) The absence of a control trained on terminological instructions and the fact that evaluation instructions are generated by the same GPT-4o pipeline as training instructions are genuine validity concerns about whether explanatory language per se causes the effect, but under the review rules those are correctness-risk issues, not circularity: no equation, fitted parameter, or cited uniqueness theorem makes the reported behavior equal to the input by construction. I therefore find no significant circularity.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the dataset construction (GPT-4o descriptions), the choice of training objective, and the attribution of zero-shot behavior to the fine-tuning rather than to the pretrained base model. There are no newly invented physical entities; explanatory instructions are a data representation.

free parameters (2)
  • top-k sampling value for image token generation = 2048
    Chosen by hand in Section 3 (Inference); affects image generation quality and is recommended for all image stages. Not fitted to a benchmark target, but a free design choice that influences the zero-shot outputs.
  • DECVT subset sampling ratios for task-level zero-shot experiments = 30% explanatory (50% editing / 50% visual pairs), 20% terminological
    Chosen by hand in Sec 4.2; the reported task-level zero-shot results come from this specific subsample, so the claim depends on this split.
assumptions (3)
  • domain assumption GPT-4o-generated explanatory instructions are an accurate and sufficient representation of the transformation between image pairs.
    The dataset DECVT is built from GPT-4o descriptions (Appendix A.1); if these descriptions are inaccurate or miss task-relevant structure, the model learns a distorted task space. The authors acknowledge in Appendix D.4 that GPT-4o still produces incomplete or deviating descriptions.
  • domain assumption The autoregressive next-token prediction objective on discrete image tokens is an appropriate learning signal for acquiring task-level generalization.
    The method relies on standard next-token training over Lumina-mGPT (Sec 3); this is a design choice, not a proven mechanism for zero-shot task generalization.
  • domain assumption Capabilities demonstrated on held-out tasks after fine-tuning are attributable to the explanatory instruction training rather than to the pretrained initialization.
    Sec 3 initializes from Lumina-mGPT-7B, a multimodal generative model with existing image-to-image abilities; no control training is reported that would separate pretraining inheritance from the effect of DECVT.

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Pith. "Pith review of Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization." pith.science (2026). https://pith.science/paper/S5224KQ3

@misc{pith2026241218525,
  author       = {Pith},
  title        = {Pith review of: Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S5224KQ3}},
  note         = {Machine review of arXiv:2412.18525}
}
abstract

Computer Vision (CV) has yet to fully achieve the zero-shot task generalization observed in Natural Language Processing (NLP), despite following many of the milestones established in NLP, such as large transformer models, extensive pre-training, and the auto-regression paradigm, among others. In this paper, we explore the idea that CV adopts discrete and terminological task definitions (\eg, ``image segmentation''), which may be a key barrier to zero-shot task generalization. Our hypothesis is that without truly understanding previously-seen tasks--due to these terminological definitions--deep models struggle to generalize to novel tasks. To verify this, we introduce Explanatory Instructions, which provide an intuitive way to define CV task objectives through detailed linguistic transformations from input images to outputs. We create a large-scale dataset comprising 12 million ``image input $\to$ explanatory instruction $\to$ output'' triplets, and train an auto-regressive-based vision-language model (AR-based VLM) that takes both images and explanatory instructions as input. By learning to follow these instructions, the AR-based VLM achieves instruction-level zero-shot capabilities for previously-seen tasks and demonstrates strong zero-shot generalization for unseen CV tasks. Code and dataset will be openly available on our GitHub repository.

Figures

Figures reproduced from arXiv: 2412.18525 by the authors.

Figure 1
Figure 1. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Examples of terminological-based vision tasks, e.g., holistically nested edge detection. two components contain approximately 6 million bidirec￾tional pairs of “image ↔ explanatory instructions ↔ image” triplets (i.e., around 12 million individual “input image → explanatory instruction → output” triplets). Below, we pro￾vide an introduction along with example entries for each component. Please refer to Appendix A fo… view at source ↗
Figure 3
Figure 3. Examples of explanatory-based vision tasks. cate lighting compositions, nuanced adjustments to scene composition and visual ambiance, or macro-level visual content changes but with similar features. These complex dynamics extend beyond the capabilities of conventional terminological-based vision tasks and require more intricate language instructions. We collect images to expand this component from search engines, wi… view at source ↗
Figures from the paper (54 more)
Figure 4
Figure 4. Figure 4: Framework of our vanilla token-based VLM method. level zero-shot capabilities using the proposed Explanatory Instructions, which characterize vision tasks through lin￾guistic descriptions of their objectives, rather than to pro￾vide a state-of-the-art solution. Consequ…
Figure 5
Figure 5. Figure 5: Examples of instruction-level zero-shot capabilities. 4. Experiments In this section, we evaluate the model’s zero-shot capabili￾ties on unseen instructions and unseen vision tasks through different experimental settings. 4.1. Zero-shot Capabilities on Unseen Instructi…
Figure 6
Figure 6. Figure 6: Examples of task-level zero-shot capabilities (HED-to-Image). Resolution: 448×448. Explanatory Instruction: “Gradually restore the scene’s natural colors, filling in each region with realistic gradients and textures that represent natural elements. Reintroduce details …
Figure 7
Figure 7. Figure 7: Examples of task-level zero-shot capabilities (Canny-to-Image). Resolution: 448×448. Explanatory Instruction: “Fill in all the empty outlines with rich colors that reflect vibrant tones, while redefining the shapes with smooth textures. Add layers of depth to the flat …
Figure 8
Figure 8. Figure 8: Examples of task-level zero-shot capabilities (Depth-to-Image). Resolution: 448×448. Explanatory Instruction: “Create a realistic coastal scene based on the image. Begin by interpreting the gradient colors to identify the relative distance of landscape elements. Use th…
Figure 9
Figure 9. Figure 9: Data sample for explanatory-based vision tasks. Explanatory instruction from A to B: Shake up the perspective to bring the animal slightly closer, reposition upright with a slight turn of the head. Reduction of debris gives the ground a smoother, even appearance. Expla…
Figure 10
Figure 10. Figure 10: Data sample for explanatory-based vision tasks. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_10.png]
Figure 11
Figure 11. Figure 11: Data sample for explanatory-based vision tasks. Explanatory instruction from A to B: Brighten the overall scene, adding a more sunlit ambiance. Modify the gripping object to reflect a natural, green element while altering the environment to a more elevated, branch-lik…
Figure 12
Figure 12. Figure 12: Data sample for explanatory-based vision tasks. Explanatory instruction from A to B: Replace the white square base with a large wooden board. Display the spheres in a less strict arrangement. Incorporate garlic bulbs and a scattering of spices in the background, compl…
Figure 13
Figure 13. Figure 13: Data sample for explanatory-based vision tasks. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_13.png]
Figure 14
Figure 14. Figure 14: Data sample for explanatory-based vision tasks. Explanatory instruction from A to B: Introduce a background featuring a variety of green leaves and stems, while adding another flower similar to the first one. Ensure the new flower has a more fully opened appearance an…
Figure 15
Figure 15. Figure 15: Data sample for explanatory-based vision tasks. Explanatory instruction from A to B: Adjust the color scheme from a blue-green light spectrum to a combination of red and green, creating a shift in the color dynamics. Alter the central projection, ensuring that the bea…
Figure 16
Figure 16. Figure 16: Data sample for explanatory-based vision tasks. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_16.png]
Figure 17
Figure 17. Figure 17: Data sample for terminological-based vision tasks (Image Restoration). Explanatory instruction from A to B: Enhance color saturation while maintaining the current brightness. Emphasize cooler tones for a more vibrant and lively appearance. Explanatory instruction from…
Figure 18
Figure 18. Figure 18: Data sample for terminological-based vision tasks (Image Restoration). Explanatory instruction from A to B: Drain the image of all bright colors, shifting the mood into a sepia-like monochrome. The dragon figure remains, but now weathered and eroded, appearing as if c…
Figure 19
Figure 19. Figure 19: Data sample for terminological-based vision tasks (Style Transfer). 22 [PITH_FULL_IMAGE:figures/full_fig_p022_19.png]
Figure 20
Figure 20. Figure 20: Data sample for terminological-based vision tasks (Style Transfer). Explanatory instruction from A to B: For the following object categories, apply the corresponding solid color overlays to fully cover them: Color every wineglass object with a aqua solid layer. Paint …
Figure 21
Figure 21. Figure 21: Data sample for terminological-based vision tasks (Segmentation). Explanatory instruction from A to B: Paint over every clock, streetlight object with dodgerblue, covering them entirely. Explanatory instruction from B to A: Erase the dodgerblue overlay on the clock, s…
Figure 22
Figure 22. Figure 22: Data sample for terminological-based vision tasks (Segmentation). 23 [PITH_FULL_IMAGE:figures/full_fig_p023_22.png]
Figure 23
Figure 23. Figure 23: Data sample for terminological-based vision tasks (Object Detection). Explanatory instruction from A to B: Remove the raindrop patterns that obscure sections of the scene to reveal a clear, unob￾structed view. Enhance the visibility of details by eliminating blurred s…
Figure 24
Figure 24. Figure 24: Data sample for terminological-based vision tasks (Deraining). Explanatory instruction from A to B: Introduce a layer of fog that softly envelops the scenery, generating a slightly diffused appearance with reduced contrast. Increase the density of mist, particularly a…
Figure 25
Figure 25. Figure 25: Data sample for terminological-based vision tasks (Dehazing). 24 [PITH_FULL_IMAGE:figures/full_fig_p024_25.png]
Figure 26
Figure 26. Figure 26: Data sample for terminological-based vision tasks (Desnowing). Explanatory instruction from A to B: Simplify the scene into a grayscale silhouette, emphasizing the outlines and contours of prominent subjects while eliminating detailed features and textures. Fade out b…
Figure 27
Figure 27. Figure 27: Data sample for terminological-based vision tasks (Depth Estimation). Explanatory instruction from A to B: Introduce a series of vividly colored dots and lines across the canine figure. Each line should connect to form an intricate pattern that traces the outline and …
Figure 28
Figure 28. Figure 28: Data sample for terminological-based vision tasks (Pose Estimation). 25 [PITH_FULL_IMAGE:figures/full_fig_p025_28.png]
Figure 29
Figure 29. Figure 29: Data sample for terminological-based vision tasks (Pose Estimation). Explanatory instruction from A to B: Apply a transformation that generalizes the depiction of depth and orientation using a colorful gradient representing angles across the surface. Structures become…
Figure 30
Figure 30. Figure 30: Data sample for terminological-based vision tasks (Surface Normal Estimation). Explanatory instruction from A to B: Highlight only major edges, transforming the image into a boundary map. Explanatory instruction from B to A: Convert boundary outlines into a realistic …
Figure 31
Figure 31. Figure 31: Data sample for terminological-based vision tasks (HED Boundaries). 26 [PITH_FULL_IMAGE:figures/full_fig_p026_31.png]
Figure 32
Figure 32. Figure 32: Explanatory instructions assist the model in understanding task objectives. (Segmentation). 29 [PITH_FULL_IMAGE:figures/full_fig_p029_32.png]
Figure 33
Figure 33. Figure 33: Instruction-level zero-shot samples for quantitative experiments (Deraining). Input Image Unseen Explanatory Instruction: “The vibrancy intensifies, revealing a spectrum of colors that emphasize the angles and orientations of each surface in the scene.” Ground Truth I…
Figure 34
Figure 34. Figure 34: Instruction-level zero-shot samples for quantitative experiments (Surface Normal Estimation). 30 [PITH_FULL_IMAGE:figures/full_fig_p030_34.png]
Figure 35
Figure 35. Figure 35: Instruction-level zero-shot samples for quantitative experiments (Depth Estimation). Input Image Unseen Explanatory Instruction: “Enhance the clarity by reducing the haze, adjusting contrast and brightness levels for a sharper and more detailed view.” Ground Truth Inp…
Figure 36
Figure 36. Figure 36: Instruction-level zero-shot samples for quantitative experiments (Dehazing). 31 [PITH_FULL_IMAGE:figures/full_fig_p031_36.png]
Figure 37
Figure 37. Figure 37: Instruction-level zero-shot samples for quantitative experiments (Semantic Segmentation). Input Image Unseen Explanatory Instruction: “Adding rich layers of color and texture to the outlines would allow for a return of the original setting’s vibrancy. Explosive growth…
Figure 38
Figure 38. Figure 38: Instruction-level zero-shot samples for quantitative experiments (HED-to-Image). 32 [PITH_FULL_IMAGE:figures/full_fig_p032_38.png]
Figure 39
Figure 39. Figure 39: Task level & instruction-level zero-shot samples for quantitative experiments (Canny-to-Image). Input Image Unseen Explanatory Instruction: “To improve visibility and clarity, increase the brightness uniformly across the entire area. Next, adjust the contrast to diffe…
Figure 40
Figure 40. Figure 40: Task level & instruction-level zero-shot samples for quantitative experiments (Low-light Enhancement). 33 [PITH_FULL_IMAGE:figures/full_fig_p033_40.png]
Figure 41
Figure 41. Figure 41: Task level & instruction-level zero-shot samples for quantitative experiments (Inpainting). Input Image Unseen Explanatory Instruction: “Expand the vertical floral display into a broader composition, ensuring the blooms are evenly spread across, with minimal visible s…
Figure 42
Figure 42. Figure 42: Task level & instruction-level zero-shot samples for quantitative experiments (Outpainting). 34 [PITH_FULL_IMAGE:figures/full_fig_p034_42.png]
Figure 43
Figure 43. Figure 43: Task level & instruction-level zero-shot samples for quantitative experiments (Under-display Camera Image Restoration). 35 [PITH_FULL_IMAGE:figures/full_fig_p035_43.png]
Figure 44
Figure 44. Figure 44: Examples of both task- and instruction-level zero-shot capabilities (Low-light Enhancement). Resolution: 448×448. Ex￾planatory Instruction: “Increase the overall brightness to reveal details in dark areas while preserving highlights. Adjust the contrast to enhance the…
Figure 45
Figure 45. Figure 45: Examples of both task- and instruction-level zero-shot capabilities (Map-to-Image). Resolution: 448×448. Explana￾tory Instruction: “Apply a process that introduces intricate details by simulating realistic textures, adding natural elements like trees and vegetation, a…
Figure 46
Figure 46. Figure 46: Examples of instruction-level zero-shot capabilities (Deraining). Resolution: 448×448. Explanatory Instruction: “Slowly remove the rain falling from the sky in the image, still maintain the state of night, and the girl on the bridge is also still holding the umbrella,…
Figure 47
Figure 47. Figure 47: Examples of instruction-level zero-shot capabilities (Desnowing). Resolution: 448×448. Explanatory Instruction: “Remove the falling snow from the sky in the image, keep the other objects and snow in the image, still keep it dark, but pay attention to the adjustment of…
Figure 48
Figure 48. Figure 48: Examples of both task- and instruction-level zero-shot capabilities (Deblurring). Resolution: 448×448. Explanatory Instruction: “The image shows noticeable multiple visual overlaps of trees and buildings. I would like to remove visual overlaps and restore a clear, sha…
Figure 49
Figure 49. Figure 49: Examples of instruction-level zero-shot capabilities (Dehazing). Resolution: 448×448. Explanatory Instruction: “Retain the distant clouds in the image while removing as much fog as possible. Attempt to restore the faintly visible sun in the distance, but ensure there …
Figure 50
Figure 50. Figure 50: Examples of instruction-level zero-shot capabilities (Depth Estimation). Resolution: 448×448. Input Image Unseen Explanatory Instruction: “Translate the visible structures into a range of bright colors reflecting orientation angles, enhancing variations across surface…
Figure 51
Figure 51. Figure 51: Examples of instruction-level zero-shot capabilities (Surface Normal Estimation). Resolution: 448×448. 38 [PITH_FULL_IMAGE:figures/full_fig_p038_51.png]
Figure 52
Figure 52. Figure 52: Examples of instruction-level zero-shot capabilities (Semantic Segmentation). Resolution: 448×448. Input Image Unseen Explanatory Instruction: “Mark the cat-like creature in the picture with a yellow box.” Output Image Ground Truth Input Image Unseen Explanatory Instr…
Figure 53
Figure 53. Figure 53: Examples of instruction-level zero-shot capabilities (Object Detection). Resolution: 448×448. 39 [PITH_FULL_IMAGE:figures/full_fig_p039_53.png]
Figure 54
Figure 54. Figure 54: Examples of instruction-level zero-shot capabilities (HED Boundary Detection). Resolution: 448×448. Input Image Unseen Explanatory Instruction: “Gradually reduce atmospheric interference, allowing clearer visibility of buildings and sharpening the outlines. Enhance cl…
Figure 55
Figure 55. Figure 55: Examples of instruction-level zero-shot capabilities (Dehazing). Resolution: 448×448. 40 [PITH_FULL_IMAGE:figures/full_fig_p040_55.png]
Figure 56
Figure 56. Figure 56: Examples of instruction-level zero-shot capabilities (Deraining). Resolution: 448×448. 41 [PITH_FULL_IMAGE:figures/full_fig_p041_56.png]
Figure 57
Figure 57. Figure 57: Latent space visualization of explanatory instructions. 2) When visualized in a reduced feature space, fixed task-specific instructions (e.g., Semantic Segmentation) exhibit discrete clustering—a property that inherently ties task execution to rigid syntactic forms, p…

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Under One Sun: Multi-Object Generative Perception of Materials and Illumination

    cs.CV 2026-03 conditional novelty 6.0 of 10

    Factorizing video editing into semantic-token anchoring and motion-restoration pre-training produces strong zero-shot and SOTA open-source instruction-guided video edits without heavy external structural priors.

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Works this paper leans on

68 extracted references · 62 canonical work pages · cited by 1 Pith paper

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    Traditional stone building with a steeply pitched red-tiled roof

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    2) Fill the selected area with a bright, distinct color to make the sky stand out

    Color the Sky: 1) Select the sky area. 2) Fill the selected area with a bright, distinct color to make the sky stand out

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    2) Apply a semi- transparent hue over the selected area to give it a shaded appearance while still allowing the underlying details to be visible

    Shade the Building: 1) Select the entire building and its extension, including the roof and chimneys. 2) Apply a semi- transparent hue over the selected area to give it a shaded appearance while still allowing the underlying details to be visible

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    2) Fill this area with a bright, vibrant color to emphasize the grassy area

    Color the Lawn: 1) Select the lawn area in front of the building. 2) Fill this area with a bright, vibrant color to emphasize the grassy area

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    2) Apply a distinct, muted color to the wall to make it stand out from the lawn and building

    Highlight the Stone Wall: 1) Select the stone wall in front of the building. 2) Apply a distinct, muted color to the wall to make it stand out from the lawn and building

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    2) Fill this area with a bright, distinct color to differentiate it from the surrounding elements

    Color the Bush: 1) Identify and select the bush or shrub to the right of the stone wall. 2) Fill this area with a bright, distinct color to differentiate it from the surrounding elements. Explanatory instruction from B to A:

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    2) Replace the bright color with a natural sky color, making it appear more realistic

    Restore the Sky: 1)Select the area of the sky that has been filled with a bright, distinct color. 2) Replace the bright color with a natural sky color, making it appear more realistic

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    2) Remove the shading to restore the building’s natural appearance, showing its original material and color

    Remove the Building Shading: 1) Select the area of the building and its extension shaded with a semi-transparent hue. 2) Remove the shading to restore the building’s natural appearance, showing its original material and color

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    2) Replace the bright color with a natural grass color to make it look realistic

    Restore the Lawn Color: 1) Select the area of the lawn that has been filled with a bright, vibrant color. 2) Replace the bright color with a natural grass color to make it look realistic

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    Remove the Highlight from the Stone Wall: 1) Select the stone wall that has been highlighted with a distinct, muted color

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    gpt-4o-2024-05-13

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    Alter the grass color to a vivid, artificial-looking green

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    Simplify the textures and details to create a more flat, cartoon-like appearance. Explanatory instruction from B to A: To transform Image B into something resembling Image A without referencing A’s content:

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    Enhance the texture and details of the building to create a more realistic, photographic appearance. Table 5. Examples of outputs from “claude-3-5-sonnet-20240620”. 17 Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization Caption of ...

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    In contrast, explanatory instructions span a continuous spectrum across tasks, which we identify as the primary driver of zero-shot generalization capability

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    A partially revealed internal object within a structure

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