Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:30:31.579699Z
Paper Citation Record · LEDGER
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery
As of 8 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2506.02367.
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 2026-08-07T06:34:17.273281+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
21 of 21 outbound references displayed
- verified exact0
- verified fuzzy18
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 89cdb839-cf48-41e2-acdb-585e677c887a · outbound
This paper cites k-means++: the advantages of careful seeding.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery k-means++: the advantages of careful seeding
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:34.028568Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1c35e978-b2bc-474b-a06e-e832874bb702 · outbound
This paper cites an unresolved cited work.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery Unresolved cited work
Reference 2
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T11:30:34.020106Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4e67a7e3-9627-42f7-867c-eea890065b62 · outbound
This paper cites an unresolved cited work.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T11:30:34.011683Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 387ee97f-2da8-4fe3-83ba-3b775729cab0 · outbound
This paper cites Open-world semi-supervised learning, 2021.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery Open-world semi-supervised learning, 2021
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:34.003720Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 16053795-7c8d-41c2-ad61-c8ae834a8935 · outbound
This paper cites On the relationship between self-attention and convolutional layers.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery On the relationship between self-attention and convolutional layers
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:33.996110Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 3631d281-c058-4724-ba70-69d86d04c1a3 · outbound
This paper cites Ima- genet: A large-scale hierarchical image database.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery Ima- genet: A large-scale hierarchical image database
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:33.988004Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1747d7a3-c742-4f13-8611-93251f8d0cc1 · outbound
This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery An image is worth 16x16 words: Transformers for image recognition at scale
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:33.980483Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 08b46b37-a056-4c8e-aba0-2f99e02dc5bf · outbound
This paper cites 3d object representations for fine-grained categorization.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery 3d object representations for fine-grained categorization
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:33.972480Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e9f24d98-e970-4588-892b-818e8a010595 · outbound
This paper cites Learning multiple layers of features from tiny images.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery Learning multiple layers of features from tiny images
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:33.964288Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 678d3d02-ed12-4825-b224-7ba8e023ba35 · outbound
This paper cites Active generalized category discovery.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery Active generalized category discovery
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:33.955858Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 32d21600-f8ab-43c5-bd7c-cfa5eb589fff · outbound
This paper cites A review of deep learning in image recognition.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery A review of deep learning in image recognition
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:33.856741Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 0d32dbd6-ff17-4960-bcb4-5570cbb37010 · outbound
This paper cites Dynamic conceptional contrastive learning for generalized category discovery.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7579–7588, 2023.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery Dynamic conceptional contrastive learning for generalized category discovery.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7579–7588, 2023
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:33.644587Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 9006388c-716c-457f-bca0-42fead445df0 · outbound
This paper cites Openldn: Learning to discover novel classes for open-world semi-supervised learning.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery Openldn: Learning to discover novel classes for open-world semi-supervised learning
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:33.452950Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5432bc94-06ed-4a81-b615-b5d972e31742 · outbound
This paper cites A graph-theoretic framework for understanding open-world semi-supervised learning.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery A graph-theoretic framework for understanding open-world semi-supervised learning
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:33.294114Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f5e7a500-bde9-4f04-b0bf-007fa2f64400 · outbound
This paper cites Attention is all you need.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery Attention is all you need
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:32.978795Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 46c91d96-ab6b-4c5e-b671-1fc731ad4fcd · outbound
This paper cites Generalized category discovery.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery Generalized category discovery
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:32.793604Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation fdd2c413-c3d7-4356-8ad9-4087e1d706a3 · outbound
This paper cites No representation rules them all in category discovery.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery No representation rules them all in category discovery
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:32.549434Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8114b487-f77c-4990-9086-f2d8d68223d8 · outbound
This paper cites an unresolved cited work.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery Unresolved cited work
Reference 18
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T11:30:32.379158Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation def108d9-acde-49b6-bd4d-d1a3f6c8f305 · outbound
This paper cites Parametric classification for generalized category discovery: A baseline study.2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 16544–16554, 2022.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery Parametric classification for generalized category discovery: A baseline study.2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 16544–16554, 2022
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:32.174671Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation cafb86ce-a5e2-479d-a5b2-8536bc776cab · outbound
This paper cites Khan, Zhiqiang Shen, Muzammal Naseer, Guangyi Chen, and Fahad Shahbaz Khan.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery Khan, Zhiqiang Shen, Muzammal Naseer, Guangyi Chen, and Fahad Shahbaz Khan
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:31.986865Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8fe31085-e362-470c-8178-955e105676b7 · outbound
This paper cites Learning semi-supervised gaussian mixture models for generalized category discovery.
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery Learning semi-supervised gaussian mixture models for generalized category discovery
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:30:31.790722Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Pith citing papers
No inbound Pith citation observations are available.