Paper Citation Record · LEDGER

Compositional Scene Understanding through Inverse Generative Modeling

As of 9 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 1 inbound Pith citation observation for arXiv:2505.21780.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.21780 v4

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:31:08.798977Z

measured 71 of 71 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T17:14:01.979367Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

70 of 70 outbound references displayed

verified exact3
verified fuzzy23
unresolved44
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3b31ca26-b0ed-4157-a0a2-6884242e4bf6 · outbound

This paper cites write newline.

Compositional Scene Understanding through Inverse Generative Modeling write newline

Reference 1

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This paper cites @esa (Ref.

Compositional Scene Understanding through Inverse Generative Modeling @esa (Ref

Reference 2

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This paper cites an unresolved cited work.

Compositional Scene Understanding through Inverse Generative Modeling Unresolved cited work

Reference 3

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Observation bed0d9c1-e908-4e70-8fd0-1091925d713e · outbound

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Compositional Scene Understanding through Inverse Generative Modeling Unresolved cited work

Reference 4

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This paper cites Backpropagation and stochastic gradient descent method.

Compositional Scene Understanding through Inverse Generative Modeling Backpropagation and stochastic gradient descent method

Reference 5

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Observation 5c614754-a8ea-46f4-9e99-7873952088df · outbound

This paper cites SegDiff: Image Segmentation with Diffusion Probabilistic Models.

Compositional Scene Understanding through Inverse Generative Modeling SegDiff: Image Segmentation with Diffusion Probabilistic Models

Reference 6

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Observation 4df89546-87ce-4569-bbb1-c212e565da7b · outbound

This paper cites Break-a-scene: Extracting multiple concepts from a single image.

Compositional Scene Understanding through Inverse Generative Modeling Break-a-scene: Extracting multiple concepts from a single image

Reference 7

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Observation 235b3296-d247-4fc6-be10-f2b01069f787 · outbound

This paper cites Towards compositional understanding of the world by agent-based deep learning.

Compositional Scene Understanding through Inverse Generative Modeling Towards compositional understanding of the world by agent-based deep learning

Reference 8

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Observation d444a0d3-fb85-40c2-b20b-cee3d8ecdffc · outbound

This paper cites Recognition-by-components: a theory of human image understanding.

Compositional Scene Understanding through Inverse Generative Modeling Recognition-by-components: a theory of human image understanding

Reference 9

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Observation b01a33cc-c4ff-43b5-b85a-a704329e58c5 · outbound

This paper cites A., Kornblith, S., Chen, T., Parmar, N., Minderer, M., and Norouzi, M.

Compositional Scene Understanding through Inverse Generative Modeling A., Kornblith, S., Chen, T., Parmar, N., Minderer, M., and Norouzi, M

Reference 10

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Observation 5ab40c78-8d67-44dc-8205-47d9268e16e6 · outbound

This paper cites Your Diffusion Model is Secretly a Certifiably Robust Classifier.

Compositional Scene Understanding through Inverse Generative Modeling Your Diffusion Model is Secretly a Certifiably Robust Classifier

Reference 11

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Observation f14f2db2-43ef-419a-a265-24a8110ab24e · outbound

This paper cites Enhanced Controllability of Diffusion Models via Feature Disentanglement and Realism-Enhanced Sampling Methods.

Compositional Scene Understanding through Inverse Generative Modeling Enhanced Controllability of Diffusion Models via Feature Disentanglement and Realism-Enhanced Sampling Methods

Reference 12

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Observation 2abf83de-89c8-45bb-ba41-68219f1837a6 · outbound

This paper cites Aspects of the Theory of Syntax.

Compositional Scene Understanding through Inverse Generative Modeling Aspects of the Theory of Syntax

Reference 13

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Observation 95f45988-d245-44cf-9d13-f686c5ca4ef5 · outbound

This paper cites and Jaini, P.

Compositional Scene Understanding through Inverse Generative Modeling and Jaini, P

Reference 14

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Observation b8596139-f898-4c9a-8c8b-645f962fc9cc · outbound

This paper cites Attribute-Centric Compositional Text-to-Image Generation.

Compositional Scene Understanding through Inverse Generative Modeling Attribute-Centric Compositional Text-to-Image Generation

Reference 15

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Observation a0492db3-2538-4a8d-a29b-6ef3ed2e221d · outbound

This paper cites Compositional Generative Modeling: A Single Model is Not All You Need.

Compositional Scene Understanding through Inverse Generative Modeling Compositional Generative Modeling: A Single Model is Not All You Need

Reference 16

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Observation 3e2cff6b-91bc-40fc-9105-dd73f7582392 · outbound

This paper cites Implicit Generation and Generalization in Energy-Based Models.

Compositional Scene Understanding through Inverse Generative Modeling Implicit Generation and Generalization in Energy-Based Models

Reference 17

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Observation a7d0bc0e-0f44-4333-a5a2-00c842ba1b0c · outbound

This paper cites Compositional visual generation with energy based models.

Compositional Scene Understanding through Inverse Generative Modeling Compositional visual generation with energy based models

Reference 18

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Observation c1e164b3-df9f-4ea9-a0a0-8f27b8a75df3 · outbound

This paper cites J., and Mordatch, I.

Compositional Scene Understanding through Inverse Generative Modeling J., and Mordatch, I

Reference 19

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Observation 1060ef90-6c81-49be-a3bf-26be9a2933f7 · outbound

This paper cites Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC.

Compositional Scene Understanding through Inverse Generative Modeling Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC

Reference 20

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This paper cites Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis.

Compositional Scene Understanding through Inverse Generative Modeling Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis

Reference 21

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Compositional Scene Understanding through Inverse Generative Modeling Unresolved cited work

Reference 22

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Compositional Scene Understanding through Inverse Generative Modeling Unresolved cited work

Reference 23

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Observation 367dfec8-b03e-42e2-bf68-514de2876fdc · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Compositional Scene Understanding through Inverse Generative Modeling An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 24

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Observation f51eac6b-6f16-444a-be13-94261d4ea826 · outbound

This paper cites H., Chechik, G., and Cohen-Or, D.

Compositional Scene Understanding through Inverse Generative Modeling H., Chechik, G., and Cohen-Or, D

Reference 25

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This paper cites ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.

Compositional Scene Understanding through Inverse Generative Modeling ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

Reference 26

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Compositional Scene Understanding through Inverse Generative Modeling Unresolved cited work

Reference 27

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This paper cites On the Binding Problem in Artificial Neural Networks.

Compositional Scene Understanding through Inverse Generative Modeling On the Binding Problem in Artificial Neural Networks

Reference 28

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Compositional Scene Understanding through Inverse Generative Modeling Deep residual learning for image recognition

Reference 29

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This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Compositional Scene Understanding through Inverse Generative Modeling A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 30

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Compositional Scene Understanding through Inverse Generative Modeling Unresolved cited work

Reference 31

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Compositional Scene Understanding through Inverse Generative Modeling Denoising diffusion probabilistic models

Reference 32

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Compositional Scene Understanding through Inverse Generative Modeling Composer: Creative and Controllable Image Synthesis with Composable Conditions

Reference 33

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This paper cites Intriguing properties of generative classifiers.

Compositional Scene Understanding through Inverse Generative Modeling Intriguing properties of generative classifiers

Reference 34

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This paper cites Clevr: A diagnostic dataset for compositional language and elementary visual reasoning.

Compositional Scene Understanding through Inverse Generative Modeling Clevr: A diagnostic dataset for compositional language and elementary visual reasoning

Reference 35

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This paper cites ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation.

Compositional Scene Understanding through Inverse Generative Modeling ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

Reference 36

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Compositional Scene Understanding through Inverse Generative Modeling Unresolved cited work

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Compositional Scene Understanding through Inverse Generative Modeling C., Prabhudesai, M., Duggal, S., Brown, E., and Pathak, D

Reference 39

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Compositional Scene Understanding through Inverse Generative Modeling C., Kumar, A., and Pathak, D

Reference 40

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Compositional Scene Understanding through Inverse Generative Modeling Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 41

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Compositional Scene Understanding through Inverse Generative Modeling Composing Ensembles of Pre-trained Models via Iterative Consensus

Reference 42

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Compositional Scene Understanding through Inverse Generative Modeling Learning to compose visual relations

Reference 43

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Compositional Scene Understanding through Inverse Generative Modeling Compositional Visual Generation with Composable Diffusion Models

Reference 44

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Compositional Scene Understanding through Inverse Generative Modeling B., and Torralba, A

Reference 45

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Compositional Scene Understanding through Inverse Generative Modeling Deep learning face attributes in the wild

Reference 46

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Compositional Scene Understanding through Inverse Generative Modeling Object-centric learning with slot attention, 2020

Reference 47

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Compositional Scene Understanding through Inverse Generative Modeling Compositional Risk Minimization

Reference 48

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Compositional Scene Understanding through Inverse Generative Modeling Few-Shot Task Learning through Inverse Generative Modeling

Reference 49

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Compositional Scene Understanding through Inverse Generative Modeling and Jordan, M

Reference 50

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This paper cites Controllable and compositional generation with latent-space energy-based models.

Compositional Scene Understanding through Inverse Generative Modeling Controllable and compositional generation with latent-space energy-based models

Reference 51

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This paper cites Do imagenet classifiers generalize to imagenet? In International conference on machine learning, pp.\ 5389--5400.

Compositional Scene Understanding through Inverse Generative Modeling Do imagenet classifiers generalize to imagenet? In International conference on machine learning, pp.\ 5389--5400

Reference 52

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This paper cites You only look once: Unified, real-time object detection.

Compositional Scene Understanding through Inverse Generative Modeling You only look once: Unified, real-time object detection

Reference 53

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Compositional Scene Understanding through Inverse Generative Modeling High-resolution image synthesis with latent diffusion models, 2022

Reference 54

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Observation 78d6de68-f992-42a7-ab05-54765b74ffaf · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Compositional Scene Understanding through Inverse Generative Modeling U-net: Convolutional networks for biomedical image segmentation

Reference 55

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This paper cites Visual Representation Learning Does Not Generalize Strongly Within the Same Domain.

Compositional Scene Understanding through Inverse Generative Modeling Visual Representation Learning Does Not Generalize Strongly Within the Same Domain

Reference 56

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This paper cites Bridging the Gap to Real-World Object-Centric Learning.

Compositional Scene Understanding through Inverse Generative Modeling Bridging the Gap to Real-World Object-Centric Learning

Reference 57

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This paper cites Detecting and recovering sequential deepfake manipulation.

Compositional Scene Understanding through Inverse Generative Modeling Detecting and recovering sequential deepfake manipulation

Reference 58

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Compositional Scene Understanding through Inverse Generative Modeling Unresolved cited work

Reference 59

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Compositional Scene Understanding through Inverse Generative Modeling Unresolved cited work

Reference 60

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This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Compositional Scene Understanding through Inverse Generative Modeling Deep unsupervised learning using nonequilibrium thermodynamics

Reference 61

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This paper cites Learning Disentangled Prompts for Compositional Image Synthesis.

Compositional Scene Understanding through Inverse Generative Modeling Learning Disentangled Prompts for Compositional Image Synthesis

Reference 62

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This paper cites Compositional Image Decomposition with Diffusion Models.

Compositional Scene Understanding through Inverse Generative Modeling Compositional Image Decomposition with Diffusion Models

Reference 63

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This paper cites Measuring robustness to natural distribution shifts in image classification.

Compositional Scene Understanding through Inverse Generative Modeling Measuring robustness to natural distribution shifts in image classification

Reference 64

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This paper cites N., Vapnik, V., et al.

Compositional Scene Understanding through Inverse Generative Modeling N., Vapnik, V., et al

Reference 65

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This paper cites Hierarchical open-vocabulary universal image segmentation.

Compositional Scene Understanding through Inverse Generative Modeling Hierarchical open-vocabulary universal image segmentation

Reference 66

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This paper cites Slot-vae: Object-centric scene generation with slot attention.

Compositional Scene Understanding through Inverse Generative Modeling Slot-vae: Object-centric scene generation with slot attention

Reference 67

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This paper cites Compositional generalization from first principles.

Compositional Scene Understanding through Inverse Generative Modeling Compositional generalization from first principles

Reference 68

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This paper cites Unleashing text-to-image diffusion models for visual perception.

Compositional Scene Understanding through Inverse Generative Modeling Unleashing text-to-image diffusion models for visual perception

Reference 69

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This paper cites RoboDreamer: Learning Compositional World Models for Robot Imagination.

Compositional Scene Understanding through Inverse Generative Modeling RoboDreamer: Learning Compositional World Models for Robot Imagination

Reference 70

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Pith citing papers

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CARV: A Diagnostic Benchmark for Compositional Analogical Reasoning in Multimodal LLMs cites this paper.

CARV: A Diagnostic Benchmark for Compositional Analogical Reasoning in Multimodal LLMs Compositional Scene Understanding through Inverse Generative Modeling

Reference 280

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