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Paper Citation Record · LEDGER

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors

As of 7 August 2026, this Paper Citation Record lists 88 of 88 outbound references and 1 inbound Pith citation observation for arXiv:2505.24103.

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

pith.paper-citation-record.v1
2505.24103 v1

Coverage vector

measured 88 of 88 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:40:09.004854Z

measured 89 of 89 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T01:18:51.054590Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

88 of 88 outbound references displayed

  • verified exact4
  • verified fuzzy55
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a7c0e218-5f93-4024-b100-9e36c8c38a54 · outbound

This paper cites Affordances from human videos as a versatile representation for robotics.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Affordances from human videos as a versatile representation for robotics

Reference 1

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no resolver link, observed 2026-08-07T12:39:58.751957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:39:58.751957Z digest=sha256:c103de252e5fe74c02753a32eec7010a7d7b965d90ad490ab462dd938f95989a

Observation 4c29c85b-4ec6-40d7-881c-504cbe857ef6 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 2

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no resolver link, observed 2026-08-07T12:39:58.865435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:39:58.865435Z digest=sha256:3379e20765a306c084f41251a46b904a5ce1265e742fad911574fccb7476aa9e

Observation 54c71364-bed4-4665-a754-b55031e7a989 · outbound

This paper cites Do as i can, not as i say: Grounding language in robotic affordances.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Do as i can, not as i say: Grounding language in robotic affordances

Reference 3

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unresolved
no resolver link, observed 2026-08-07T12:39:58.927352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:39:58.927352Z digest=sha256:1bfccd19807d6b3e085c4a8e68c7a7341e59b9e409ec4ab2a9f9d83fd83d6205

Observation 41fd3939-d19c-43e9-be94-f447893a45a9 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Emerging properties in self-supervised vision transformers

Reference 4

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unresolved
no resolver link, observed 2026-08-07T12:39:59.014076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:39:59.014076Z digest=sha256:bb66f62952988da161a46079c4562eeb627fd3ba756b6cf84c3136a9ebcc6d5e

Observation 3a4e260f-7f9b-4e77-95c4-635d41025f14 · outbound

This paper cites Are standard Object Segmentation models sufficient for Learning Affordance Segmentation?.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Are standard Object Segmentation models sufficient for Learning Affordance Segmentation?

Reference 5

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unresolved
no resolver link, observed 2026-08-07T12:39:59.083244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:39:59.083244Z digest=sha256:2e3b79eae6d57eaa80428e8590a62af36e847a17a9c0408a109278f4fa774d7d

Observation 6fa9e365-406c-408b-bc14-b258653782af · outbound

This paper cites WorldAfford: Affordance Grounding based on Natural Language Instructions.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors WorldAfford: Affordance Grounding based on Natural Language Instructions

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:40:10.569183Z

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.

source=arxiv_source observed=2026-08-07T12:39:59.233447Z digest=sha256:e49ca1a8522e166d5ccd2f93816768be664e0f19410eedb06b0facd3fc0ba181

Observation 24883ee1-9f99-441a-a942-953bea94d82a · outbound

This paper cites Affordance grounding from demonstration video to target image.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Affordance grounding from demonstration video to target image

Reference 7

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no resolver link, observed 2026-08-07T12:39:59.335010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:39:59.335010Z digest=sha256:1388ec098d7f36e73c9aba964aae9150447a0678e0905f6fc82e111b29d864aa

Observation 6414bd5c-157a-4aca-b147-8fc5ff48cd5e · outbound

This paper cites MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning

Reference 8

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no resolver link, observed 2026-08-07T12:39:59.467864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:39:59.467864Z digest=sha256:787c8ced6c9d2c014bd748f706bbf88e5c09c9168faf00d8840d5120fa617cb6

Observation 72fdbc11-503d-4e99-a2fb-fbeaa8f92927 · outbound

This paper cites Towards label-free scene understanding by vision foundation models.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Towards label-free scene understanding by vision foundation models

Reference 9

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no resolver link, observed 2026-08-07T12:39:59.631612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:39:59.631612Z digest=sha256:513b58e219a937f92ace20abcd54d66317d9ce709ce5494f54a05392017aee3b

Observation e54fe254-4dc3-4ef9-801f-c72e40727155 · outbound

This paper cites Segment anything model ( SAM ) enhances pseudo-labels for weakly supervised semantic segmentation.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Segment anything model ( SAM ) enhances pseudo-labels for weakly supervised semantic segmentation

Reference 10

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no resolver link, observed 2026-08-07T12:39:59.774660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:39:59.774660Z digest=sha256:f613dfb39e67e437276fa74640c4d9e830818f7ec4aec4978115dcb5f24b682d

Observation a0b6eccd-e6fb-4e07-8be6-6e4a9d5570bf · outbound

This paper cites Sam-adapter: Adapting segment anything in underperformed scenes.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Sam-adapter: Adapting segment anything in underperformed scenes

Reference 11

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no resolver link, observed 2026-08-07T12:39:59.860803Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:39:59.860803Z digest=sha256:55abbf95daa3eb4ab65fcaa35186c7b6306414f2029088f78033eeb3e5e52d01

Observation 1d3041a6-9c35-4cc2-96de-215e6774e9a8 · outbound

This paper cites Context autoencoder for self-supervised representation learning.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Context autoencoder for self-supervised representation learning

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T12:39:59.949393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:39:59.949393Z digest=sha256:7d103c66001f927b003aaee3cb539eadc4aa4f22b8e3584e08b95a5c2021550b

Observation 3863307e-92aa-4d6b-9f1b-5b2fe0314fe9 · outbound

This paper cites Cerberus transformer: Joint semantic, affordance and attribute parsing.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Cerberus transformer: Joint semantic, affordance and attribute parsing

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:23.706501Z

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.

source=arxiv_source observed=2026-08-07T12:40:00.031978Z digest=sha256:88a20de62121b998a6e316d1a8f3a31bc3bb610e841a896b78a467787e3673af

Observation 84c2303c-7ce2-4d1d-848b-88f6a7d4c08a · outbound

This paper cites Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation Models.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation Models

Reference 14

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:40:10.336014Z

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.

source=arxiv_source observed=2026-08-07T12:40:00.147567Z digest=sha256:b35d298aa11ce80a84a71bc8f1e674430288925290a15a01128684bc3db3a5f5

Observation 85e1f16d-ccc8-4389-9290-9e3f0f7483a8 · outbound

This paper cites Ganhand: Predicting human grasp affordances in multi-object scenes.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Ganhand: Predicting human grasp affordances in multi-object scenes

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-07T12:40:23.446623Z

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.

source=arxiv_source observed=2026-08-07T12:40:00.226797Z digest=sha256:dfb544aacf74bffce8bb9832de543dcad72bd821a9a4a7c291ed32cb6cd0d01a

Observation 014daa32-368d-4324-83a7-fdfd1a58a99c · outbound

This paper cites What does clip know about peeling a banana? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 2238--2247, 2024.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors What does clip know about peeling a banana? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 2238--2247, 2024

Reference 16

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verified fuzzy
raw_fallback, observed 2026-08-07T12:40:23.283165Z

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.

source=arxiv_source observed=2026-08-07T12:40:00.284340Z digest=sha256:52f9052f0c50167bc43e64383f3d7bdadbbf170328acf8586cf84e2af102e970

Observation 35256fd2-b3fe-436a-a01b-e0aadd21891d · outbound

This paper cites Scenefun3d: Fine-grained functionality and affordance understanding in 3d scenes.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Scenefun3d: Fine-grained functionality and affordance understanding in 3d scenes

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:23.069678Z

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.

source=arxiv_source observed=2026-08-07T12:40:00.392195Z digest=sha256:63a7e31e717fce7e303c50fe6825f44571ca0b2d4f4c714a6fad0a00a90bc15c

Observation 96426b1f-ecf8-49c7-9ab3-897d4b560f7a · outbound

This paper cites 3d affordancenet: A benchmark for visual object affordance understanding.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors 3d affordancenet: A benchmark for visual object affordance understanding

Reference 18

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verified fuzzy
raw_fallback, observed 2026-08-07T12:40:22.799870Z

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.

source=arxiv_source observed=2026-08-07T12:40:00.479067Z digest=sha256:cf0838926ad78a6eab9fda200b2de510eac3ba1332292d7299bf9b505f7c0078

Observation 34629281-66a0-4bac-9192-c4b77af02d36 · outbound

This paper cites Affordancenet: An end-to-end deep learning approach for object affordance detection.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Affordancenet: An end-to-end deep learning approach for object affordance detection

Reference 19

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metadata mismatch
raw_fallback, observed 2026-08-07T12:40:10.111782Z

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.

source=arxiv_source observed=2026-08-07T12:40:00.568734Z digest=sha256:7c9c0241a5d5e7d6d010dd60b019a09792180dc3fef97064c61e5917533d8397

Observation 20668186-3c18-4d5f-b68e-0239ea2171e0 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors An image is worth 16x16 words: Transformers for image recognition at scale

Reference 20

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no resolver link, observed 2026-08-07T12:40:00.664053Z

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source=arxiv_source observed=2026-08-07T12:40:00.664053Z digest=sha256:bed2d4d8bbc54b49617fb9c8e0d9569013cd37c4bfd575df1a06c1aefd714cf6

Observation 2275f72b-5d30-4ba1-a202-35cd24ea3d78 · outbound

This paper cites Demo2vec: Reasoning object affordances from online videos.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Demo2vec: Reasoning object affordances from online videos

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:22.564203Z

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.

source=arxiv_source observed=2026-08-07T12:40:00.751189Z digest=sha256:38ae39f01d44e85ac5a7119e44cf275792b084e49034d42b241f0e85aa2d8e61

Observation 891d7902-be97-4d52-9214-6f796a72324c · outbound

This paper cites The theory of affordances.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors The theory of affordances

Reference 22

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verified fuzzy
raw_fallback, observed 2026-08-07T12:40:22.403653Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:40:00.851532Z digest=sha256:58f951369975a79f6d968db2e0c1048d8b7e6bd2b8d6a7ed98b82d5277481fdb

Observation 26496ca4-bbb0-44e8-a990-25ada9327275 · outbound

This paper cites Handal: A dataset of real-world manipulable object categories with pose annotations, affordances, and reconstructions.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Handal: A dataset of real-world manipulable object categories with pose annotations, affordances, and reconstructions

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:22.273639Z

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.

source=arxiv_source observed=2026-08-07T12:40:00.934593Z digest=sha256:e060e633dd6fb0c8b7628d3fd6fda0c40d7a2045affce3ddb8706d7c5101ba4b

Observation 42bfeff1-c686-4451-b5d6-c398199ec13e · outbound

This paper cites One-shot transfer of affordance regions? affcorrs! In Conference on Robot Learning, pp.\ 550--560.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors One-shot transfer of affordance regions? affcorrs! In Conference on Robot Learning, pp.\ 550--560

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:22.174194Z

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.

source=arxiv_source observed=2026-08-07T12:40:00.999702Z digest=sha256:781f1bce81d38fd88f7c5a783461d6d366d76f053481496a17a02887154aa44f

Observation cd1d26f9-aff3-46ea-a960-eb96265de086 · outbound

This paper cites ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models

Reference 25

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no resolver link, observed 2026-08-07T12:40:01.059085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:01.059085Z digest=sha256:435d94df466d97f1f9cb587dedb106f65570a1a1c36a61a5df37c9026d5ea519

Observation 5ef4275e-0ee5-4b8e-812e-b84f14bc72ca · outbound

This paper cites Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation

Reference 26

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unresolved
no resolver link, observed 2026-08-07T12:40:01.149654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:01.149654Z digest=sha256:586ec5042234d394897ba885c6174b5a325ce9dc4b2b69b18422d1b5ca4559e1

Observation bbd10119-79f9-4772-a7fa-5764a56d988e · outbound

This paper cites Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation

Reference 27

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unresolved
no resolver link, observed 2026-08-07T12:40:01.282620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:01.282620Z digest=sha256:3d9623c53056c2836a69e040894e76c04697c7b644512da69f79e0681359a9e5

Observation 2ebdb078-0f37-4770-be77-a0087f0b8fbd · outbound

This paper cites Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models

Reference 28

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:40:09.681582Z

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.

source=arxiv_source observed=2026-08-07T12:40:01.398231Z digest=sha256:292c5b838d13ad69149746a5e6c7694bca4d77e76b85a7e377281700f0805829

Observation bee773b5-ddf8-41f9-9f2a-77dc0b074318 · outbound

This paper cites Segment anything.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Segment anything

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T12:40:01.520365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:01.520365Z digest=sha256:e1f936496163af2e5a8fbdf236750ef8615ba081ad54adf0bce790c962ff6404

Observation 6f587f35-1a1f-40f0-b364-370274f4d470 · outbound

This paper cites From sam to cams: Exploring segment anything model for weakly supervised semantic segmentation.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors From sam to cams: Exploring segment anything model for weakly supervised semantic segmentation

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:21.984852Z

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.

source=arxiv_source observed=2026-08-07T12:40:01.653919Z digest=sha256:173e7ae507efddbdd095e01ada3392e7cf27d611fc24d68107e30092334a3fc1

Observation cfad5b98-478a-4db1-9fe9-83dd3100fd99 · outbound

This paper cites Lisa: Reasoning segmentation via large language model.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Lisa: Reasoning segmentation via large language model

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T12:40:01.769181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:01.769181Z digest=sha256:7e313f657d436aef89d72947a53ae2580a34ea886bbee247730ec87f25337ff6

Observation 6788e2c4-fbf9-4d46-b19a-bd33596402db · outbound

This paper cites Locate: Localize and transfer object parts for weakly supervised affordance grounding.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Locate: Localize and transfer object parts for weakly supervised affordance grounding

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:21.693558Z

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.

source=arxiv_source observed=2026-08-07T12:40:01.896640Z digest=sha256:286cde8cdd2b78c8cf09bbd4764c757dddaceec4ea995e284f8569244545953f

Observation 4eb7bd67-f1e4-467e-b49b-33ac16ac3341 · outbound

This paper cites One-shot open affordance learning with foundation models.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors One-shot open affordance learning with foundation models

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:21.469335Z

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.

source=arxiv_source observed=2026-08-07T12:40:02.025492Z digest=sha256:f22938a88e1d2e9d7c44b11825fa1500026ed5d5dab7e893e5c1cd0549e99aef

Observation 710ccacd-23d6-4b56-ab73-d5a348de753e · outbound

This paper cites Partglee: A foundation model for recognizing and parsing any objects.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Partglee: A foundation model for recognizing and parsing any objects

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:21.323706Z

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.

source=arxiv_source observed=2026-08-07T12:40:02.169571Z digest=sha256:359c53a714409974cae8c9122d12ff7012684e3a48c7fc3d9c9134c9f58a0357

Observation 7595b354-9cba-4dce-bb15-98a471e17157 · outbound

This paper cites Manipllm: Embodied multimodal large language model for object-centric robotic manipulation.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Manipllm: Embodied multimodal large language model for object-centric robotic manipulation

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:21.160617Z

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.

source=arxiv_source observed=2026-08-07T12:40:02.290883Z digest=sha256:a27cd6695cc4f08c8f18a24fd4e5b5399d0b1b4b60d70d04faf9815cf1d68fd9

Observation 3d023b14-a056-44e1-a44d-fcd0a555bd62 · outbound

This paper cites Maal: Multimodality-aware autoencoder-based affordance learning for 3d articulated objects.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Maal: Multimodality-aware autoencoder-based affordance learning for 3d articulated objects

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:21.039420Z

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.

source=arxiv_source observed=2026-08-07T12:40:02.414594Z digest=sha256:067fc25fd6d950c16451f8279f0dacb4670ea52e86c9ba0d7de04f190bf9bac0

Observation 70ac3370-cbd8-40bc-a327-d05685327f37 · outbound

This paper cites Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:20.851215Z

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.

source=arxiv_source observed=2026-08-07T12:40:02.564935Z digest=sha256:aed6ec5f6b4d6c3155318171ea329d5d66a49e6d3568c125b3679b1fb535f547

Observation 422c9c17-756c-489e-8db6-6e31fb7489c4 · outbound

This paper cites Visual instruction tuning.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Visual instruction tuning

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T12:40:02.689102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:02.689102Z digest=sha256:48e733342ad5d96f2ae75efe520992670a98a908bf80b4fdcd5ca9a28213ee0d

Observation fd55472f-097a-4c58-94ba-c24f50dc98e1 · outbound

This paper cites Joint hand motion and interaction hotspots prediction from egocentric videos.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Joint hand motion and interaction hotspots prediction from egocentric videos

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:20.615209Z

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.

source=arxiv_source observed=2026-08-07T12:40:02.797107Z digest=sha256:1bb992cc0f620c5057b4f32008741b699c0068816250b3d0e0c91c5b24fddd89

Observation 3dcd412a-23ef-4d4a-8e61-a7f796194e5c · outbound

This paper cites Learning to segment affordances.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Learning to segment affordances

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:20.433845Z

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.

source=arxiv_source observed=2026-08-07T12:40:02.907181Z digest=sha256:1c3dd0528d93a83094f1302f8e85c243dca9fe9f33a33d0da3fe237123f751fe

Observation 179f91b4-e7ef-4bf7-8a35-274e93bd0b06 · outbound

This paper cites One-shot affordance detection.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors One-shot affordance detection

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:20.153480Z

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.

source=arxiv_source observed=2026-08-07T12:40:03.044967Z digest=sha256:c1efa6ab6e2fa3fea1a57be9d04c498f7b93afaec026ecc99ccb759047b0bce2

Observation 47b32159-1bbe-4797-a01d-ea49d0674d6a · outbound

This paper cites Learning affordance grounding from exocentric images.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Learning affordance grounding from exocentric images

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:19.884599Z

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.

source=arxiv_source observed=2026-08-07T12:40:03.196660Z digest=sha256:562cab2321bc2e4f3b6ad5d57888b94729c3a6ffe72cd3b7eb28cfa7a826d937

Observation 7f743081-263c-4741-8e08-add1bef3a736 · outbound

This paper cites Grounded affordance from exocentric view.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Grounded affordance from exocentric view

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:19.653175Z

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.

source=arxiv_source observed=2026-08-07T12:40:03.389275Z digest=sha256:42fd86937e0ef9e158d1ceb02f37f9d679b57fdad4bae0478f879c9614a437dc

Observation e30c6ca5-5711-4896-a569-cd8edd72eb0c · outbound

This paper cites Leverage interactive affinity for affordance learning.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Leverage interactive affinity for affordance learning

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:19.405025Z

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.

source=arxiv_source observed=2026-08-07T12:40:03.601284Z digest=sha256:0eb56149fe2f1aba969eda16a80982dbe6a95ef73a764dd40bad0192e1fd3a5f

Observation ee4802ce-660e-43a6-b184-8dec988862f6 · outbound

This paper cites Local Occupancy-Enhanced Object Grasping with Multiple Triplanar Projection.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Local Occupancy-Enhanced Object Grasping with Multiple Triplanar Projection

Reference 45

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:40:09.387770Z

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.

source=arxiv_source observed=2026-08-07T12:40:03.772591Z digest=sha256:fd07f801609145f1c4ab719749c59b06384379e97ac65ecd16e5a5615f609a2f

Observation dfe8f53f-6ffb-41da-a6ff-0028d949ec8f · outbound

This paper cites Simple open-vocabulary object detection.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Simple open-vocabulary object detection

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:19.122979Z

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.

source=arxiv_source observed=2026-08-07T12:40:03.947918Z digest=sha256:9a22efe2178d9de1ec6a63f17310c3c2cf9a83f97a70b80a63cd78834c89765c

Observation 410d51cb-d2fc-4136-82f8-ffa3402add5c · outbound

This paper cites Where2act: From pixels to actions for articulated 3d objects.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Where2act: From pixels to actions for articulated 3d objects

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:18.848923Z

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.

source=arxiv_source observed=2026-08-07T12:40:04.111848Z digest=sha256:c8f599e47978dba9595327fe34959dec70b2a4d4105d8ebc155e6b33ad552eb5

Observation fef166e7-211a-449d-bce0-1044ee782ead · outbound

This paper cites Bayesian deep learning for affordance segmentation in images.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Bayesian deep learning for affordance segmentation in images

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:18.598219Z

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.

source=arxiv_source observed=2026-08-07T12:40:04.196833Z digest=sha256:f0b12bb933d223b790320e096a01edaa24bc44d4a201a75da4a6a2fee43330ad

Observation 41792084-920b-415b-b4f0-e44a0afc7deb · outbound

This paper cites Affordance detection of tool parts from geometric features.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Affordance detection of tool parts from geometric features

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:18.356687Z

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.

source=arxiv_source observed=2026-08-07T12:40:04.333697Z digest=sha256:d40e27061585f68dc1a8842d97cf286591c0cf7f8b97eb5ddd66a8d268ec99a1

Observation 78b738a6-9f69-4a9b-a841-e2674adec648 · outbound

This paper cites Learning affordance landscapes for interaction exploration in 3d environments.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Learning affordance landscapes for interaction exploration in 3d environments

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:18.102947Z

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.

source=arxiv_source observed=2026-08-07T12:40:04.420599Z digest=sha256:0a79da35f2a51b3143636e1265d76d40407de9e6c819f3a07c5708f410244e4f

Observation e7eca54f-1673-443c-91bc-fe3077749b7e · outbound

This paper cites Grounded human-object interaction hotspots from video.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Grounded human-object interaction hotspots from video

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:17.865001Z

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.

source=arxiv_source observed=2026-08-07T12:40:04.496528Z digest=sha256:750bafd749142787140fee43a8dc072cd0606950c3e8a61a54aefd36a9569784

Observation b8b06b52-4789-4fac-b86d-7e5cc80fee9b · outbound

This paper cites Detecting object affordances with convolutional neural networks.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Detecting object affordances with convolutional neural networks

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:17.583878Z

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.

source=arxiv_source observed=2026-08-07T12:40:04.578790Z digest=sha256:6dfaab1a2c17e9e49dd5eea2456c423ae15379383d3576c6ffc0aad76d550617

Observation ef0eaf73-aa9e-475b-a22c-25f4100878a4 · outbound

This paper cites Object-based affordances detection with convolutional neural networks and dense conditional random fields.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Object-based affordances detection with convolutional neural networks and dense conditional random fields

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:17.307366Z

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.

source=arxiv_source observed=2026-08-07T12:40:04.696076Z digest=sha256:b3d35d4c8cf77c7956269d47cb6e6ea06e4edb92d276ec2d9f9ff88785c8080c

Observation a67d3041-9dbf-4c3a-b49e-b32bec102184 · outbound

This paper cites Open-vocabulary affordance detection in 3d point clouds.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Open-vocabulary affordance detection in 3d point clouds

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:17.038694Z

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.

source=arxiv_source observed=2026-08-07T12:40:04.776817Z digest=sha256:8b3c86c2a92feb435b4c35901b1d945e6104824e57de262e499c549bf9523aa0

Observation 153f1044-25ca-4da6-b736-13159a9c4361 · outbound

This paper cites an unresolved cited work.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Unresolved cited work

Reference 55

Resolution
unresolved
raw_fallback, observed 2026-08-07T12:40:16.754245Z

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.

source=arxiv_source observed=2026-08-07T12:40:04.880320Z digest=sha256:5d7e0b28e2df326b752a9c22d4579031b95152e9177df687b4dd0ce8a05153c9

Observation ba07822e-9f84-455a-9310-8b708404ffd6 · outbound

This paper cites Peters, Asha Iyer, Laurent Itti, and Christof Koch.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Peters, Asha Iyer, Laurent Itti, and Christof Koch

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T12:40:04.986105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:04.986105Z digest=sha256:7bcc049fc4c461370341e5cfa11467a6c126d24291006a55751599508e342cd1

Observation 72d2fd7b-7e4e-41ea-8b63-5a99974288df · outbound

This paper cites Understanding 3d object interaction from a single image.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Understanding 3d object interaction from a single image

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:16.476731Z

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.

source=arxiv_source observed=2026-08-07T12:40:05.086090Z digest=sha256:77f0417966e7b965aa56e709d0a660c1b0207f9367fd8e4ba9076e1ae13fd042

Observation bd64bdad-c273-4e2d-a93c-8b13d6b5c627 · outbound

This paper cites Affordancellm: Grounding affordance from vision language models.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Affordancellm: Grounding affordance from vision language models

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:16.243221Z

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.

source=arxiv_source observed=2026-08-07T12:40:05.200131Z digest=sha256:a74b106504da7025a6f498bafb03cddd5226a8c70f57117b488532273b45138a

Observation 46c697ac-8c9d-45bd-ba32-d5fcf1b8d320 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Learning transferable visual models from natural language supervision

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-07T12:40:05.283153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:05.283153Z digest=sha256:2dc68b72f9489780b7c44a197430fc3cbb9e12e8ca5d17df9692bdb27ec5f940

Observation c43c8413-11b9-412c-bf53-3fd26e7d4cb9 · outbound

This paper cites Strategies to leverage foundational model knowledge in object affordance grounding.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Strategies to leverage foundational model knowledge in object affordance grounding

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:15.972718Z

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.

source=arxiv_source observed=2026-08-07T12:40:05.356266Z digest=sha256:118d714c33ed48079fb06ecd833a695fce64d93d9f2b49c049f629ef29fb97ac

Observation 80606f6a-34d0-4a11-a1d8-6c38acf80cf7 · outbound

This paper cites Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-07T12:40:05.432045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:05.432045Z digest=sha256:bd464f9f5bb606876a5249045f8cb071fb3b48d8c921948dd649dc25643a1b02

Observation 90b34370-8fca-4182-a258-359bc75e2302 · outbound

This paper cites A multi-scale cnn for affordance segmentation in rgb images.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors A multi-scale cnn for affordance segmentation in rgb images

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:15.823081Z

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.

source=arxiv_source observed=2026-08-07T12:40:05.498202Z digest=sha256:e1f1a70ce14ab0f5354feebdf873a4268e353b2ec6b5716f37cba5b41d2b1d93

Observation 47684d9e-1263-468c-889a-90e5116a8710 · outbound

This paper cites Weakly supervised affordance detection.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Weakly supervised affordance detection

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:15.677731Z

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.

source=arxiv_source observed=2026-08-07T12:40:05.567308Z digest=sha256:b3b45a27244c13197520e3e06b43f24da7dc0c73aff03e05423355ef979bc4bc

Observation eefa128f-e20e-47dc-9e0c-d0789c2b9aa6 · outbound

This paper cites Hierarchical transformer for visual affordance understanding using a large-scale dataset.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Hierarchical transformer for visual affordance understanding using a large-scale dataset

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:15.509801Z

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.

source=arxiv_source observed=2026-08-07T12:40:05.686445Z digest=sha256:7c5d86782069b0d1f020474848938a11c447dc5cca2b280a4068771e5346cc79

Observation 13fee644-6435-4a36-a952-7c3790740b89 · outbound

This paper cites Grounded segment anything: From objects to parts.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Grounded segment anything: From objects to parts

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:15.242780Z

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.

source=arxiv_source observed=2026-08-07T12:40:05.836706Z digest=sha256:cd84a8780a97041ef1f6e77f6f7654a4c147c26794e383c39ca92930d54738eb

Observation 8ecc65b7-1ec6-430d-84bc-a2b175a79daa · outbound

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

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Going denser with open-vocabulary part segmentation

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:14.937789Z

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.

source=arxiv_source observed=2026-08-07T12:40:05.989970Z digest=sha256:6dc2111da0d734a4b09d86f5854de38a0bbb4f98141fd6667dd66ea0e175a65a

Observation e2c32a8f-34cd-4489-84d4-6d462c0e2b69 · outbound

This paper cites An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problems.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problems

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-07T12:40:06.101120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:06.101120Z digest=sha256:532450188df7b9515aea459f6cf9a4af1295f3f2091cd6f8d0b995b8aa37ac60

Observation 6ffcee7b-f2b3-48fe-8729-82723016a37a · outbound

This paper cites Oval-prompt: Open-vocabulary affordance localization for robot manipulation through llm affordance-grounding.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Oval-prompt: Open-vocabulary affordance localization for robot manipulation through llm affordance-grounding

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:14.646354Z

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.

source=arxiv_source observed=2026-08-07T12:40:06.196272Z digest=sha256:e39e3b092beedf792d5e3bfdfa2cf59efb0f9bca36ac7be47151d9199f76efd9

Observation 008849a0-a536-481a-8b84-c9d725639604 · outbound

This paper cites An interactive navigation method with effect-oriented affordance.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors An interactive navigation method with effect-oriented affordance

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:14.298271Z

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.

source=arxiv_source observed=2026-08-07T12:40:06.324539Z digest=sha256:d2f0f7086eb72c1ebb89cecf2d53b0f33abab1d8a5361015253c653d7b9e8292

Observation e3270b88-69b1-49f4-9fe4-13da1d328f37 · outbound

This paper cites Adaafford: Learning to adapt manipulation affordance for 3d articulated objects via few-shot interactions.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Adaafford: Learning to adapt manipulation affordance for 3d articulated objects via few-shot interactions

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:14.079223Z

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.

source=arxiv_source observed=2026-08-07T12:40:06.482449Z digest=sha256:f58b60414f5de2efb88c07b3a781ebbe270cc58e800fb051f29a13338105cfe6

Observation cb41983f-b02d-47c2-8307-4939a7c1cc2c · outbound

This paper cites Move as you say interact as you can: Language-guided human motion generation with scene affordance.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Move as you say interact as you can: Language-guided human motion generation with scene affordance

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:13.807872Z

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.

source=arxiv_source observed=2026-08-07T12:40:06.646983Z digest=sha256:5a41e30922efce4d85e63eb13fe1bf3062905c9b13b6568256bcba7a3efb49d6

Observation e809f454-e1f8-4476-8119-72b6288edc13 · outbound

This paper cites VAT -mart: Learning visual action trajectory proposals for manipulating 3d ART iculated objects.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors VAT -mart: Learning visual action trajectory proposals for manipulating 3d ART iculated objects

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:13.549855Z

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.

source=arxiv_source observed=2026-08-07T12:40:06.758945Z digest=sha256:b3b532cc24d50f42d5179881274418c85eb1cd30ced1dda219584d7ea4d78d2b

Observation edccc4b3-5af1-43f9-9d00-66bf27b5feaf · outbound

This paper cites Clims: Cross language image matching for weakly supervised semantic segmentation.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Clims: Cross language image matching for weakly supervised semantic segmentation

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:13.264343Z

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.

source=arxiv_source observed=2026-08-07T12:40:06.881378Z digest=sha256:b1b89c69f70aeaff450c86258b305831949fd3f18dcea14ea03f164888377f99

Observation c55eeef7-738e-4b25-a534-933d4125581e · outbound

This paper cites Learning multi-modal class-specific tokens for weakly supervised dense object localization.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Learning multi-modal class-specific tokens for weakly supervised dense object localization

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:12.989791Z

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.

source=arxiv_source observed=2026-08-07T12:40:07.002160Z digest=sha256:a2d54499366f0a769a1bb4b3d8249e254b0a42d6a899cb6c2722ae5025063bcd

Observation e24ed98d-4b73-4cdc-9e3b-af221917dbe3 · outbound

This paper cites Weakly supervised multimodal affordance grounding for egocentric images.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Weakly supervised multimodal affordance grounding for egocentric images

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:12.704871Z

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.

source=arxiv_source observed=2026-08-07T12:40:07.141612Z digest=sha256:07a6af93b3bd8b1bfed95df5dd9b389167f78862415c0ad80dc41423405c5b6c

Observation 7bc74989-6e5a-4682-8522-7325a6014e3f · outbound

This paper cites Foundation model assisted weakly supervised semantic segmentation.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Foundation model assisted weakly supervised semantic segmentation

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:12.519261Z

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.

source=arxiv_source observed=2026-08-07T12:40:07.316983Z digest=sha256:fdf23b6a94106d54999adfc1fab036a8f73b6fd1ddb99919b8e98b724de5cf3d

Observation 986bc182-1234-4848-8d35-a93d5ad68da6 · outbound

This paper cites Grounding 3d object affordance from 2d interactions in images.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Grounding 3d object affordance from 2d interactions in images

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:12.328056Z

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.

source=arxiv_source observed=2026-08-07T12:40:07.437998Z digest=sha256:71505aa41002b03c4d116864e208d54b81cb4675aa61835f6ace8e6b2c9f8072

Observation 71b4c90c-7283-4cf6-b1e8-5786607a8430 · outbound

This paper cites Lemon: Learning 3d human-object interaction relation from 2d images.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Lemon: Learning 3d human-object interaction relation from 2d images

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:12.079748Z

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.

source=arxiv_source observed=2026-08-07T12:40:07.578038Z digest=sha256:b464afd9bbf062fa549850beb24189005d500d9f5c0394bb1cebb5db6f5f2fc4

Observation 98e6064a-e0bc-47ab-8918-28534bca74ff · outbound

This paper cites Affordance diffusion: Synthesizing hand-object interactions.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Affordance diffusion: Synthesizing hand-object interactions

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:11.855685Z

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.

source=arxiv_source observed=2026-08-07T12:40:07.714694Z digest=sha256:855732a1004c81ab0d12f7ed37cd66ae31d3359dbdf11cbae891d548923da99e

Observation 0b7423ad-eb73-4b59-b64b-988715b42e59 · outbound

This paper cites Fine-grained affordance annotation for egocentric hand-object interaction videos.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Fine-grained affordance annotation for egocentric hand-object interaction videos

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:11.591902Z

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.

source=arxiv_source observed=2026-08-07T12:40:07.862097Z digest=sha256:a0999ebab4ba02ebadceab5df5911945738a0a5bed8834808a7b0bac553d833b

Observation 04e330a6-2d92-4063-a4a8-de379f6edd49 · outbound

This paper cites Frozen clip: A strong backbone for weakly supervised semantic segmentation.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Frozen clip: A strong backbone for weakly supervised semantic segmentation

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:11.373593Z

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.

source=arxiv_source observed=2026-08-07T12:40:07.993561Z digest=sha256:09144f3e0a51c7900b9b6ff71414636af7a6f66df328fc8f5ca2828bab88a251

Observation 0c94f27f-6d38-4b28-b4cb-53447e6f1983 · outbound

This paper cites Self-Explainable Affordance Learning with Embodied Caption.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Self-Explainable Affordance Learning with Embodied Caption

Reference 82

Resolution
unresolved
no resolver link, observed 2026-08-07T12:40:08.111870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:08.111870Z digest=sha256:dfb0f26ad96152a3a838f2197718c68fa3ac7547dba6edc0970d684f7cb3734e

Observation d2358a2d-4b08-4aaf-8c97-ea814a00849f · outbound

This paper cites Fast Segment Anything.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Fast Segment Anything

Reference 83

Resolution
unresolved
no resolver link, observed 2026-08-07T12:40:08.272223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:08.272223Z digest=sha256:2f140e49538642244d15895631d14be5ede5c1e6033f042162c1df83af0e3a28

Observation 9c949da0-6c61-4c7c-9974-9d2e1c31afbf · outbound

This paper cites Learning deep features for discriminative localization.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Learning deep features for discriminative localization

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:11.092086Z

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.

source=arxiv_source observed=2026-08-07T12:40:08.445426Z digest=sha256:8a21e2e238c5295a306aed088cb4ab92a8e481017f89dd531774d3988309f29a

Observation 8e50204c-b588-4d14-9aa5-8d9d2964e33b · outbound

This paper cites write newline.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors write newline

Reference 85

Resolution
unresolved
no resolver link, observed 2026-08-07T12:40:08.592657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:08.592657Z digest=sha256:db059230f21f2cdf7321d4e7c57d54d58c0851ccf899368ca3744983e35da1b8

Observation 29648570-a647-4f53-870f-7fde5ef1d85f · outbound

This paper cites @esa (Ref.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors @esa (Ref

Reference 86

Resolution
unresolved
no resolver link, observed 2026-08-07T12:40:08.714425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:08.714425Z digest=sha256:36094f984cf806001dc0225fd142fe1200a21ed4958b1cc20ed216fa89f939dd

Observation e73de448-65a6-479e-855e-4d46cc9421d7 · outbound

This paper cites an unresolved cited work.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors Unresolved cited work

Reference 87

Resolution
unresolved
no resolver link, observed 2026-08-07T12:40:08.887600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:08.887600Z digest=sha256:ca4afddc8734dce9630f40ec1d49d63d20f2f7b64bb553da7de3d56a02aacf7c

Observation f7af00ab-6d90-4f37-8ee9-9dba93b48877 · outbound

This paper cites 2yJ+q/0 Q(hAz lC6B wo^ n;e=ad E L D!ԝV.

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors 2yJ+q/0 Q(hAz lC6B wo^ n;e=ad E L D!ԝV

Reference 88

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:40:10.818633Z

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.

source=arxiv_source observed=2026-08-07T12:40:09.004854Z digest=sha256:2ac15c366d559aef4b06f90a9af9615df2a92bd32656e17ed3f77a83b0115845

Pith citing papers

Observation 6eca28c4-65c4-413e-b286-32f7d3bc7230 · inbound

Token-Based Affordance Grounding with Large Vision-Language Models cites this paper.

Token-Based Affordance Grounding with Large Vision-Language Models Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors

Reference 44

Resolution
unresolved
no resolver link, observed 2026-07-12T01:18:51.054590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T01:18:51.054590Z digest=sha256:c220520002508096a0f9537badb856a827c47fe4bd2aa6c71c880c0dfd54b6e7