Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-24T20:48:51.430219Z
Paper Citation Record · LEDGER
How much real data do we actually need: Analyzing object detection performance using synthetic and real data
As of 28 July 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:1907.07061.
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 2026-07-27T06:30:09.085275+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
20 of 20 outbound references displayed
- verified exact7
- verified fuzzy4
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch9
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 8313f1da-b1bf-4dd1-8eae-844d6f4c9db8 · outbound
This paper cites nuScenes: A multimodal dataset for autonomous driving.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data nuScenes: A multimodal dataset for autonomous driving
Reference 1
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-24T20:49:54.489370Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 4266b223-15bb-4c8b-89be-49173b3180e4 · outbound
This paper cites Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation
Reference 2
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-24T20:49:54.512900Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation b329873e-f0a6-4afb-81d3-49477a34c205 · outbound
This paper cites Augmented LiDAR Simulator for Autonomous Driving.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data Augmented LiDAR Simulator for Autonomous Driving
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T20:49:54.543397Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation be318d2b-c7c3-4a2f-a671-abc79080868e · outbound
This paper cites FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
Reference 4
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-24T20:49:54.468616Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 6cffc965-d240-4a06-99e7-799dfed7cd3a · outbound
This paper cites CyCADA: Cycle-Consistent Adversarial Domain Adaptation.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T20:49:54.461919Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 2fe245a6-4c39-4465-ae2a-f52373c8ab45 · outbound
This paper cites Speed/accuracy trade-offs for modern con- volutional object detectors.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data Speed/accuracy trade-offs for modern con- volutional object detectors
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T20:49:55.185914Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 6ddcd631-94bd-490e-b5d6-13ed7b8ef69e · outbound
This paper cites Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking
Reference 7
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-24T20:49:54.505546Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 0fed72cc-348a-4a71-ae40-b9bfff8203bb · outbound
This paper cites Few-shot image recognition by predicting parameters from activations.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data Few-shot image recognition by predicting parameters from activations
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T20:49:55.179258Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 2f593fe5-48f3-4565-9cb0-2e1efc30ae6a · outbound
This paper cites and Frtunikj, J.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data and Frtunikj, J
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T20:49:55.182486Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 7a6c5899-d891-40db-954d-23aa1d6cc55c · outbound
This paper cites XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T20:49:54.498535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 8bb5ce35-4329-4375-b408-9e30013f7dc2 · outbound
This paper cites Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
Reference 11
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-24T20:49:54.536394Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 8c07504a-d1d2-44d6-92b2-5e4c77f3cf0d · outbound
This paper cites Complex-YOLO: Real-time 3D Object Detection on Point Clouds.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data Complex-YOLO: Real-time 3D Object Detection on Point Clouds
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T20:49:54.475074Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 46e3c302-20da-4009-a023-7e36c31fb896 · outbound
This paper cites Learning to Compare: Relation Network for Few-Shot Learning.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data Learning to Compare: Relation Network for Few-Shot Learning
Reference 13
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-24T20:49:54.563592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation b89f2164-3bef-4628-8407-04276a846f38 · outbound
This paper cites Dynamic Graph CNN for Learning on Point Clouds.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data Dynamic Graph CNN for Learning on Point Clouds
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T20:49:54.519856Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation fef4cc0a-9e77-4d9f-bd2d-315bce194827 · outbound
This paper cites Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing
Reference 15
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-24T20:49:54.482333Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 5f6e554e-133b-4d81-985b-2bdf9e972888 · outbound
This paper cites SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
Reference 16
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-24T20:49:54.454179Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 5fd1a89f-2955-4f5c-807f-330bb9426fba · outbound
This paper cites Learning cross-modal deep representations for robust pedestrian detection.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data Learning cross-modal deep representations for robust pedestrian detection
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T20:49:55.189664Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation fddaeb79-c8e7-471a-944c-7b73f50394c7 · outbound
This paper cites BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T20:49:54.527731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 362e5f2c-4716-48d1-859c-f078333d22d1 · outbound
This paper cites Fully Convolutional Adaptation Networks for Semantic Segmentation.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data Fully Convolutional Adaptation Networks for Semantic Segmentation
Reference 19
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-24T20:49:54.557231Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 6fec306a-39b8-4b90-82da-081721eb531f · outbound
This paper cites Pyramid Scene Parsing Network.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data Pyramid Scene Parsing Network
Reference 20
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T20:49:54.550214Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Pith citing papers
No inbound Pith citation observations are available.