Paper Citation Record · LEDGER

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles

As of 15 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2608.08815.

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

pith.paper-citation-record.v1
2608.08815 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:28:26.745961Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

31 of 31 outbound references displayed

verified exact1
verified fuzzy15
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation da165618-e51b-4716-af4e-6a69753d452e · outbound

This paper cites The road ahead: A comprehensive review of recent advances in traffic sign and lane line recognition for autonomous systems,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles The road ahead: A comprehensive review of recent advances in traffic sign and lane line recognition for autonomous systems,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:28.108202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e735f03d-59ab-477d-862c-85408ea6da83 · outbound

This paper cites Deep residual learning for image recognition,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Deep residual learning for image recognition,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.452515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9cc817a-1b42-4a02-b3b7-f3cdd9a9cc65 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.459505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1bab795-056f-4208-b818-ed02b8519a5d · outbound

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

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.476358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 825852ba-337d-46c1-88a3-2f4bc5e9b1ca · outbound

This paper cites Intriguing properties of neural networks.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Intriguing properties of neural networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.484274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a503b76b-338a-4c71-8e17-73fd7c01c667 · outbound

This paper cites Shadows can be dangerous: Stealthy and effective physical-world adversarial attack by natural phenomenon,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Shadows can be dangerous: Stealthy and effective physical-world adversarial attack by natural phenomenon,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:28.037560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6fba2089-dbb6-4e5f-b7b2-503604aa6718 · outbound

This paper cites Natural light can also be dangerous: Traffic sign misinterpretation under adversarial natural light attacks,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Natural light can also be dangerous: Traffic sign misinterpretation under adversarial natural light attacks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:27.985563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4da4ffca-ea56-4cd5-8498-4dc437b28aff · outbound

This paper cites Robust physical-world attacks on deep learning visual classification,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Robust physical-world attacks on deep learning visual classification,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:27.953904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fad9a142-3807-4154-be67-7286253b6ab9 · outbound

This paper cites On the natural ro- bustness of vision-language models against visual perception attacks in autonomous driving,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles On the natural ro- bustness of vision-language models against visual perception attacks in autonomous driving,

Reference 9

Resolution: verified exact; raw_fallback, observed 2026-08-14T04:28:27.159244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 606c6fda-7a9f-43bb-9ff4-0f2a386e49ae · outbound

This paper cites Distillation as a defense to adversarial perturbations against deep neural networks,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Distillation as a defense to adversarial perturbations against deep neural networks,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.537234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a87c65ae-fb9f-4bdb-896a-564c447c28cc · outbound

This paper cites Countering Adversarial Images using Input Transformations.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Countering Adversarial Images using Input Transformations

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.544924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84d224c9-5157-44bb-b530-81282b7558bb · outbound

This paper cites Provable defenses against adversarial ex- amples via the convex outer adversarial polytope,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Provable defenses against adversarial ex- amples via the convex outer adversarial polytope,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:27.893692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1490b047-0f87-4679-bd6c-320e62c0fd81 · outbound

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

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.572973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0344fca9-5ead-47ad-935f-8a0c83608152 · outbound

This paper cites Robustness May Be at Odds with Accuracy.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Robustness May Be at Odds with Accuracy

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.588782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db80bc62-7e7f-4b6a-b90a-06ce968b1f47 · outbound

This paper cites NVILA: Efficient Frontier Visual Language Models.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles NVILA: Efficient Frontier Visual Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.603246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd1d4ac3-1109-43c7-9179-d14f3c5f033b · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Reproducible scaling laws for contrastive language-image learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:27.747693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 07065633-d39c-478d-a33f-23578c77f2e4 · outbound

This paper cites The German Traffic Sign Recognition Benchmark: A multi-class classification com- petition,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles The German Traffic Sign Recognition Benchmark: A multi-class classification com- petition,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:27.652630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a829d0c9-61a4-48e2-8d25-19164d16b95c · outbound

This paper cites (2025) LISA traffic sign dataset.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles (2025) LISA traffic sign dataset

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:27.542219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d84fd718-3aff-44ff-9459-8f1aab23113a · outbound

This paper cites Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.657390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1473f8f-7e5e-4b6b-80ff-c9d9d9b577f6 · outbound

This paper cites Promp- tkd: Unsupervised prompt distillation for vision-language models,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Promp- tkd: Unsupervised prompt distillation for vision-language models,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.666212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed213b15-a48a-4d8b-9067-91bc3f099899 · outbound

This paper cites Tinyclip: Clip distillation via affinity mimicking and weight inheritance,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Tinyclip: Clip distillation via affinity mimicking and weight inheritance,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:27.389045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 771f606b-8945-4c1a-943c-16cf8ec2292f · outbound

This paper cites Clip-kd: An empirical study of clip model distillation,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Clip-kd: An empirical study of clip model distillation,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.679022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d31aebe1-fc7e-44b7-8fd7-51f00c169ece · outbound

This paper cites Rethink- ing the inception architecture for computer vision,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Rethink- ing the inception architecture for computer vision,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:27.351713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e072175f-5d4e-4b31-838e-d371dbbf19c2 · outbound

This paper cites Available: https://git-disl.github.io/GTDLBench/datasets/ lisa traffic sign dataset/.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Available: https://git-disl.github.io/GTDLBench/datasets/ lisa traffic sign dataset/

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:27.469186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3d33c040-51c0-48b9-ae18-e7e5a63f72fe · outbound

This paper cites Training with noise is equivalent to tikhonov regular- ization,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Training with noise is equivalent to tikhonov regular- ization,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:27.326974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 97871c39-c498-4f48-9303-0110606dc4f6 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Dropout: a simple way to prevent neural networks from overfitting,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:27.300922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 895a760d-df9e-4278-be7c-979f7255847b · outbound

This paper cites Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.704738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3137575-9857-4368-993f-c78331e62968 · outbound

This paper cites Contrast limited adaptive histogram equalization,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Contrast limited adaptive histogram equalization,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:27.269815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e0a91d2f-3de2-4408-b0cd-40d73fc6848f · outbound

This paper cites Mitigating Adversarial Effects Through Randomization.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Mitigating Adversarial Effects Through Randomization

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.717387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f77c40bc-53e3-4547-ab8c-e6640ee043c0 · outbound

This paper cites Certified adversarial robustness via randomized smoothing,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Certified adversarial robustness via randomized smoothing,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-14T04:28:26.734827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17e930bb-7ba3-4934-a384-a0a384c3dc26 · outbound

This paper cites Robustifying zero-shot vision language models by subspaces alignment,.

Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Robustifying zero-shot vision language models by subspaces alignment,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:28:27.226468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.