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Paper Citation Record · LEDGER

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models

As of 21 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2505.03569.

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

pith.paper-citation-record.v1
2505.03569 v2

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:53:56.720892Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

13 of 13 outbound references displayed

  • verified exact0
  • verified fuzzy6
  • unresolved4
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 43fdf467-6df5-413f-aee9-4ea6ca444522 · outbound

This paper cites ImageNet- Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification.

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models ImageNet- Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:53:56.885481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T23:53:56.690866Z digest=sha256:42fdc13927faa9e43ce3b395d16f56f68c9ed4280bed3c6298e2c1e22524a2eb

Observation 1d6bce0e-6d33-4b52-8fd6-b4c73e78aab6 · outbound

This paper cites Inpaint Anything: Segment Anything Meets Image Inpainting.

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models Inpaint Anything: Segment Anything Meets Image Inpainting

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-15T23:53:56.698553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:53:56.698553Z digest=sha256:3bea3e1165796d7e6245af473ec5a96a1f2d88cc34c85ef6b0b33b1a6f5f3a9d

Observation f9e8d2eb-29a3-4f2e-999d-3d81ce31b935 · outbound

This paper cites All models are pretrained on ImageNet1k only.

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models All models are pretrained on ImageNet1k only

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:53:56.851331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T23:53:56.706153Z digest=sha256:88509def6aae5513776ec92133fdcf35ead608c6e6339e1c6e4793daa342efa7

Observation 79c907e6-9d03-47b7-99f3-e4f451830732 · outbound

This paper cites Hence, we prefer human-annotated ImageNet bounding boxes.

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models Hence, we prefer human-annotated ImageNet bounding boxes

Reference 9

Resolution
malformed identifier
raw_fallback, observed 2026-08-15T23:53:56.825634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T23:53:56.713655Z digest=sha256:37741697c906ae98573ec478f0c2b571fbe2539e58635be70577c44bc4fae318

Observation 9c1f6bd5-c88d-4699-978b-4b242335042c · outbound

This paper cites 17 Published in Transactions on Machine Learning Research (08/2025) 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 mIOU 0 50 100 150 200Frequency A vg.

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models 17 Published in Transactions on Machine Learning Research (08/2025) 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 mIOU 0 50 100 150 200Frequency A vg

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:53:56.838021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T23:53:56.709803Z digest=sha256:d1c9434dbac10d718148112f2e62b418492c119855cd4850ad65cf63c4d5b573

Observation 78a5eedf-c076-4d85-a00a-690595a5cb02 · outbound

This paper cites For the JTT model, the images are applied with random resized cropping followed by horizontal flipping.

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models For the JTT model, the images are applied with random resized cropping followed by horizontal flipping

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:53:56.802774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T23:53:56.720892Z digest=sha256:f76e434c84ce16da69fc0ce7228b06a4eec38780fbd2d19def34e93460e84f2e

Observation b1449918-4a1a-49b0-b794-5009795625c6 · outbound

This paper cites (2023) for optimizing the last layer for ImageNet-9 dataset (Xiao et al., 2021).

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models (2023) for optimizing the last layer for ImageNet-9 dataset (Xiao et al., 2021)

Reference 100

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:53:56.814215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T23:53:56.717126Z digest=sha256:cfc0fc0d38c5529b246e305a32788281167cbaa6f7a4ecfca1cf758694fbc644

Observation 8be6c51b-7203-44b7-aa4d-9c6fc46dfbd4 · outbound

This paper cites an unresolved cited work.

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models Unresolved cited work

Reference 1991

Resolution
unresolved
no resolver link, observed 2026-08-15T23:53:56.694641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:53:56.694641Z digest=sha256:7eb7643395d948c57d525d7511a21ab238e19984faac447718122f9922eacfe5

Observation c664026d-3727-4552-bb5b-f98eee735a07 · outbound

This paper cites an unresolved cited work.

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models Unresolved cited work

Reference 2018

Resolution
malformed identifier
raw_fallback, observed 2026-08-15T23:53:56.864420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T23:53:56.702517Z digest=sha256:7a725954645f05b0ac547efc87c619d5c0ea027f22a192f78806bfaea53e494e

Observation f190b907-67cc-403a-8cce-8d04c2ecdde1 · outbound

This paper cites LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs.

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-15T23:53:56.682776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:53:56.682776Z digest=sha256:e63a6114aef096502c8f55fa7ee5b842fedb5bf3ea8a6b86a8e601bd053f1413

Observation 46d5964a-d4b7-4f9d-a310-de48d2fb9a31 · outbound

This paper cites Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases.

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases

Reference 2022

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:53:56.898115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T23:53:56.679118Z digest=sha256:90ba2b471c90a74d68d05775b5c926f9daca0b2307da300faa89d5fb16a5f56f

Observation 0afcc4b1-732c-4af1-8e95-e8b50f48e4f2 · outbound

This paper cites EVA-CLIP: Improved Training Techniques for CLIP at Scale.

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-15T23:53:56.686838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:53:56.686838Z digest=sha256:7e062d3437efe1253d4caab3445df74758c6f4b10161f51c37a14b12eea09cfd

Observation 44fc2322-91b9-48ab-af04-4e1290460886 · outbound

This paper cites AIM: Amending Inherent Interpretability via Self-Supervised Masking.

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models AIM: Amending Inherent Interpretability via Self-Supervised Masking

Reference 2025

Resolution
metadata mismatch
local_arxiv, observed 2026-08-15T23:53:56.790215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T23:53:56.674503Z digest=sha256:3f87b33d3dd783e5ec0802795934d95ed7ef1f5c6ddd136fa19f01338f3ee302

Pith citing papers

No inbound Pith citation observations are available.