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Paper Citation Record · LEDGER

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition

As of 8 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2505.23313.

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

pith.paper-citation-record.v1
2505.23313 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:55:12.834660Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

61 of 61 outbound references displayed

  • verified exact3
  • verified fuzzy34
  • unresolved23
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d2d49b07-43d4-4324-95fd-d4a8e33bd478 · outbound

This paper cites Pedestrian attribute recognition: A survey,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Pedestrian attribute recognition: A survey,

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-07T12:55:20.366543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation f09ac288-d69d-40da-b97f-53650d994c6f · outbound

This paper cites Improving person re-identification by attribute and identity learning,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Improving person re-identification by attribute and identity learning,

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-07T12:55:20.178658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:07.891959Z digest=sha256:76663f05f0454e350fa202fff1908d4c26ef6bf14efa0a46b6a4da65e5e39a57

Observation 53a77a22-94bc-45f1-af5d-c44187f8e553 · outbound

This paper cites Attmot: improving multiple-object tracking by introducing auxiliary pedestrian attributes,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Attmot: improving multiple-object tracking by introducing auxiliary pedestrian attributes,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:19.995912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation a0254c75-025b-4fe9-b663-0dbc4d75d537 · outbound

This paper cites Attribute- guided pedestrian retrieval: Bridging person re-id with internal attribute variability,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Attribute- guided pedestrian retrieval: Bridging person re-id with internal attribute variability,

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-07T12:55:19.780063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:08.167927Z digest=sha256:4f496cb7cbc2fd7c06e742246d4ce5e3c317d4a14166288f18ce51c7d2ac1b32

Observation 5098b420-ad0b-45c9-867f-39949d53c0df · outbound

This paper cites Attribute recognition by joint recurrent learning of context and correlation,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Attribute recognition by joint recurrent learning of context and correlation,

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-07T12:55:19.390486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:08.299983Z digest=sha256:173fef5221db15f10baa793a85919e4ddde4569deced51dad1198fa29b93f36d

Observation 07fa31e3-e5fc-4d12-9d78-697a7d9c35a9 · outbound

This paper cites A simple visual-textual baseline for pedestrian attribute recognition,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition A simple visual-textual baseline for pedestrian attribute recognition,

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-07T12:55:19.172573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:08.389045Z digest=sha256:fddf5b5fd0d87c609f852d9ceb6bd448b88425ed83723993eb918d3e5d873ca0

Observation 546f1743-07af-41e8-beb7-efaa0dface66 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Learning transferable visual models from natural language supervision,

Reference 8

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raw_fallback, observed 2026-08-07T12:55:18.990323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:08.475774Z digest=sha256:244192a8cc0ec265231b8159a54926d1c2231c49a01c1d0ee0a1311dbc5d7905

Observation 9913ca98-f05e-489b-ae87-544ae76c770b · outbound

This paper cites GPT-4 Technical Report.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition GPT-4 Technical Report

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:08.561745Z digest=sha256:a5a54086c004ead701ba190e46e8decefe84d0d74999f72b35206b82794eadae

Observation f9438499-5ce0-435d-8452-40cdd7760309 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:08.636731Z digest=sha256:6f3203db016d2268b97754c139e5e74b066ea278996719aea501854cd868ae4b

Observation e3356fb2-3f32-47d9-8342-7b504cf91b5e · outbound

This paper cites Qwen Technical Report.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Qwen Technical Report

Reference 11

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Observation 0b3a6d3c-44f2-4e9c-8bb2-077086ea8194 · outbound

This paper cites Pedestrian attribute recognition via clip based prompt vision-language fusion,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Pedestrian attribute recognition via clip based prompt vision-language fusion,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:18.769264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:08.766370Z digest=sha256:791240718eb5f3fa9a7893f17f77e06e5a36e61096b424b63f3da7afe3df621d

Observation 4aaeb43d-2707-4c5a-b8be-af4b8fb247bd · outbound

This paper cites Pedestrian attribute recognition: A new benchmark dataset and a large language model augmented framework,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Pedestrian attribute recognition: A new benchmark dataset and a large language model augmented framework,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:18.621107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:08.831564Z digest=sha256:ad1d177bc84258e238d7392b2bf702a4fdbcbee929a6971281bbfac9551e0148

Observation b6409c07-a1bf-4751-9f4c-51ac73d81914 · outbound

This paper cites Physical adversarial attack meets computer vision: A decade survey,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Physical adversarial attack meets computer vision: A decade survey,

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-07T12:55:18.442000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:08.961165Z digest=sha256:7205650b4128b99c48976a1ca30a291330043b8667b12b53425e0b30d396899b

Observation 21308daa-958a-44d1-bac8-e0cc75605786 · outbound

This paper cites Mutual-modality adversarial attack with semantic perturbation,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Mutual-modality adversarial attack with semantic perturbation,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:18.239586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:09.053116Z digest=sha256:6bd950f49ebd4eb1257835b09f9646785ae9b2dc3315b7612168808743111fcb

Observation 53af098e-945c-42cb-8d38-bae1e3a7a09a · outbound

This paper cites Diffattack: Evasion attacks against diffusion-based adversarial purification,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Diffattack: Evasion attacks against diffusion-based adversarial purification,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:18.008202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:09.119928Z digest=sha256:543144601ca5c9816e4ce73816d5fdfeed2474995519be6d4b6e0d15251bb3e9

Observation bdd6f855-44ea-4644-ae4f-316e52394b21 · outbound

This paper cites Strong transferable adversar- ial attacks via ensembled asymptotically normal distribution learning,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Strong transferable adversar- ial attacks via ensembled asymptotically normal distribution learning,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:17.785597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:09.221075Z digest=sha256:9214bc20511d3b21ffa92e6858445cd576f991df8b8e9160fd03e6df7f5ea9fd

Observation 57d22da9-4755-4d9b-9a16-eb0c155e07f6 · outbound

This paper cites Stealthiness assessment of adversarial perturbation: From a visual perspective,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Stealthiness assessment of adversarial perturbation: From a visual perspective,

Reference 18

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raw_fallback, observed 2026-08-07T12:55:17.567589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:09.286661Z digest=sha256:439f8a44921cb24414b1108e83c4bfe85b8e0243b8f654d4e61fa66e81c622c8

Observation 0b32dded-4937-40be-890b-323ab10272f8 · outbound

This paper cites Pedestrian attribute recog- nition at far distance,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Pedestrian attribute recog- nition at far distance,

Reference 19

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raw_fallback, observed 2026-08-07T12:55:17.360976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:09.362625Z digest=sha256:cafb1f52014b868678d4408c2e55b127c438498c1f7492ec290a008173017330

Observation b727f4b4-2970-406d-b8a4-11a348a95ccd · outbound

This paper cites Hydraplus-net: Attentive deep features for pedestrian analysis,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Hydraplus-net: Attentive deep features for pedestrian analysis,

Reference 20

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raw_fallback, observed 2026-08-07T12:55:17.120800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:09.463690Z digest=sha256:697f4e6b21f9ac10054f3144252e38bd084c1d4ba4679c820d831f45ded815a1

Observation aa8d0c78-e2d1-4233-ab3f-2bfa027796f0 · outbound

This paper cites A richly annotated pedes- trian dataset for person retrieval in real surveillance scenarios,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition A richly annotated pedes- trian dataset for person retrieval in real surveillance scenarios,

Reference 21

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raw_fallback, observed 2026-08-07T12:55:16.916561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:09.538834Z digest=sha256:76f07bdb7a27879979e98c8829762a484d6e0ed81d0c0ea39a7cc2380aae66c9

Observation d798fe70-5b33-42d2-b10c-a196fbd67019 · outbound

This paper cites Intriguing properties of neural networks.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Intriguing properties of neural networks

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:09.630931Z digest=sha256:40eb42bd9ced8d31bcd7996d3f4d9425342660f9689439c4796ae8dcc2ab6dff

Observation c226c5cc-37fb-4fc8-b132-817e723d295a · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Explaining and Harnessing Adversarial Examples

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:09.712866Z digest=sha256:446e43472a4c9d0d69d01a4a440f9e93af62bd36433f4279534bb02d5ae13f49

Observation 32612ef9-278c-4b4d-b29c-e9da96496992 · outbound

This paper cites Adversarial examples in the physical world,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Adversarial examples in the physical world,

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:09.805219Z digest=sha256:ff674303aecfe98f780320ac0540b10277b211b5b66ec6f6011399cc288abdb5

Observation 119d7d96-6a6b-42cf-8de1-8805451d94eb · outbound

This paper cites Boosting adversarial attacks with momentum,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Boosting adversarial attacks with momentum,

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:09.859867Z digest=sha256:fd2744662d4c795f081bad31b8986339d255cc7b6123463a5f2bde6fb534fff5

Observation 0f2d89f8-6451-4879-8351-9919f52a2aea · outbound

This paper cites Improving transferability of adversarial examples with input diversity,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Improving transferability of adversarial examples with input diversity,

Reference 26

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no resolver link, observed 2026-08-07T12:55:09.917413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:09.917413Z digest=sha256:a5e645711923af64cf5b3a96e9fd5af2e4eed186a6554c7688ee9f1b11d46a3d

Observation 41636ec8-9540-40d5-bcce-99cb07c75266 · outbound

This paper cites Evading defenses to transferable adversarial examples by translation-invariant attacks,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Evading defenses to transferable adversarial examples by translation-invariant attacks,

Reference 27

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Observation 95fc24e1-a7d1-4892-9845-ecb17882b13c · outbound

This paper cites Advdrop: Adversarial attack to dnns by dropping information,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Advdrop: Adversarial attack to dnns by dropping information,

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-07T12:55:16.730328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation b34b6c1f-f0fc-4ea5-84b1-40b8939da234 · outbound

This paper cites The best protection is attack: Fooling scene text recognition with minimal pixels,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition The best protection is attack: Fooling scene text recognition with minimal pixels,

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-07T12:55:16.546342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:10.132479Z digest=sha256:5faaed3dc61fb18e4d31d109058020d0cfcff92c5786a0eb1000dfa6926c96b2

Observation 59faab4e-f9db-4771-8347-94733c790ad2 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:10.204398Z digest=sha256:a0437d8dcb973452ae8ea013fc3edc5e59de2412df238c77ff7bab0c63b7ed3c

Observation a5f60b0f-6f56-4bdc-982a-b85ac909052c · outbound

This paper cites Ensemble Adversarial Training: Attacks and Defenses.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Ensemble Adversarial Training: Attacks and Defenses

Reference 31

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T12:55:10.270586Z digest=sha256:d57aa2949a7821776d3a01ce0520d85bd6b3acaecd864e1e0629ecfee9e3108b

Observation 810ed97e-0ff1-4639-a9dc-d9002070e885 · outbound

This paper cites Countering Adversarial Images using Input Transformations.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Countering Adversarial Images using Input Transformations

Reference 32

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no resolver link, observed 2026-08-07T12:55:10.335984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:10.335984Z digest=sha256:f2954671b118c7e85c9377816eefa2c9e6f6a263722edbc810ac74369e412c7d

Observation fbcac3b2-2a76-475f-91c1-5fc107e47f68 · outbound

This paper cites Mitigating Adversarial Effects Through Randomization.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Mitigating Adversarial Effects Through Randomization

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:10.431484Z digest=sha256:97c18fc0157c7e0032c36578a7b8378c00a75c994489bb6f8cc8a90c46d21b6e

Observation fcdb0f9b-d9e6-4707-b439-4dff727c29e2 · outbound

This paper cites Defense against adversarial attacks using high-level representation guided denoiser,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Defense against adversarial attacks using high-level representation guided denoiser,

Reference 34

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no resolver link, observed 2026-08-07T12:55:10.500260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:10.500260Z digest=sha256:d1d1ae077582e6c6f8387f148da96958eb732a650fc64ffd3e328f3515b11dfd

Observation 32ea8f98-4d7e-4a35-a57c-d93206eb77ca · outbound

This paper cites Enhancing the transferability of adversarial attacks through variance tuning,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Enhancing the transferability of adversarial attacks through variance tuning,

Reference 35

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no resolver link, observed 2026-08-07T12:55:10.565856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:10.565856Z digest=sha256:cc834cf50772cb7612733ff3ce7a17cd9c195a6e470768623e8b46fa7414eb44

Observation b6c0e771-5534-4b64-ac33-1710277972ac · outbound

This paper cites Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks

Reference 36

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no resolver link, observed 2026-08-07T12:55:10.617905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:10.617905Z digest=sha256:6be0f9665152c862128ba9bbca3538660e231d066ee9299dcc8eeac59b07302a

Observation 54d9c871-9084-4c1e-b130-11fc3e153998 · outbound

This paper cites Certified adversarial robustness via randomized smoothing,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Certified adversarial robustness via randomized smoothing,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:16.384539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:10.673746Z digest=sha256:27fa7bf5ac8de40248b1222ac64d03768d5b2303d3c76aed456d02aacefa4de6

Observation 41893b0e-d7c9-437b-aab5-1ebee2571871 · outbound

This paper cites A robust open-set multi- instance learning for defending adversarial attacks in digital image,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition A robust open-set multi- instance learning for defending adversarial attacks in digital image,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:16.171370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:10.733600Z digest=sha256:f4b71779be80b019ecf207f9f399888bc2d201923e30141887e6f6281dfc908e

Observation f0b61a8b-942d-441d-8b96-e1166e7b4952 · outbound

This paper cites Generative adversarial nets,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Generative adversarial nets,

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T12:55:10.786337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:10.786337Z digest=sha256:b8a0549d154e9db57290801a2018871e55f873657cad8fb2b867e1ea328f0661

Observation 031ad194-f71c-43c8-8b80-a96ca3aa4ea8 · outbound

This paper cites Generating Adversarial Examples with Adversarial Networks.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Generating Adversarial Examples with Adversarial Networks

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T12:55:10.881029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:10.881029Z digest=sha256:eb7a04dc2e2c69180818cc7f5bb3b7a1eb19051d59ddaaaef8dc400a11e2270d

Observation a6ba3ab9-aa0e-4c9d-9373-801e1d53d23a · outbound

This paper cites AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T12:55:10.951615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:10.951615Z digest=sha256:226d1d98955f74d31c269788f644ed672ca756b374d34c5cd82ba3a067cede83

Observation 45993bc7-8ef5-4f53-bb5b-1c8682b67a4e · outbound

This paper cites Label-only model inversion attacks: Attack with the least information,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Label-only model inversion attacks: Attack with the least information,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:16.011340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:11.026806Z digest=sha256:d6a7f5b68aad7f48a1fed56f3f714ecb919dfc42619ff35a53589f3320f44e5b

Observation 5b4895db-fd06-4019-97c7-d3f49e021f3c · outbound

This paper cites Robust and general- ized physical adversarial attacks via meta-gan,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Robust and general- ized physical adversarial attacks via meta-gan,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:15.830505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:11.105486Z digest=sha256:e4dcd0b04f3efcc56424b0f545f195586c50c70f1f9e942edd5f77fb5500e6bd

Observation 59f622eb-4e1c-42a9-a95c-9a95528ee09e · outbound

This paper cites Hierarchical reasoning network for pedestrian attribute recognition,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Hierarchical reasoning network for pedestrian attribute recognition,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:15.670005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:11.172179Z digest=sha256:f68b3c04aebcd7c566994f0fc472820e9ad3a4fb430b799d2b7072755f03e5c5

Observation 63b0ac36-e8dc-41f0-a9a7-6cb289a65804 · outbound

This paper cites Multi-task cnn model for attribute prediction,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Multi-task cnn model for attribute prediction,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:19.566482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:11.239680Z digest=sha256:370ed400ccafa854e832322db15729b839d53fc9d61b55249d1c5cf618c1698e

Observation 257c68fc-08ef-40e9-9fff-f9ba833190e8 · outbound

This paper cites Recurrent attention model for pedestrian attribute recognition,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Recurrent attention model for pedestrian attribute recognition,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:15.516778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:11.331606Z digest=sha256:e0e3f4467554e85f49791b59d3910986bba469d254cbe7730bd0821aef55b8c1

Observation 2212bc88-3b32-4615-9713-8b646a07e70e · outbound

This paper cites Correlation graph convolu- tional network for pedestrian attribute recognition,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Correlation graph convolu- tional network for pedestrian attribute recognition,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:15.409412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:11.391103Z digest=sha256:a43f93a461db2390b77aee8be1b6e1a9533a8f231d3b8adbcc889378fbd10f16

Observation 303e3bb3-8537-4c4c-a4d0-288816fec0f9 · outbound

This paper cites Relation-aware pedestrian attribute recognition with graph convolutional networks,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Relation-aware pedestrian attribute recognition with graph convolutional networks,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:15.201081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:11.458119Z digest=sha256:d5e22e4807c3a066a3f2b9865107fe328c35708d08ef5816eea858cdac98ba04

Observation cba168e1-1479-43b6-a408-a195e1114945 · outbound

This paper cites Visual attention consistency for human attribute recognition,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Visual attention consistency for human attribute recognition,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:15.031673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:11.572076Z digest=sha256:1caec01d9db5371a33f25a09a695a5453ca34c1cc99b5ba97f8aea766cb77018

Observation b9753cdc-2d00-4ffd-8738-e804e474e763 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-07T12:55:11.719523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:11.719523Z digest=sha256:619abf3a981c4c843be91472223cc45cd78f37ae91ac7bc074399d5ad71fc3cb

Observation 9a7c89be-1730-4fd0-8878-21ccb9baeffb · outbound

This paper cites Learning clip guided visual-text fusion transformer for video-based pedestrian attribute recog- nition,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Learning clip guided visual-text fusion transformer for video-based pedestrian attribute recog- nition,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:14.873878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:11.869936Z digest=sha256:537a50a8b4ad9f23bd8ee1ed55be878dd938bcda4ed30bc7f6319e90f12deb46

Observation 9b16ba13-267c-4424-be36-7dc7278505f3 · outbound

This paper cites Spatio-Temporal Side Tuning Pre-trained Foundation Models for Video-based Pedestrian Attribute Recognition.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Spatio-Temporal Side Tuning Pre-trained Foundation Models for Video-based Pedestrian Attribute Recognition

Reference 52

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:55:13.693731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:11.984212Z digest=sha256:1b3c2ff226ae648b2bc3c8b3bbac6f1d220302d130da313a8b9f49ba33e5f478

Observation a0c4daa8-72b6-4727-b5a0-841c36b52a23 · outbound

This paper cites Drformer: Learning dual relations using trans- former for pedestrian attribute recognition,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Drformer: Learning dual relations using trans- former for pedestrian attribute recognition,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:14.720544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:12.076772Z digest=sha256:d93bb38812695aec72687f43217ff769b2fa76153fa2aa5aa8664251584af699

Observation e5e4e3f9-ad90-4098-b8c9-995953e34216 · outbound

This paper cites RGB-Event based Pedestrian Attribute Recognition: A Benchmark Dataset and An Asymmetric RWKV Fusion Framework.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition RGB-Event based Pedestrian Attribute Recognition: A Benchmark Dataset and An Asymmetric RWKV Fusion Framework

Reference 54

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:55:13.505778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:12.130086Z digest=sha256:e6a62ca561e1f6a471c50260f0bbfaee8124a61dec8b16da5034b7a243ce6bab

Observation e2aa4333-6ade-4e1e-918d-5c833d29c217 · outbound

This paper cites SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigm.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigm

Reference 55

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:55:13.329802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:12.211952Z digest=sha256:fd5a2c5e123feec25cf52e13136b3de79016c74e3fcfdbbafe70d865831e60dc

Observation e779b876-01e8-496f-b1cd-4e2f85717534 · outbound

This paper cites Cdul: Clip- driven unsupervised learning for multi-label image classification,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Cdul: Clip- driven unsupervised learning for multi-label image classification,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:14.536559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:12.280264Z digest=sha256:e504e52b0281505ac4f8eafa1794442bf8a4e7effafaf447da38327b1c842247

Observation 967f5bfd-1364-415e-ab92-8163487bd8c0 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Towards deep learning models resistant to adversarial attacks,

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-07T12:55:12.373287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:12.373287Z digest=sha256:94983535d5d7bdd4af563bfe30065f7c17f25a1ae19183db22282f2ef856ccc7

Observation 0ad43ab4-627c-4268-8c44-bd759702d692 · outbound

This paper cites Boosting adversarial transferability via gradient relevance attack,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Boosting adversarial transferability via gradient relevance attack,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:14.352062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:12.461377Z digest=sha256:079d068fb76292a8df24257fb6c152531bb379fd7b957c890d9f1e18a04e9f4b

Observation dd943d0b-3b9a-4fe6-aee5-0729fb666ced · outbound

This paper cites Enhancing adversarial trans- ferability through neighborhood conditional sampling,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Enhancing adversarial trans- ferability through neighborhood conditional sampling,

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-07T12:55:12.565811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:12.565811Z digest=sha256:37372a3a2cb0692115dae5ad5d0104fddabd42fed51d28ea05998895e60b37a9

Observation 4b72d36b-4fbe-4ff8-8d8e-0624b0c903e0 · outbound

This paper cites On the importance of initialization and momentum in deep learning,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition On the importance of initialization and momentum in deep learning,

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T12:55:12.664773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:55:12.664773Z digest=sha256:bcab62823ea5bfcf28483f629abc5c42e51888bce424c29145a417d99da2fbb9

Observation c9152e08-d81a-4655-b76b-17dc42f00fc3 · outbound

This paper cites An empirical study of mamba-based pedestrian attribute recognition,.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition An empirical study of mamba-based pedestrian attribute recognition,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:55:14.048162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:12.787579Z digest=sha256:37d6d2606f53dea9fc433b06a449b67d6f75726ebba7d4ef364cafdaa383591b

Observation df68cdf9-e855-4d80-8b57-9346c2613b2d · outbound

This paper cites An Empirical Study of Mamba-based Pedestrian Attribute Recognition.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition An Empirical Study of Mamba-based Pedestrian Attribute Recognition

Reference 2024

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T12:55:13.035170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:55:12.834660Z digest=sha256:c79573239055cc4aa0449624c3c044f1820d2a20943de60687940b90be0dbeb7

Pith citing papers

No inbound Pith citation observations are available.