Paper Citation Record · LEDGER

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models

As of 20 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2412.19920.

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

pith.paper-citation-record.v1
2412.19920 v1

Coverage vector

measured 57 of 57 reference resolution

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measured 58 of 58 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:49:31.472256Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

57 of 57 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0a792ff9-0916-4dbe-89b8-4c096aed1348 · outbound

This paper cites Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects

Reference 1

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Observation 2c7ee7c7-278d-4092-b93a-730d38aa4af3 · outbound

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

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Recognition-by-components: a theory of human image understanding

Reference 2

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Observation 8f91061e-f7bb-4444-aa1e-1af1a5932298 · outbound

This paper cites SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement

Reference 3

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Observation b93ff618-e5b0-4797-8252-3660c10e7fbe · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Emerg- ing properties in self-supervised vision transformers

Reference 4

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This paper cites Emerg- ing properties in self-supervised vision transformers.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Emerg- ing properties in self-supervised vision transformers

Reference 5

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Observation 10dbafcb-10ae-42ce-988a-1a9122ff4799 · outbound

This paper cites Cnnˆ{2}: View- point generalization via a binocular vision.Advances in Neu- ral Information Processing Systems, 32, 2019.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Cnnˆ{2}: View- point generalization via a binocular vision.Advances in Neu- ral Information Processing Systems, 32, 2019

Reference 6

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Observation d4e8acbd-36e4-4717-b8d6-346e1379179c · outbound

This paper cites When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations

Reference 7

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Observation 906aeb83-57b8-406f-85fe-c0e63e62b4c0 · outbound

This paper cites PaLI: A Jointly-Scaled Multilingual Language-Image Model.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models PaLI: A Jointly-Scaled Multilingual Language-Image Model

Reference 8

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Observation ece909c3-f819-4140-bba4-09c400a17a94 · outbound

This paper cites Object modelling by regis- tration of multiple range images.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Object modelling by regis- tration of multiple range images

Reference 9

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Observation a1688fc8-0637-4cf5-90c9-f02e7fe98691 · outbound

This paper cites Abo: Dataset and benchmarks for real-world 3d object un- derstanding.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Abo: Dataset and benchmarks for real-world 3d object un- derstanding

Reference 10

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Observation 59e2f809-c901-4ad6-b725-c00ea9ff52d5 · outbound

This paper cites Support-vector networks.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Support-vector networks

Reference 11

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Observation 04e403c3-19e0-4ac8-bf81-ec1b02339769 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Imagenet: A large-scale hierarchical image database

Reference 12

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This paper cites Adam: A method for stochastic opti- mization.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Adam: A method for stochastic opti- mization

Reference 13

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Observation 993bd37e-b7ef-402c-82a7-13e34e7f6e1e · outbound

This paper cites Viewfool: Evaluating the robustness of visual recognition to adversarial viewpoints.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Viewfool: Evaluating the robustness of visual recognition to adversarial viewpoints

Reference 14

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Observation a9044f40-52d0-4b97-9395-813b41352b4f · outbound

This paper cites Dense and aligned captions (dac) promote compositional reasoning in vl models.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Dense and aligned captions (dac) promote compositional reasoning in vl models

Reference 15

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Observation 5e523792-67f9-4aea-aafb-9f952a44c8a0 · outbound

This paper cites Prob- ing the 3d awareness of visual foundation models.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Prob- ing the 3d awareness of visual foundation models

Reference 16

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Observation b31cedd5-9670-45d6-9053-1a11206e75e6 · outbound

This paper cites Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Communications of the ACM, 24(6):381–395, 1981.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Communications of the ACM, 24(6):381–395, 1981

Reference 17

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Observation 5f12896b-9765-4faf-89b5-a76660fe86f1 · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 18

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This paper cites The generic viewpoint assumption in a framework for visual perception.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models The generic viewpoint assumption in a framework for visual perception

Reference 19

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Observation 5db54988-54c5-4953-9f9c-01ef31d56f49 · outbound

This paper cites Exploiting the generic viewpoint as- sumption.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Exploiting the generic viewpoint as- sumption

Reference 20

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Observation 99b29d25-c7fe-48db-a785-91cdf050f1e6 · outbound

This paper cites DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data

Reference 21

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Observation 89f6c873-f5bf-4607-a3a5-18d9eab2e4c0 · outbound

This paper cites Towards viewpoint invariant 3d human pose estimation.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Towards viewpoint invariant 3d human pose estimation

Reference 22

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Observation fdde608b-1316-41b7-9258-8f51f2effb18 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Masked autoencoders are scalable vision learners

Reference 23

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This paper cites Tanks and temples: Benchmarking large-scale scene reconstruction.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Tanks and temples: Benchmarking large-scale scene reconstruction

Reference 24

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This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 25

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This paper cites Microsoft coco: Common objects in context.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Microsoft coco: Common objects in context

Reference 26

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This paper cites Improved baselines with visual instruction tuning.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Improved baselines with visual instruction tuning

Reference 27

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This paper cites A convnet for the 2020s.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models A convnet for the 2020s

Reference 28

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This paper cites When and how con- volutional neural networks generalize to out-of-distribution category–viewpoint combinations.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models When and how con- volutional neural networks generalize to out-of-distribution category–viewpoint combinations

Reference 29

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This paper cites Understanding Zero-Shot Adversarial Robustness for Large-Scale Models.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 30

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This paper cites A survey of advances in vision-based human motion capture and analysis.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models A survey of advances in vision-based human motion capture and analysis

Reference 31

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This paper cites View-point invariant 3d classification for mobile robots using a convo- lutional neural network.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models View-point invariant 3d classification for mobile robots using a convo- lutional neural network

Reference 32

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This paper cites Quality not quantity: On the interaction between dataset design and robustness of clip.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Quality not quantity: On the interaction between dataset design and robustness of clip

Reference 33

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models DINOv2: Learning Robust Visual Features without Supervision

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This paper cites Styleclip: Text-driven manipulation of stylegan imagery.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Styleclip: Text-driven manipulation of stylegan imagery

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This paper cites Learn- ing transferable visual models from natural language super- vision.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Learn- ing transferable visual models from natural language super- vision

Reference 36

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Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Learning transferable visual models from natural language supervi- sion

Reference 37

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This paper cites Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction

Reference 38

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This paper cites Silhouettes: a graphical aid to the inter- pretation and validation of cluster analysis.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Silhouettes: a graphical aid to the inter- pretation and validation of cluster analysis

Reference 39

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This paper cites Towards viewpoint-invariant visual recognition via adversarial training.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Towards viewpoint-invariant visual recognition via adversarial training

Reference 40

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This paper cites Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models

Reference 41

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Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Imagenet large scale visual recognition challenge

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This paper cites On the adversarial robustness of multi-modal foundation models.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models On the adversarial robustness of multi-modal foundation models

Reference 43

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This paper cites Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Reference 44

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This paper cites Learning viewpoint-agnostic visual representations by recovering to- kens in 3d space.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Learning viewpoint-agnostic visual representations by recovering to- kens in 3d space

Reference 45

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This paper cites From big data to knowledge in ai.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models From big data to knowledge in ai

Reference 46

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This paper cites Accidental pin- hole and pinspeck cameras: Revealing the scene outside the picture.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Accidental pin- hole and pinspeck cameras: Revealing the scene outside the picture

Reference 47

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Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Deit iii: Revenge of the vit

Reference 48

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models LLaMA: Open and Efficient Foundation Language Models

Reference 49

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This paper cites A closer look at the robustness of contrastive language-image pre-training (clip).

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models A closer look at the robustness of contrastive language-image pre-training (clip)

Reference 50

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Observation 28112453-37d5-4570-bb27-10b3a5dcf5f5 · outbound

This paper cites Sam-clip: Merging vision foundation models to- wards semantic and spatial understanding.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Sam-clip: Merging vision foundation models to- wards semantic and spatial understanding

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:24.432298Z

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Observation 38c142ab-31a8-4b63-b222-6fee9de5a265 · outbound

This paper cites On the role of structure in vision.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models On the role of structure in vision

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:24.391829Z

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Observation b1f50270-393c-44c4-a088-05ca5efbb8cc · outbound

This paper cites Viewpoint invari- ant human re-identification in camera networks using pose priors and subject-discriminative features.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Viewpoint invari- ant human re-identification in camera networks using pose priors and subject-discriminative features

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:24.363774Z

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Observation 357544e4-985e-4bf2-b60b-0da8460138cb · outbound

This paper cites Discovering viewpoint-invariant relationships that characterize objects.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Discovering viewpoint-invariant relationships that characterize objects

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:24.330256Z

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Observation ba492d60-0684-439d-b57a-02e83e0eda72 · outbound

This paper cites Sigmoid loss for language image pre-training.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Sigmoid loss for language image pre-training

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T23:53:23.726656Z

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Observation 0e8e5063-2609-45dd-92b6-bf077944920a · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models BERTScore: Evaluating Text Generation with BERT

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T23:53:23.734282Z

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Observation ff4a890b-563a-44f0-8107-8ba7957ee468 · outbound

This paper cites Ood-cv: A benchmark for robustness to indi- vidual nuisances in real-world out-of-distribution shifts.

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models Ood-cv: A benchmark for robustness to indi- vidual nuisances in real-world out-of-distribution shifts

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:24.260473Z

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Pith citing papers

Observation ad65c6c9-6016-4209-9f4b-6c4b6dd7375b · inbound

MSG-Loc: Multi-Label Likelihood-based Semantic Graph Matching for Object-Level Global Localization cites this paper.

MSG-Loc: Multi-Label Likelihood-based Semantic Graph Matching for Object-Level Global Localization Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T18:49:31.472256Z

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