Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:55:40.776768Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 1 inbound Pith citation observation for arXiv:2505.16993.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:55:40.776768Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-13T06:02:40.158866Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-13T06:07:22.539904Z
74 of 74 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 2460445c-9575-432c-a64a-35e294bcf3ca · outbound
Native Segmentation Vision Transformers Swin transformer: Hierarchical vision transformer using shifted windows
Reference 1
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Native Segmentation Vision Transformers Convnext v2: Co-designing and scaling convnets with masked autoencoders
Reference 2
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Native Segmentation Vision Transformers Neighborhood attention transformer
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Native Segmentation Vision Transformers Backpropagation applied to handwritten zip code recognition
Reference 4
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Observation da6f51d4-8ab3-42fd-99f6-aed459b366ac · outbound
Native Segmentation Vision Transformers Schwing, Alexander Kirillov, and Rohit Girdhar
Reference 6
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Native Segmentation Vision Transformers Feature pyramid networks for object detection
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Native Segmentation Vision Transformers FaPN: Feature-aligned pyramid network for dense image prediction
Reference 8
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Reference 9
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Native Segmentation Vision Transformers Clusterformer: Clustering as a universal visual learner
Reference 10
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Native Segmentation Vision Transformers Learning hierarchical image segmentation for recognition and by recognition
Reference 11
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Native Segmentation Vision Transformers Tcformer: Visual recognition via token clustering transformer
Reference 12
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Native Segmentation Vision Transformers Schwing, and Alexander Kirillov
Reference 13
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Native Segmentation Vision Transformers Slic superpixels compared to state-of-the-art superpixel methods
Reference 14
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Native Segmentation Vision Transformers Object-centric learning with slot attention
Reference 15
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Native Segmentation Vision Transformers Mean shift: A robust approach toward feature space analysis
Reference 16
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Native Segmentation Vision Transformers Felzenszwalb and Daniel P
Reference 17
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Native Segmentation Vision Transformers Contour detection and hierarchical image segmentation
Reference 18
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Native Segmentation Vision Transformers Seeds: Superpixels extracted via energy-driven sampling
Reference 19
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Native Segmentation Vision Transformers Semantic understanding of scenes through the ade20k dataset
Reference 20
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Native Segmentation Vision Transformers Panoptic segmentation
Reference 21
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Native Segmentation Vision Transformers Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
Reference 22
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Native Segmentation Vision Transformers Gradient-based learning applied to document recognition
Reference 23
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Native Segmentation Vision Transformers An image is worth 16x16 words: Transformers for image recognition at scale
Reference 24
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Native Segmentation Vision Transformers A convnet for the 2020s
Reference 25
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Observation 0350cb82-c86e-4800-bd90-f7e8a3f9b611 · outbound
Native Segmentation Vision Transformers SAM 2: Segment Anything in Images and Videos
Reference 26
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Native Segmentation Vision Transformers Fully convolutional networks for semantic segmentation
Reference 27
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Native Segmentation Vision Transformers U-net: Convolutional networks for biomedical image segmentation
Reference 28
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Native Segmentation Vision Transformers Rich feature hierarchies for accurate object detection and semantic segmentation
Reference 29
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Native Segmentation Vision Transformers Fast r-cnn
Reference 30
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Observation abbb1278-5402-40c8-b8a9-a796dda25b68 · outbound
Native Segmentation Vision Transformers End-to-end object detection with transformers
Reference 31
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Observation e95186b4-c75a-4946-8c30-eb2aae10353d · outbound
Native Segmentation Vision Transformers Normalized cuts and image segmentation
Reference 32
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Native Segmentation Vision Transformers Multiscale combinatorial grouping
Reference 33
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Reference 34
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Reference 35
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Native Segmentation Vision Transformers Unified perceptual parsing for scene understanding
Reference 37
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Native Segmentation Vision Transformers Berg, and Li Fei-Fei
Reference 38
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Native Segmentation Vision Transformers Learning transferable visual models from natural language supervision
Reference 39
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Reference 40
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Native Segmentation Vision Transformers A simple framework for text-supervised semantic segmentation
Reference 41
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Native Segmentation Vision Transformers Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Reference 42
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Reference 43
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Reference 44
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Native Segmentation Vision Transformers Learning to generate text-grounded mask for open-world semantic segmentation from only image-text pairs
Reference 45
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Reference 46
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Native Segmentation Vision Transformers The role of context for object detection and semantic segmentation in the wild
Reference 47
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Reference 48
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Reference 49
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Native Segmentation Vision Transformers Open- world semantic segmentation via contrasting and clustering vision-language embedding
Reference 51
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Reference 53
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Reference 54
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Reference 55
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Reference 56
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Reference 57
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Native Segmentation Vision Transformers Single-stage semantic segmentation from image labels
Reference 58
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Reference 59
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Reference 60
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Reference 61
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Reference 65
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Reference 67
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Reference 70
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Reference 71
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