Paper Citation Record · LEDGER

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking

As of 12 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2509.02182.

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

pith.paper-citation-record.v1
2509.02182 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:57:01.393367Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T05:25:15.311060Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T05:28:05.053060Z

Reference resolution

58 of 58 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 12f90e8d-566c-4112-a65f-d25c2b81b5e8 · outbound

This paper cites Combating adver- saries with anti-adversaries.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Combating adver- saries with anti-adversaries

Reference 1

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Observation a8539277-2c5c-4d95-b795-5cbd3fc1c754 · outbound

This paper cites Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

Reference 2

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Observation e0dcfd88-100c-4a11-9ca1-fdd0c3c3d2fc · outbound

This paper cites Parameter-free online test-time adaptation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Parameter-free online test-time adaptation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:02.081955Z

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Observation 5b9a429f-ec41-47dc-a49a-a6df306cb7c2 · outbound

This paper cites Parameter-free online test-time adaptation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Parameter-free online test-time adaptation

Reference 4

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Observation 76f50032-5b54-44b7-8dc3-5aca1a611d90 · outbound

This paper cites Online con- tinual learning with natural distribution shifts: An empiri- cal study with visual data.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Online con- tinual learning with natural distribution shifts: An empiri- cal study with visual data

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:02.062263Z

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Observation 656f937d-9f2f-46e1-9634-e9c40eb6080c · outbound

This paper cites Dataset shift in machine learning.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Dataset shift in machine learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:02.052804Z

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Observation 757c1039-ed4d-4c91-80e1-acfcb1aeb46a · outbound

This paper cites Contrastive test-time adaptation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Contrastive test-time adaptation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:02.043229Z

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Observation 7b77ec5d-9c0f-48b2-bbf5-38f597c92047 · outbound

This paper cites Evaluating the adversarial robustness of adaptive test-time defenses.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Evaluating the adversarial robustness of adaptive test-time defenses

Reference 8

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Observation 3b9ceafa-291e-4bfc-8cd9-4cb647d4f23d · outbound

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

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Imagenet: A large-scale hierarchical image database

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:02.022988Z

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Observation df60e415-6371-4b08-bd5a-8d21aca38bfa · outbound

This paper cites Back to the Source: Diffusion-Driven Test-Time Adaptation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Back to the Source: Diffusion-Driven Test-Time Adaptation

Reference 10

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Observation 4c2a3eb5-9041-4815-8366-99701d1cb21c · outbound

This paper cites Real-Time Evaluation in Online Continual Learning: A New Hope.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Real-Time Evaluation in Online Continual Learning: A New Hope

Reference 11

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Observation 1f72ac3f-7cea-4bdb-b49d-d7620fa9231f · outbound

This paper cites Unsupervised Representation Learning by Predicting Image Rotations.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Unsupervised Representation Learning by Predicting Image Rotations

Reference 12

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Observation 61167754-976f-4393-825e-beeaa17332d9 · outbound

This paper cites Note: Robust continual test- time adaptation against temporal correlation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Note: Robust continual test- time adaptation against temporal correlation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:02.012624Z

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Observation e6468c23-22b8-42a6-b55c-93a0cbebea40 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Explaining and Harnessing Adversarial Examples

Reference 14

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Observation e772c443-517b-4602-897a-8b7218f41c45 · outbound

This paper cites Deep residual learning for image recognition.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Deep residual learning for image recognition

Reference 15

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Observation dd6f7f6b-3464-4e9e-b35a-4fba4d8371f8 · outbound

This paper cites Benchmarking neu- ral network robustness to common corruptions and perturba- tions.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Benchmarking neu- ral network robustness to common corruptions and perturba- tions

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.992430Z

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Observation 0bc986d3-d211-4258-a936-22cbfcccdb3b · outbound

This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.981401Z

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Observation 2098c192-d527-47fa-ad69-20fa519d5183 · outbound

This paper cites Denoising diffu- sion probabilistic models.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Denoising diffu- sion probabilistic models

Reference 18

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Observation 33fcfcd6-4066-41d6-a994-e8a4c1b6fd2f · outbound

This paper cites Test-time classifier adjustment module for model-agnostic domain generaliza- tion.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Test-time classifier adjustment module for model-agnostic domain generaliza- tion

Reference 19

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Observation db03627c-b636-4e2d-93b8-61e089986ffe · outbound

This paper cites Test-time classifier adjustment module for model-agnostic domain generaliza- tion.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Test-time classifier adjustment module for model-agnostic domain generaliza- tion

Reference 20

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Observation 006657b2-5a75-435c-a009-f4c81f6dbbe6 · outbound

This paper cites 3d common corruptions and data augmentation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking 3d common corruptions and data augmentation

Reference 21

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Observation 02dc2591-c4e9-447a-a10d-64a076079f08 · outbound

This paper cites Overcoming catastrophic forgetting in neu- ral networks.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Overcoming catastrophic forgetting in neu- ral networks

Reference 22

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Observation 5874ae33-7b1e-4858-9304-0e234f74f0e4 · outbound

This paper cites Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment

Reference 23

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Observation b9970589-23ab-4156-b9c8-02a503477838 · outbound

This paper cites Learning multiple layers of features from tiny images.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Learning multiple layers of features from tiny images

Reference 24

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Observation 0e581e9f-f40b-4f69-948e-3553fcade5f1 · outbound

This paper cites Pseudo-label: The simple and effi- cient semi-supervised learning method for deep neural net- works.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Pseudo-label: The simple and effi- cient semi-supervised learning method for deep neural net- works

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.912645Z

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Observation 1617faeb-c0ca-4f50-bfad-112ef26403c7 · outbound

This paper cites Revisiting Batch Normalization For Practical Domain Adaptation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Revisiting Batch Normalization For Practical Domain Adaptation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T11:57:01.279189Z

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Observation ba0295eb-24e3-4220-92d6-ab7d6fc75a71 · outbound

This paper cites Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation

Reference 27

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Observation 7ba12899-e8ea-4760-8126-ca67899a01ea · outbound

This paper cites Do we really need to access the source data? source hypothesis transfer for un- supervised domain adaptation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Do we really need to access the source data? source hypothesis transfer for un- supervised domain adaptation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.902795Z

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Observation ea035b4d-9767-4926-b68f-be7687f38255 · outbound

This paper cites A comprehensive survey on test-time adaptation under distribution shifts, 2023.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking A comprehensive survey on test-time adaptation under distribution shifts, 2023

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.893108Z

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Observation 087917d1-c272-47e0-830b-6bccb758ae22 · outbound

This paper cites Ttt++: When does self-supervised test-time training fail or thrive? Advances in Neural Information Processing Systems , 34: 21808–21820, 2021.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Ttt++: When does self-supervised test-time training fail or thrive? Advances in Neural Information Processing Systems , 34: 21808–21820, 2021

Reference 30

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Observation b2055a52-e1a1-455c-893d-09dbea493c96 · outbound

This paper cites Kitting in the wild through online domain adaptation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Kitting in the wild through online domain adaptation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.873607Z

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Observation 5599bdee-f8c4-4b1f-ac84-ac3d607c7e58 · outbound

This paper cites The norm must go on: dynamic unsuper- vised domain adaptation by normalization.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking The norm must go on: dynamic unsuper- vised domain adaptation by normalization

Reference 32

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Observation a17d8ce7-ed7a-4881-8e16-7f359a315bf8 · outbound

This paper cites Act- mad: Activation matching to align distributions for test-time- training, 2022.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Act- mad: Activation matching to align distributions for test-time- training, 2022

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.759033Z

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Observation f4b8f942-b2a4-47df-b45d-e5e15dbaae2e · outbound

This paper cites Trackingnet: A large-scale dataset and benchmark for object tracking in the wild.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Trackingnet: A large-scale dataset and benchmark for object tracking in the wild

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.748942Z

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Observation 72809687-e345-4abb-b705-bdd432f39671 · outbound

This paper cites Efficient test-time model adaptation without forgetting.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Efficient test-time model adaptation without forgetting

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.738696Z

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Observation f9d4055a-0a87-41fc-b134-105c65c9600c · outbound

This paper cites To- wards stable test-time adaptation in dynamic wild world.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking To- wards stable test-time adaptation in dynamic wild world

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.728174Z

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Observation 468667da-cf8f-473c-8ed1-523d4a71de7e · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Pytorch: An imperative style, high-performance deep learning library

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.718320Z

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Observation f6098243-ea56-4970-bca5-28d9eae841af · outbound

This paper cites Enhancing adversarial robustness via test-time transforma- tion ensembling.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Enhancing adversarial robustness via test-time transforma- tion ensembling

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.707953Z

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Observation de18d3e7-908a-4c61-86bc-cb3890a34f24 · outbound

This paper cites Rdumb: A simple approach that questions our progress in continual test-time adaptation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Rdumb: A simple approach that questions our progress in continual test-time adaptation

Reference 39

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Observation acc6c47e-b1f7-4b62-b823-2a9b0bd6f2a3 · outbound

This paper cites YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video

Reference 40

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This paper cites Adapting visual category models to new domains.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Adapting visual category models to new domains

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.687275Z

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Observation a279c538-f04e-4d43-a45a-8da13284af54 · outbound

This paper cites Acdc: The adverse conditions dataset with correspondences for se- mantic driving scene understanding.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Acdc: The adverse conditions dataset with correspondences for se- mantic driving scene understanding

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.677129Z

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Observation 9ce2c862-b4cf-4916-8954-0df45717f0e6 · outbound

This paper cites Improving robustness against common corruptions by covariate shift adaptation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Improving robustness against common corruptions by covariate shift adaptation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.666927Z

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Observation 0830964b-69ea-4d96-8723-391c88b56f99 · outbound

This paper cites Online learning and online convex optimization.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Online learning and online convex optimization

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.656960Z

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Observation 715275f9-33e3-45b2-90f2-86ab2fe03744 · outbound

This paper cites Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-05T11:57:01.447205Z

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Observation 51b14700-42ab-4390-b358-766a9ff0988a · outbound

This paper cites Test-time training with self- supervision for generalization under distribution shifts.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Test-time training with self- supervision for generalization under distribution shifts

Reference 47

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Source-reported events for the cited work

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Observation d2b35dd2-8456-4254-bc62-e922430bd574 · outbound

This paper cites Unbiased look at dataset bias.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Unbiased look at dataset bias

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.640941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ec72aef7-98c8-4c9d-8231-7400d574c463 · outbound

This paper cites Adversarial discriminative domain adaptation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Adversarial discriminative domain adaptation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.630905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3371ae06-a9b9-4074-9038-34de27f589d9 · outbound

This paper cites Tent: Fully Test-time Adaptation by Entropy Minimization.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T11:57:01.362729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 312ad108-991b-4a90-a5b0-770d70a6e936 · outbound

This paper cites Continual test-time domain adaptation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Continual test-time domain adaptation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.620025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 581483fa-26dc-4653-b274-9d0e6d704603 · outbound

This paper cites Robust test- time adaptation in dynamic scenarios.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Robust test- time adaptation in dynamic scenarios

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.610110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 15b853c9-e60b-466b-922b-f46c4f5f74f4 · outbound

This paper cites MEMO: Test Time Robustness via Adaptation and Augmentation.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking MEMO: Test Time Robustness via Adaptation and Augmentation

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T11:57:01.372899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7780b794-10cc-41cd-b87c-967550ca7078 · outbound

This paper cites an unresolved cited work.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:57:01.599824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ba39155c-410d-44df-a001-23d9f357f0f3 · outbound

This paper cites This dynamic approach enables us to precisely control the severity level of each corruption, closely mimicking real-world scenarios.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking This dynamic approach enables us to precisely control the severity level of each corruption, closely mimicking real-world scenarios

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.589128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b5df348c-a53c-453e-924e-ed9151365fba · outbound

This paper cites ViT outperforms ResNet-18, even at lower batch sizes, due to its reduced sensitivity to batch size [36].

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking ViT outperforms ResNet-18, even at lower batch sizes, due to its reduced sensitivity to batch size [36]

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.579197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e1454f71-90cd-4bde-b511-50950826bfed · outbound

This paper cites an unresolved cited work.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:57:01.568448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7da679d6-b631-4564-840d-5a39a120bf1c · outbound

This paper cites These examples are generated during the memory bank initialization process.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking These examples are generated during the memory bank initialization process

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.558758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 902921e6-b215-4037-9e0c-70400e90734d · outbound

This paper cites These tables contain additional data and de- tailed results.

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking These tables contain additional data and de- tailed results

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:57:01.548665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation 4e06a09d-3dbe-44e8-914a-5518d225a7d6 · inbound

GoTTA be Diverse: Rethinking Memory Policies for Test-Time Adaptation cites this paper.

GoTTA be Diverse: Rethinking Memory Policies for Test-Time Adaptation ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:28:05.056282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.