Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:43:03.602324Z
Paper Citation Record · LEDGER
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling
As of 23 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2505.21187.
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 2026-08-23T06:30:58.430688+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
54 of 54 outbound references displayed
- verified exact9
- verified fuzzy35
- unresolved8
- parse uncertain0
- malformed identifier1
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 7ca1232f-26d0-4459-bbf7-456459a068a7 · outbound
This paper cites Asynchronous Cor- ner Detection and Tracking for Event Cameras in Real Time.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Asynchronous Cor- ner Detection and Tracking for Event Cameras in Real Time
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:11.912695Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation d2dd32ef-033c-4672-97bb-ffee14da4473 · outbound
This paper cites A Low Power, Fully Event-Based Gesture Recognition System.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling A Low Power, Fully Event-Based Gesture Recognition System
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:11.621817Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation e680d0ac-6548-47c4-892b-cc1f43648de5 · outbound
This paper cites Pushing the boundaries of event subsampling in event-based video classification using CNNs.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Pushing the boundaries of event subsampling in event-based video classification using CNNs
Reference 3
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:43:05.434337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4cff2c93-e62b-4df9-bb35-aab7c3e20b61 · outbound
This paper cites Wes Baldwin, Ruixu Liu, Mohammed Almatrafi, Vijayan Asari, and Keigo Hirakawa.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Wes Baldwin, Ruixu Liu, Mohammed Almatrafi, Vijayan Asari, and Keigo Hirakawa
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:11.438707Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 641e9ea7-c5a1-4698-9999-2e257d1c6b81 · outbound
This paper cites an unresolved cited work.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Unresolved cited work
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:42:56.817759Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7ffe0839-1242-483b-a991-08eda4742b0b · outbound
This paper cites A Differentiable Recurrent Surface for Asynchronous Event-Based Data.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling A Differentiable Recurrent Surface for Asynchronous Event-Based Data
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:43:05.257922Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ec81ff45-b300-4cf5-b8d8-3c244b0ed610 · outbound
This paper cites A Power-Performance Approach to Comparing Sen- sor Families, with application to comparing neuromorphic to traditional vision sensors.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling A Power-Performance Approach to Comparing Sen- sor Families, with application to comparing neuromorphic to traditional vision sensors
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:11.285143Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation eac65091-a8f1-4804-a6b0-194f3d5d8c28 · outbound
This paper cites Spatial and Temporal Downsampling in Event-Based Visual Classifica- tion.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Spatial and Temporal Downsampling in Event-Based Visual Classifica- tion
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:11.108613Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation d5ede379-c481-4428-a855-56db688a61d1 · outbound
This paper cites Feedback control of event cameras.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Feedback control of event cameras
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:10.921425Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 0d1b8703-5327-4b88-8515-04f310d0d34a · outbound
This paper cites Incorporating learnable membrane time constant to enhance learning of spiking neu- ral networks.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Incorporating learnable membrane time constant to enhance learning of spiking neu- ral networks
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:10.698510Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 5ef0203d-494d-4dba-bec3-6ceaea36b1c1 · outbound
This paper cites Learning gen- erative visual models from few training examples: An incre- mental Bayesian approach tested on 101 object categories.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Learning gen- erative visual models from few training examples: An incre- mental Bayesian approach tested on 101 object categories
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:10.526458Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 101dc209-87eb-48f5-afc5-dc69758d0b4a · outbound
This paper cites Event Density Based Denois- ing Method for Dynamic Vision Sensor.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Event Density Based Denois- ing Method for Dynamic Vision Sensor
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:10.329183Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 6cd996d9-1281-4ef6-b9eb-1ddbc24a1c03 · outbound
This paper cites an unresolved cited work.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Unresolved cited work
Reference 13
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T13:43:10.150758Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 73f4b938-dca7-43a6-b439-fd525e8e5dab · outbound
This paper cites Davison, Jorg Conradt, Kostas Daniilidis, and Da- vide Scaramuzza.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Davison, Jorg Conradt, Kostas Daniilidis, and Da- vide Scaramuzza
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:09.975216Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4ca48eb0-55b6-4a23-a076-1065811a4f77 · outbound
This paper cites End-to-End Learning of Repre- sentations for Asynchronous Event-Based Data.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling End-to-End Learning of Repre- sentations for Asynchronous Event-Based Data
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:09.831209Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4558f77b-8c03-4095-8089-1553e17fcb61 · outbound
This paper cites Dense Continuous-Time Optical Flow from Events and Frames.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Dense Continuous-Time Optical Flow from Events and Frames
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:43:05.064372Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f7b304e0-77e3-465f-91f5-30675053cc8e · outbound
This paper cites Ev- Downsampling: a robust method for downsampling event camera data.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Ev- Downsampling: a robust method for downsampling event camera data
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:09.649233Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 6b1270e0-8abe-4089-b461-e68dafd6fd30 · outbound
This paper cites luvHarris: A Practical Corner Detector for Event-Cameras.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling luvHarris: A Practical Corner Detector for Event-Cameras
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:09.415894Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3c2468cb-b2cc-430a-9ac8-f17a254cf6a5 · outbound
This paper cites Event Data Downscaling for Embedded Computer Vision:.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Event Data Downscaling for Embedded Computer Vision:
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:09.201138Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 563c6ae7-e8e1-4131-bd29-f48901c7d7e6 · outbound
This paper cites Performance comparison of DVS data spatial downscaling methods using Spiking Neural Networks.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Performance comparison of DVS data spatial downscaling methods using Spiking Neural Networks
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:08.975279Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation b3ad903b-6cf9-4704-8f58-fc49c7cfacc3 · outbound
This paper cites EventDrop: data augmentation for event-based learning,.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling EventDrop: data augmentation for event-based learning,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:08.800789Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 53c9a25d-c131-44cb-b783-f93cb5889870 · outbound
This paper cites Low Cost and Latency Event Camera Background Activity Denoising.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Low Cost and Latency Event Camera Background Activity Denoising
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:08.631497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3355bc01-bd28-4d89-bc1f-eef9eb820474 · outbound
This paper cites Autonomous Drone Racing: A Survey.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Autonomous Drone Racing: A Survey
Reference 23
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:43:04.845627Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 656d4cd0-aa6c-45dc-bdec-cad9afb136b0 · outbound
This paper cites Harris and M.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Harris and M
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:08.475373Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 8a36996c-0d5a-4d33-a5a6-212386ef2d24 · outbound
This paper cites Deep Residual Learning for Image Recognition.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Deep Residual Learning for Image Recognition
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:42:59.968536Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3de38d29-ba00-434c-8507-cd377a3a5929 · outbound
This paper cites O(N)O(N)-Space Spatiotemporal Filter for Reducing Noise in Neuromorphic Vision Sensors.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling O(N)O(N)-Space Spatiotemporal Filter for Reducing Noise in Neuromorphic Vision Sensors
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:08.315722Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 7b4d0146-fe62-4298-9180-5e884f8d9906 · outbound
This paper cites Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:08.158638Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 2a0cbbad-a5cf-4d74-b772-310f1823f53d · outbound
This paper cites Adam: A Method for Stochastic Optimization.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Adam: A Method for Stochastic Optimization
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:43:00.511321Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6e47ce5a-d7d2-4b7c-9633-515c73cb8dbf · outbound
This paper cites Masked Event Modeling: Self-Supervised Pretraining for Event Cameras.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Masked Event Modeling: Self-Supervised Pretraining for Event Cameras
Reference 29
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:43:04.673763Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation b00b40b0-8476-4d4e-b0f6-7995e1b12134 · outbound
This paper cites Bio-inspired stereo vision system with silicon retina imagers.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Bio-inspired stereo vision system with silicon retina imagers
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:08.031128Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 76d5aa9a-b550-4584-a502-bebed33cec5f · outbound
This paper cites Shi, and Ryad B.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Shi, and Ryad B
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:07.861070Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 0029553c-693e-46ef-a09b-d77438d621e3 · outbound
This paper cites FA-Harris: A Fast and Asynchronous Corner De- tector for Event Cameras.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling FA-Harris: A Fast and Asynchronous Corner De- tector for Event Cameras
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:07.717741Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 971efaa3-62a2-4b7c-93b7-1a1ce2c36c2d · outbound
This paper cites Design of a spatiotemporal correlation filter for event-based sensors.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Design of a spatiotemporal correlation filter for event-based sensors
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:07.607837Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 60b93c66-09f7-4f7c-acc3-d8f715ee718c · outbound
This paper cites Information theoretic preattentive saliency: A closed-form solution.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Information theoretic preattentive saliency: A closed-form solution
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:07.524071Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 84e39f01-245c-4620-9236-d0dd81f9d000 · outbound
This paper cites The Improbability of Har- ris Interest Points.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling The Improbability of Har- ris Interest Points
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:07.390545Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 68f6d71a-00e3-4a73-8f36-436300847cb9 · outbound
This paper cites VLSI analogs of neuronal visual process- ing: a synthesis of form and function.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling VLSI analogs of neuronal visual process- ing: a synthesis of form and function
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:07.271330Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ffb64f8a-bb4a-4d5f-a92f-38f48131fe3f · outbound
This paper cites Maqueda, Antonio Loquercio, Guillermo Gallego, Narciso Garcia, and Davide Scaramuzza.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Maqueda, Antonio Loquercio, Guillermo Gallego, Narciso Garcia, and Davide Scaramuzza
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:07.106151Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3496b353-02d5-442e-ac00-e18c1a292884 · outbound
This paper cites Fast Event-based Corner Detection.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Fast Event-based Corner Detection
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:06.956951Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation a52bec1b-bd81-46e3-99ff-aa5210851179 · outbound
This paper cites Cohen, and Nitish Thakor.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Cohen, and Nitish Thakor
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:06.768934Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ad060d84-97e5-4567-9c1d-bbd8da9a2f68 · outbound
This paper cites HFirst: A Temporal Approach to Object Recognition.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling HFirst: A Temporal Approach to Object Recognition
Reference 40
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:43:04.472712Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation cce118d2-8ca4-47a0-946c-7c42d13a8a6b · outbound
This paper cites GET: Group Event Transformer for Event-Based Vision.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling GET: Group Event Transformer for Event-Based Vision
Reference 41
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:43:04.289971Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 801b19e0-c3e2-4736-8158-8f5483e03a70 · outbound
This paper cites A qvga 143 db dynamic range frame-free pwm image sensor with lossless pixel-level video compression and time-domain cds.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling A qvga 143 db dynamic range frame-free pwm image sensor with lossless pixel-level video compression and time-domain cds
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:43:02.407259Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 11cf3efc-37aa-44f4-b8fc-21a79b5cb3b5 · outbound
This paper cites Event- Net: Asynchronous Recursive Event Processing.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Event- Net: Asynchronous Recursive Event Processing
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:06.651623Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f66af2ed-ef07-412a-8604-54925052ec4a · outbound
This paper cites HATS: Histograms of Aver- aged Time Surfaces for Robust Event-Based Object Classifi- cation.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling HATS: Histograms of Aver- aged Time Surfaces for Robust Event-Based Object Classifi- cation
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:06.466592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 381c0981-a2a5-4eae-8163-268290bde841 · outbound
This paper cites Fast event-based Harris corner detection exploiting the advan- tages of event-driven cameras.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Fast event-based Harris corner detection exploiting the advan- tages of event-driven cameras
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:06.303038Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 6cf73d44-7218-45cb-afc9-f37d22ffa3da · outbound
This paper cites Spiking Neural Networks for event-based action recognition: A new task to understand their advantage.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Spiking Neural Networks for event-based action recognition: A new task to understand their advantage
Reference 46
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-07T13:43:04.074010Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ed898604-9d66-4632-aea7-4c27d8e9dc48 · outbound
This paper cites Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:43:02.850359Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77f7f3a6-4410-4a50-a04a-8843cbd59adb · outbound
This paper cites Unsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Unsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:06.121540Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 5e6c22cc-a74c-4344-b0f4-aea73f87d8dc · outbound
This paper cites From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection
Reference 49
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:43:03.817547Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation bfe2af0c-03ed-4775-ad18-4af9d55a9bac · outbound
This paper cites The parameters are chosen to ensure that the average number of events ⟨N ⟩ remains similar across different subsampling methods at each subsampling level for each dataset.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling The parameters are chosen to ensure that the average number of events ⟨N ⟩ remains similar across different subsampling methods at each subsampling level for each dataset
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:43:05.925704Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c39446f3-5337-4af6-8120-31dd2bf4d7c6 · outbound
This paper cites an unresolved cited work.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Unresolved cited work
Reference 53
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T13:43:05.796710Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 908c38ef-9c4a-4f21-971a-58817c2f427e · outbound
This paper cites In the first row, we show the original event data without any subsampling.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling In the first row, we show the original event data without any subsampling
Reference 54
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-07T13:43:05.638431Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 87a76708-cc5e-4afe-a7aa-ac7c8bfac588 · outbound
This paper cites an unresolved cited work.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling Unresolved cited work
Reference 2018
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T13:43:11.745825Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ec92de2e-bb5d-4ae6-b2b0-1456cd7763e4 · outbound
This paper cites EventDrop: data augmentation for event-based learning.
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling EventDrop: data augmentation for event-based learning
Reference 2021
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:43:04.953241Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Pith citing papers
No inbound Pith citation observations are available.