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Paper Citation Record · LEDGER

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement

As of 17 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2507.06928.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.06928 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:56:52.215373Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

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  • verified fuzzy26
  • unresolved20
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ad832f65-9297-4a3c-9faa-dee95dc0733b · outbound

This paper cites PDiscoFormer: Relaxing Part Discovery Constraints with Vision Transformers.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement PDiscoFormer: Relaxing Part Discovery Constraints with Vision Transformers

Reference 1

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Observation d0df4fcb-6e4e-4c52-9bee-5fedf66c5f81 · outbound

This paper cites k-means++: The advantages of careful seeding.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement k-means++: The advantages of careful seeding

Reference 2

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Source-reported events for the cited work

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Observation 3e75e198-9cb7-4040-993e-517c265a59ac · outbound

This paper cites Open-World Semi-Supervised Learning.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Open-World Semi-Supervised Learning

Reference 3

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Observation fd44f21a-efc8-4f81-8b87-cd63085a850d · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Emerg- ing properties in self-supervised vision transformers

Reference 4

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Observation 63873446-f6dd-4a6a-92df-e60e3ce5e924 · outbound

This paper cites This looks like that: deep learn- ing for interpretable image recognition.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement This looks like that: deep learn- ing for interpretable image recognition

Reference 5

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Observation c102698a-a28d-447b-aac3-1cf8c1248b03 · outbound

This paper cites Parametric information max- imization for generalized category discovery.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Parametric information max- imization for generalized category discovery

Reference 6

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Observation 53875159-5a4b-4488-9d29-f87030d46fee · outbound

This paper cites Contrastive Mean-Shift Learning for Generalized Category Discovery.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Contrastive Mean-Shift Learning for Generalized Category Discovery

Reference 7

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Observation 77e92a9e-b09f-4e03-a4be-5543ca995491 · outbound

This paper cites Unsupervised part discovery from con- trastive reconstruction.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Unsupervised part discovery from con- trastive reconstruction

Reference 8

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Source-reported events for the cited work

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Observation 30fd7757-5384-452f-85ed-5bc03eadcf58 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Imagenet: A large-scale hierarchical image database

Reference 9

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Source-reported events for the cited work

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Observation 9b5e258a-c78a-4cf6-9246-0e15e8e212a7 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 11

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Source-reported events for the cited work

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Observation cdb60cc4-8f69-40ef-a2b9-1062310d3524 · outbound

This paper cites A unified objective for novel class discovery.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement A unified objective for novel class discovery

Reference 12

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Observation ed497001-540d-4f20-a5ea-5eb616547e7f · outbound

This paper cites Statistical theory of extreme values and some practical applications: a series of lectures.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Statistical theory of extreme values and some practical applications: a series of lectures

Reference 13

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Source-reported events for the cited work

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Observation c3e05716-4a02-4cf0-83d2-d122f6393a8e · outbound

This paper cites Noise-contrastive estimation: A new estimation principle for unnormalized statistical models.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 8fe78958-7eb0-4378-898d-e88b76590dd2 · outbound

This paper cites Automatically Discovering and Learning New Visual Categories with Ranking Statistics.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Automatically Discovering and Learning New Visual Categories with Ranking Statistics

Reference 15

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Observation c9982b62-0272-4c7f-8d1b-be3dc0a65a02 · outbound

This paper cites CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery

Reference 16

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Unavailable: canonical work link unavailable.

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Observation 0bfa6cf9-a151-4da1-a0dd-827587f9fbd8 · outbound

This paper cites Deep residual learning for image recognition.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Deep residual learning for image recognition

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 30d7398d-92f8-4829-82b1-cee19d4a14db · outbound

This paper cites Deep residual learning for image recognition.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Deep residual learning for image recognition

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c59dc59b-796d-4489-b9cd-60907099d67b · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Momentum contrast for unsupervised visual rep- resentation learning

Reference 19

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Observation 737b2d4c-bbe3-4b00-9923-8208523ff8a8 · outbound

This paper cites Interpretable and accurate fine- grained recognition via region grouping.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Interpretable and accurate fine- grained recognition via region grouping

Reference 20

Resolution
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Source-reported events for the cited work

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Observation e01aed2d-f89f-47a3-89cc-20d264a09a4b · outbound

This paper cites Scops: Self-supervised co-part segmentation.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Scops: Self-supervised co-part segmentation

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 77f25c0f-004c-4a50-be9d-b8980b9ac05b · outbound

This paper cites 3d object representations for fine-grained categorization.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement 3d object representations for fine-grained categorization

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 77e0153b-e74b-46a5-bc4f-e3c453eaa63f · outbound

This paper cites Learning multiple layers of features from tiny images.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Learning multiple layers of features from tiny images

Reference 23

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Observation 4d7d7a78-3f61-4520-8743-0eb3749254fb · outbound

This paper cites Panoptic-partformer: Learning a unified model for panoptic part segmentation.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Panoptic-partformer: Learning a unified model for panoptic part segmentation

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation a9baa447-a4ed-49fd-8142-a966f46b4db0 · outbound

This paper cites Microsoft coco: Common objects in context.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Microsoft coco: Common objects in context

Reference 25

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Observation 562930b6-992b-40a9-aa54-8543a11a2141 · outbound

This paper cites A* sam- pling.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement A* sam- pling

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 2d76bf4b-8003-408b-a71f-fdaca7c2326d · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Fine-Grained Visual Classification of Aircraft

Reference 27

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Observation 0e900be5-3986-41a1-b273-715bc885656d · outbound

This paper cites Gmnet: Graph matching network for large scale part semantic segmentation in the wild.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Gmnet: Graph matching network for large scale part semantic segmentation in the wild

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 2be0ff27-5cc2-4379-9d03-6f9d05f329c0 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement DINOv2: Learning Robust Visual Features without Supervision

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation aaa64579-bfb2-4514-accc-3789a1509700 · outbound

This paper cites Dynamic conceptional contrastive learning for generalized category discovery.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Dynamic conceptional contrastive learning for generalized category discovery

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation fe63f47b-fd51-42a0-b190-acd6751718a4 · outbound

This paper cites Learn to categorize or categorize to learn? self-coding for general- ized category discovery.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Learn to categorize or categorize to learn? self-coding for general- ized category discovery

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 5d1d9b61-94b8-494c-a13e-85fc85a70d59 · outbound

This paper cites SelEx: Self-Expertise in Fine-Grained Generalized Category Discovery.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement SelEx: Self-Expertise in Fine-Grained Generalized Category Discovery

Reference 32

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Observation 43a5e1f3-ff1f-4d33-8509-e79e928abeed · outbound

This paper cites Particle: Part discov- ery and contrastive learning for fine-grained recognition.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Particle: Part discov- ery and contrastive learning for fine-grained recognition

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 31466545-8da0-4e98-b1ea-2fd38f75ba7e · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 34

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Unavailable: canonical work link unavailable.

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Observation 1d79bada-3bcd-4c27-9537-708a97c14ce4 · outbound

This paper cites Going denser with open-vocabulary part segmentation.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Going denser with open-vocabulary part segmentation

Reference 35

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raw_fallback, observed 2026-08-06T18:56:52.702252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 97ac57fa-2080-45ab-b7aa-0678c1fa816b · outbound

This paper cites The Herbarium Challenge 2019 Dataset.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement The Herbarium Challenge 2019 Dataset

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f252f563-096c-4c74-98b5-30a83233749b · outbound

This paper cites Con- trastive multiview coding.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Con- trastive multiview coding

Reference 37

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unresolved
no resolver link, observed 2026-08-06T18:56:52.146571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8922cf17-32de-4aff-8989-d21b5026c0bf · outbound

This paper cites Pdisconet: Semantically consistent part discovery for fine-grained recognition.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Pdisconet: Semantically consistent part discovery for fine-grained recognition

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-06T18:56:52.672306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation c191603f-247f-4529-8fff-ae997ce63bed · outbound

This paper cites Attention is all you need.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Attention is all you need

Reference 39

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no resolver link, observed 2026-08-06T18:56:52.159028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 246e0db4-50f2-4ba3-a63e-b3ea9b5d6d41 · outbound

This paper cites Generalized category discovery.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Generalized category discovery

Reference 40

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verified fuzzy
raw_fallback, observed 2026-08-06T18:56:52.645263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 0c805061-4e9f-42f5-b85d-dde15397ea61 · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement The caltech-ucsd birds-200-2011 dataset

Reference 41

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unresolved
no resolver link, observed 2026-08-06T18:56:52.169052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:56:52.169052Z digest=sha256:86a112ed5d143c5d2a95bfd6c8ccae56e3ca5d9cb0ae04ec773aeba897214a64

Observation c7136355-ef4a-4837-80ab-9b6328faeaf4 · outbound

This paper cites SPTNet: An Efficient Alternative Framework for Generalized Category Discovery with Spatial Prompt Tuning.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement SPTNet: An Efficient Alternative Framework for Generalized Category Discovery with Spatial Prompt Tuning

Reference 42

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unresolved
no resolver link, observed 2026-08-06T18:56:52.174141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:56:52.174141Z digest=sha256:cd2e9c7fdd0a2180c8c627095fd6c70e8084b7e4718a127ad061c194c39dbd91

Observation b1252b5c-b4af-489e-8a32-6d785bf0fdb4 · outbound

This paper cites Parametric classification for generalized category discovery: A baseline study.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Parametric classification for generalized category discovery: A baseline study

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:52.616622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T18:56:52.179355Z digest=sha256:7d021e70fa444bb87e62ab80d942e6234ddc76e2a07f4b0d3e8f4474394e8658

Observation f5e62343-b607-4cb8-9006-cd04ee6b28fb · outbound

This paper cites Decompose novel into known: Part concept learning for 3d novel class discovery.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Decompose novel into known: Part concept learning for 3d novel class discovery

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:52.596164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T18:56:52.184992Z digest=sha256:a6169f34f90c112a32073c841c72cb99cc374441f66cdd78a02d5a1f2a849c48

Observation 9915f41f-a4da-46ac-837b-6e63f3ae01ec · outbound

This paper cites Prompt- cal: Contrastive affinity learning via auxiliary prompts for generalized novel category discovery.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Prompt- cal: Contrastive affinity learning via auxiliary prompts for generalized novel category discovery

Reference 45

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verified fuzzy
raw_fallback, observed 2026-08-06T18:56:52.577513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T18:56:52.190728Z digest=sha256:1b0eb0497d9ef4e100854df3fe795daaa0472b19f74bf42689c8ad7b6b75f212

Observation 725b900e-0941-47be-89a5-b0eaab919fcb · outbound

This paper cites Novel visual category discov- ery with dual ranking statistics and mutual knowledge distil- lation.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Novel visual category discov- ery with dual ranking statistics and mutual knowledge distil- lation

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:52.560788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T18:56:52.195721Z digest=sha256:d247a34efc66690c4289b2ad73284ec8e7ebbdf6f1ad094f78f1c436778b4766

Observation 2e2e4b8c-3018-48ac-a88a-6baf72958a19 · outbound

This paper cites Learning semi- supervised gaussian mixture models for generalized category discovery.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Learning semi- supervised gaussian mixture models for generalized category discovery

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:52.541882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T18:56:52.205462Z digest=sha256:9705bef57ab2e78916155c43f8186c76cebb05b18ee63bffde6fb4df6f7a451d

Observation 42d48d2e-5e54-4641-8bcb-22b41d28465d · outbound

This paper cites Multi- class part parsing with joint boundary-semantic awareness.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Multi- class part parsing with joint boundary-semantic awareness

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:52.523440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T18:56:52.210342Z digest=sha256:3ef17445d414e3a1c6f65c2332e8c78407912d729967a04079aab7d50b436290

Observation 53369550-a56a-4894-a863-4f9e1f184ef3 · outbound

This paper cites Learn- ing multi-attention convolutional neural network for fine- grained image recognition.

Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement Learn- ing multi-attention convolutional neural network for fine- grained image recognition

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:52.503870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T18:56:52.215373Z digest=sha256:ee35c8e00eff169f40ed3eea6f7c6dafb30e7fdf47cacfd66b342008a10f7ed2

Pith citing papers

No inbound Pith citation observations are available.