Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:09:02.534956Z
Paper Citation Record · LEDGER
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications
As of 9 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2506.22360.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
16 of 16 outbound references displayed
- verified exact0
- verified fuzzy13
- unresolved2
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 81c44fd0-ebde-488a-8ac7-3e99e0fb96e4 · outbound
This paper cites Retinomorphic event-based vision sensors: bioinspired cameras with spiking output,.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications Retinomorphic event-based vision sensors: bioinspired cameras with spiking output,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:09:06.279573Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c405040e-b931-4e4c-b93c-4a243d91c692 · outbound
This paper cites Collision detection for UAVs using event cameras,.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications Collision detection for UAVs using event cameras,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:09:06.056884Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 5928c7c4-b384-45a5-83c0-f020a84d0664 · outbound
This paper cites Computer vision for autonomous vehicles: Problems, datasets and state of the art,.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications Computer vision for autonomous vehicles: Problems, datasets and state of the art,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:09:05.729427Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 7c1c734d-ef55-4cfc-bb7e-3077fb0e6b50 · outbound
This paper cites Deep residual learning for image recognition,.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications Deep residual learning for image recognition,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:09:05.499344Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation bb4fcccf-c938-420f-9c8d-1bfe0348068b · outbound
This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:09:01.409174Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f320bced-0efc-405d-8dad-ad6ae8e841ae · outbound
This paper cites A Large Scale Event-based Detection Dataset for Automotive.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications A Large Scale Event-based Detection Dataset for Automotive
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:09:01.498772Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d027ec63-5407-4d35-9aee-93bec5670ad6 · outbound
This paper cites Discussion on event-based cameras for dynamic obstacles recognition and detection for UAVs in outdoor environments,.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications Discussion on event-based cameras for dynamic obstacles recognition and detection for UAVs in outdoor environments,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:09:05.258233Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 27c52030-7cdb-4b16-a955-80882ced381c · outbound
This paper cites End-to-end learning of representations for asynchronous event-based data,.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications End-to-end learning of representations for asynchronous event-based data,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:09:04.991009Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 16eb2bd7-d791-47d7-a1f3-672a7269c565 · outbound
This paper cites A differentiable recurrent surface for asynchronous event-based data,.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications A differentiable recurrent surface for asynchronous event-based data,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:09:04.715159Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation dcda5a45-64d3-42cd-9811-c16d6d3f9d3a · outbound
This paper cites A Multi-Dimensional Covert Transaction Recognition Scheme for Blockchain,.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications A Multi-Dimensional Covert Transaction Recognition Scheme for Blockchain,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:09:04.401949Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation fcf505d2-d59f-4096-87b1-3841f015ac0c · outbound
This paper cites Imagenet large scale visual recognition challenge,.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications Imagenet large scale visual recognition challenge,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:09:04.113831Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f00f41b5-64d3-40fe-b25a-ff4ef307e546 · outbound
This paper cites Evaluating collaborative filtering recommender systems,.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications Evaluating collaborative filtering recommender systems,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:09:03.789239Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 1441bb7b-0b00-4220-b1bc-dc0c20138297 · outbound
This paper cites The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets,.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:09:03.471216Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 873a92b5-e4f4-4781-a6e6-efee1e13133f · outbound
This paper cites The area under the precision-recall curve as a performance metric for rare binary events,.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications The area under the precision-recall curve as a performance metric for rare binary events,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:09:03.157223Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8f587e26-c3fd-4fe6-b1d2-6633a678b8ee · outbound
This paper cites Deep learning in news recommender systems: A comprehensive survey, challenges and future trends,.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications Deep learning in news recommender systems: A comprehensive survey, challenges and future trends,
Reference 15
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-06T22:09:02.421724Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fb9bcae6-b5e3-4c2a-befa-ada9a02fc02b · outbound
This paper cites Dropout: a simple way to prevent neural networks from overfitting,.
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications Dropout: a simple way to prevent neural networks from overfitting,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:09:02.884198Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Pith citing papers
No inbound Pith citation observations are available.