Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:04:07.964738Z
Paper Citation Record · LEDGER
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery
As of 7 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2507.04051.
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, observed 2026-07-02T14:42:01.334822Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T14:47:03.347932Z
Reference resolution
62 of 62 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 80c62f9a-c4cc-4f2b-a095-5d1a411da381 · outbound
This paper cites Generalized category discovery with decoupled prototypical network.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Generalized category discovery with decoupled prototypical network
Reference 1
- Resolution
- verified fuzzy
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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 be4de4dd-d6b3-4e2a-a1e8-e8938b5fc0ad · outbound
This paper cites Emerg- ing properties in self-supervised vision transformers.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Emerg- ing properties in self-supervised vision transformers
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:19.730007Z
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 8e51297e-6f00-48aa-995c-1bb72b9e2c0a · outbound
This paper cites Parametric information maxi- mization for generalized category discovery.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Parametric information maxi- mization for generalized category discovery
Reference 3
- Resolution
- verified fuzzy
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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 0179b4fc-13ab-4193-a27a-b44ceff7c652 · outbound
This paper cites Text-to-image diffusion mod- els are zero shot classifiers.NeurIPS, 2024.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Text-to-image diffusion mod- els are zero shot classifiers.NeurIPS, 2024
Reference 4
- Resolution
- verified fuzzy
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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 4861388c-cda4-457d-bd67-de16f0fe6d2b · outbound
This paper cites On-the-fly cate- gory discovery.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery On-the-fly cate- gory discovery
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:19.009219Z
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 b1fb38e7-eeb4-489a-9d81-13dcbb26e304 · outbound
This paper cites Diffusion-based probabilistic un- certainty estimation for active domain adaptation.NeurIPS,.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Diffusion-based probabilistic un- certainty estimation for active domain adaptation.NeurIPS,
Reference 6
- Resolution
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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 d162ed97-b4a9-413f-a915-fa147a34d665 · outbound
This paper cites Diverse data augmentation with diffusions for effective test-time prompt tuning.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Diverse data augmentation with diffusions for effective test-time prompt tuning
Reference 7
- Resolution
- verified fuzzy
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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 d826a0c4-bc77-4310-9dc6-4e57201b8649 · outbound
This paper cites A unified objective for novel class discovery.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery A unified objective for novel class discovery
Reference 8
- Resolution
- verified fuzzy
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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 7da76288-af32-4b69-94cc-25cd4cfb5711 · outbound
This paper cites DreamDA: Generative Data Augmentation with Diffusion Models.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery DreamDA: Generative Data Augmentation with Diffusion Models
Reference 9
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f9417917-ccfe-4270-846e-d0a19981cade · outbound
This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
Reference 10
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 30fce410-f8ef-45b8-9b41-eca3d80d5499 · outbound
This paper cites Learning to discover novel visual categories via deep transfer cluster- ing.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Learning to discover novel visual categories via deep transfer cluster- ing
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:18.134874Z
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 c582aab2-070c-4778-876b-a369cb4d43c7 · outbound
This paper cites Autonovel: Automati- cally discovering and learning novel visual categories.IEEE TPAMI, 2021.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Autonovel: Automati- cally discovering and learning novel visual categories.IEEE TPAMI, 2021
Reference 12
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation e3039c75-7f70-4d7b-a837-bdf4d678dcdd · outbound
This paper cites John Wiley & Sons, Inc., 1975.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery John Wiley & Sons, Inc., 1975
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:17.482537Z
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 b5251441-0a8b-4390-8f88-04f93ba1fe46 · outbound
This paper cites Is synthetic data from generative models ready for image recognition?.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Is synthetic data from generative models ready for image recognition?
Reference 14
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6d4fbd5e-afc3-4c1e-91ac-ec7b4e0c7f5e · outbound
This paper cites Denoising diffu- sion probabilistic models.NeurIPS, 2020.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Denoising diffu- sion probabilistic models.NeurIPS, 2020
Reference 15
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d3f9ee2f-a393-43b4-b9b2-1c2c4b0c80b7 · outbound
This paper cites Diffusemix: Label- preserving data augmentation with diffusion models.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Diffusemix: Label- preserving data augmentation with diffusion models
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:17.240886Z
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 66087554-248f-40de-a8b5-b3014930b3da · outbound
This paper cites Joint representation learning and novel category discovery on single-and multi-modal data.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Joint representation learning and novel category discovery on single-and multi-modal data
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:16.997108Z
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 0c61d7fc-acb8-4334-94af-e1708b4ab2d7 · outbound
This paper cites Podia- 3d: Domain adaptation of 3d generative model across large domain gap using pose-preserved text-to-image diffusion.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Podia- 3d: Domain adaptation of 3d generative model across large domain gap using pose-preserved text-to-image diffusion
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:16.699174Z
Source-reported events for the cited work
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Observation 224411cd-e925-4919-bbca-bd7776c8fb26 · outbound
This paper cites Proxy anchor-based unsu- pervised learning for continuous generalized category dis- covery.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Proxy anchor-based unsu- pervised learning for continuous generalized category dis- covery
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:16.441761Z
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 4533c11d-4873-48d0-b064-7c24d34c2b21 · outbound
This paper cites 3d object representations for fine-grained categorization.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery 3d object representations for fine-grained categorization
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:16.249270Z
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 79835513-0156-4ede-a922-85cc9b61da0e · outbound
This paper cites The hungarian method for the assignment problem.Naval research logistics quarterly, 1955.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery The hungarian method for the assignment problem.Naval research logistics quarterly, 1955
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:15.999730Z
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 73bab13b-4508-4c46-a698-1295150627fa · outbound
This paper cites Diffusion models already have A semantic latent space.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Diffusion models already have A semantic latent space
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:15.731666Z
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 350dd361-901f-4a69-b8a5-07ce2411b39c · outbound
This paper cites Your diffusion model is secretly a zero-shot classifier.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Your diffusion model is secretly a zero-shot classifier
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:15.441681Z
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 6fc721c7-009a-4829-b5be-000c6877bd50 · outbound
This paper cites Context-based and diversity-driven specificity in compositional zero-shot learning.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Context-based and diversity-driven specificity in compositional zero-shot learning
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:15.152102Z
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 0a20761c-314d-44bd-80e2-3d30f7bef693 · outbound
This paper cites Novel class discovery for ultra-fine-grained visual categorization.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Novel class discovery for ultra-fine-grained visual categorization
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:14.853782Z
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 df47c484-b619-4c5c-8cc4-14fbf0cd7ebf · outbound
This paper cites Pmgnet: Disentanglement and entanglement benefit mutually for compositional zero-shot learning.Computer Vision and Image Understanding, 249: 104197, 2024.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Pmgnet: Disentanglement and entanglement benefit mutually for compositional zero-shot learning.Computer Vision and Image Understanding, 249: 104197, 2024
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:14.703661Z
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 97025a27-6974-41f1-9444-8461a230c050 · outbound
This paper cites Some methods for classification and analysis of multivariate observations.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Some methods for classification and analysis of multivariate observations
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:14.567429Z
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 1958582f-8f0b-4302-b6dc-096ed90e1847 · outbound
This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Reference 28
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5bb501fa-4fde-4d69-8210-a24c9c510626 · outbound
This paper cites Cats and dogs.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Cats and dogs
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:14.387409Z
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 6566e612-900b-4616-b8e9-e7c3c7748897 · outbound
This paper cites Unsupervised domain adap- tation via domain-adaptive diffusion.IEEE TIP, 2024.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Unsupervised domain adap- tation via domain-adaptive diffusion.IEEE TIP, 2024
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:14.218591Z
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 bdf49bb0-1779-4c04-b0fd-79fad2dcebab · outbound
This paper cites Dynamic conceptional contrastive learning for generalized category discovery.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Dynamic conceptional contrastive learning for generalized category discovery
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:14.080167Z
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 3b4c0a5d-ee1b-4d69-98de-29622b3a8873 · outbound
This paper cites Federated generalized category discovery.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Federated generalized category discovery
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:13.921901Z
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 f83a3c52-96f4-4a0e-a6ff-1ff1afaae9d0 · outbound
This paper cites On the momentum term in gradient descent learning algorithms.Neural networks, 1999.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery On the momentum term in gradient descent learning algorithms.Neural networks, 1999
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:13.771841Z
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 9781de53-195c-49ab-87d5-fabe524792b2 · outbound
This paper cites Learn- ing transferable visual models from natural language super- vision.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Learn- ing transferable visual models from natural language super- vision
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:13.595263Z
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 1b28e3d6-570c-496d-9021-10bb2028cf94 · outbound
This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Hierarchical Text-Conditional Image Generation with CLIP Latents
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:04:04.408297Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cac759c7-a9bf-46ba-9162-3030441069de · outbound
This paper cites High-resolution image syn- thesis with latent diffusion models.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery High-resolution image syn- thesis with latent diffusion models
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:04:04.537713Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 26c729fc-8a0f-42dc-86f3-19e64aabf792 · outbound
This paper cites The map equation.The European Physical Journal Special Top- ics, 2009.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery The map equation.The European Physical Journal Special Top- ics, 2009
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:13.425934Z
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 fe46bf1e-021e-4396-9efa-68849ebd658f · outbound
This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:04:04.817002Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 866cf37a-bb0a-45e4-b3c7-0f1fda380af9 · outbound
This paper cites Diversity is definitely needed: Improving model-agnostic zero-shot classification via stable diffusion.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Diversity is definitely needed: Improving model-agnostic zero-shot classification via stable diffusion
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:13.153079Z
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 51895fc6-65f3-40bc-8abf-fc87ae72868e · outbound
This paper cites Animating rotation with quaternion curves.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Animating rotation with quaternion curves
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:12.866118Z
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 09d7be6e-14a4-48c1-a45c-2be0b997bd8f · outbound
This paper cites Latent Space Disentanglement in Diffusion Transformers Enables Zero-shot Fine-grained Semantic Editing.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Latent Space Disentanglement in Diffusion Transformers Enables Zero-shot Fine-grained Semantic Editing
Reference 41
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:04:08.251440Z
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 ad38fcbd-c0f0-405c-ae97-dbd1f786fdfc · outbound
This paper cites Effective Data Augmentation With Diffusion Models.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Effective Data Augmentation With Diffusion Models
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:04:05.419193Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e2b46b72-91c8-479c-a5d0-ff4c8c9d5ec6 · outbound
This paper cites Test-time stain adaptation with diffusion models for histopathology image classification.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Test-time stain adaptation with diffusion models for histopathology image classification
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:12.638097Z
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 97f2a8af-5624-47f1-b06e-e2b882319e87 · outbound
This paper cites The inaturalist species classification and de- tection dataset.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery The inaturalist species classification and de- tection dataset
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:12.416789Z
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 46bc823c-ec11-448e-90d6-0e899768f7b2 · outbound
This paper cites Generalized category discovery.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Generalized category discovery
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:12.156459Z
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 7e3ffabf-8cf9-477d-a1dc-1368d837d1d0 · outbound
This paper cites The caltech-ucsd birds-200-2011 dataset.Computation & Neural Systems Technical Report,.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery The caltech-ucsd birds-200-2011 dataset.Computation & Neural Systems Technical Report,
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:11.889115Z
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 ed401a11-8064-46fc-ab18-3143c313c745 · outbound
This paper cites Videocomposer: Compositional video synthesis with motion controllability.NeurIPS, 2023.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Videocomposer: Compositional video synthesis with motion controllability.NeurIPS, 2023
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:11.655017Z
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 9f2e9c02-d5b1-49dd-8e9e-7ed2020ce9a5 · outbound
This paper cites Enhance im- age classification via inter-class image mixup with diffusion model.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Enhance im- age classification via inter-class image mixup with diffusion model
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:11.361450Z
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 f562bbd3-7510-4d95-a389-958a8b2d2b6b · outbound
This paper cites Parametric classification for generalized category discovery: A baseline study.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Parametric classification for generalized category discovery: A baseline study
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:11.120012Z
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 c9c1cf19-07fe-4f3a-bd99-dec962ca15f8 · outbound
This paper cites Metagcd: Learning to continually learn in generalized cat- egory discovery.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Metagcd: Learning to continually learn in generalized cat- egory discovery
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:10.849965Z
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 f579127e-1524-471b-9b6e-16d61340ee0a · outbound
This paper cites Learning to distin- guish samples for generalized category discovery.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Learning to distin- guish samples for generalized category discovery
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:10.636275Z
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 0c4de176-dd14-42b1-8c66-8a00aa86e98f · outbound
This paper cites Diffusion models and semi-supervised learners benefit mutually with few labels.NeurIPS, 2024.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Diffusion models and semi-supervised learners benefit mutually with few labels.NeurIPS, 2024
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:10.342178Z
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 8737357b-a56a-4ef9-88ad-620086ed9711 · outbound
This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery CoCa: Contrastive Captioners are Image-Text Foundation Models
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:04:06.787739Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 396c1e01-357c-4ab7-841a-9e43a28a8afa · outbound
This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Cutmix: Regu- larization strategy to train strong classifiers with localizable features
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:10.072988Z
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 2c4ecc54-095d-4c20-a6f0-a838a0889318 · outbound
This paper cites mixup: Beyond 903 empirical risk minimization.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery mixup: Beyond 903 empirical risk minimization
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:09.799342Z
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 3bb875e7-3b1b-4ea0-9766-810707e12571 · outbound
This paper cites Incremental general- ized category discovery.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Incremental general- ized category discovery
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:09.596879Z
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 b9e7475f-ed7a-4771-9c45-d968e6663aa8 · outbound
This paper cites Pyramidal person re-identification via multi-loss dynamic training.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Pyramidal person re-identification via multi-loss dynamic training
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:09.369729Z
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 4d56ddc2-dee5-46b1-ba66-8a189ce247ee · outbound
This paper cites Textual knowledge matters: Cross-modality co- teaching for generalized visual class discovery.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Textual knowledge matters: Cross-modality co- teaching for generalized visual class discovery
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:04:07.445985Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c8add011-bab9-4d78-9f71-0f1bc9dd011a · outbound
This paper cites Prototypical hash encoding for on-the-fly fine- grained category discovery.NeurIPS, 2025.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Prototypical hash encoding for on-the-fly fine- grained category discovery.NeurIPS, 2025
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:09.096970Z
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 ceb82185-fd62-4e4a-9ac6-b2d57c5e0533 · outbound
This paper cites Neighborhood contrastive learn- ing for novel class discovery.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Neighborhood contrastive learn- ing for novel class discovery
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:08.808728Z
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 05a0804e-4bc0-4a52-91a1-1f28d1a408ac · outbound
This paper cites Novel class discovery in chest x-rays via paired images and text.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Novel class discovery in chest x-rays via paired images and text
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:04:08.560625Z
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 97141dff-f77c-4606-9e25-ab8659dd7663 · outbound
This paper cites Training on Thin Air: Improve Image Classification with Generated Data.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Training on Thin Air: Improve Image Classification with Generated Data
Reference 62
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-06T20:04:07.964738Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 8d6f15b2-8580-4531-a5a9-9531450e1ff7 · inbound
Identifying Latent Concepts and Structures for Generalized Category Discovery cites this paper.
Identifying Latent Concepts and Structures for Generalized Category Discovery Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery
Reference 65
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T14:47:03.349247Z
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.