Weakly-Supervised Learning of Dense Functional Correspondences | Paper Citation Record

Observation 9dda9dd1-18d5-4c1b-b42f-e0fba1e583e1 · outbound

This paper cites Deep ViT Features as Dense Visual Descriptors.

Weakly-Supervised Learning of Dense Functional Correspondences Deep ViT Features as Dense Visual Descriptors

Reference 1

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Observation 08e178fb-d8fc-4cad-8a93-df4b88c4258c · outbound

This paper cites Contactdb: Analyzing and predicting grasp contact via thermal imaging.

Weakly-Supervised Learning of Dense Functional Correspondences Contactdb: Analyzing and predicting grasp contact via thermal imaging

Reference 2

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Observation 6ba0b3cb-94c5-45c7-8f6f-e6fd42d24874 · outbound

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

Weakly-Supervised Learning of Dense Functional Correspondences Emerg- ing properties in self-supervised vision transformers

Reference 3

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Observation a4bbc058-0cd8-4126-a622-c64b9d9389c5 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Weakly-Supervised Learning of Dense Functional Correspondences A simple framework for contrastive learning of visual representations

Reference 4

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Observation 3b6e9e88-64d6-4646-8a2c-6631d25ca806 · outbound

This paper cites Learning to act properly: Predicting and explaining affordances from images.

Weakly-Supervised Learning of Dense Functional Correspondences Learning to act properly: Predicting and explaining affordances from images

Reference 5

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Observation 3f62d68c-c342-4e39-89af-6696f2767b91 · outbound

This paper cites Blender - a 3D modelling and rendering package.

Weakly-Supervised Learning of Dense Functional Correspondences Blender - a 3D modelling and rendering package

Reference 6

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Observation 22c4983a-7d26-4fd5-8da3-5f8600716f77 · outbound

This paper cites Objaverse: A universe of annotated 3d objects.

Weakly-Supervised Learning of Dense Functional Correspondences Objaverse: A universe of annotated 3d objects

Reference 7

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Observation 44068fd0-5849-4e91-bf7d-b33f4cf17e88 · outbound

This paper cites Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models.

Weakly-Supervised Learning of Dense Functional Correspondences Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Reference 8

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Observation 54e1d0d6-4cf6-4396-89ee-d2bb07d2db17 · outbound

This paper cites Objaverse-xl: A universe of 10m+ 3d objects.

Weakly-Supervised Learning of Dense Functional Correspondences Objaverse-xl: A universe of 10m+ 3d objects

Reference 9

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Observation 24082b48-8c5b-4786-b547-896300122c83 · outbound

This paper cites 3d affordancenet: A benchmark for visual object af- fordance understanding.

Weakly-Supervised Learning of Dense Functional Correspondences 3d affordancenet: A benchmark for visual object af- fordance understanding

Reference 10

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Observation 3e65a991-4eed-4815-89e5-6c1cdd46efc5 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Weakly-Supervised Learning of Dense Functional Correspondences BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 11

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Observation 6cfad7e9-33ce-46a7-9f69-c77931230f17 · outbound

This paper cites On the effectiveness of retrieval, alignment, and replay in manipulation.

Weakly-Supervised Learning of Dense Functional Correspondences On the effectiveness of retrieval, alignment, and replay in manipulation

Reference 12

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Observation 4da627cc-66e5-409c-bd66-4908da3934eb · outbound

This paper cites PreAfford: Universal Affordance-Based Pre-Grasping for Diverse Objects and Environments.

Weakly-Supervised Learning of Dense Functional Correspondences PreAfford: Universal Affordance-Based Pre-Grasping for Diverse Objects and Environments

Reference 13

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Observation c292a0bd-355c-4cb6-9976-41659cdf370f · outbound

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

Weakly-Supervised Learning of Dense Functional Correspondences Affordancenet: An end-to-end deep learning approach for object affordance detection

Reference 14

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Observation 4cf07218-f03d-4b56-aa83-eeaecf64fcdf · outbound

This paper cites The Llama 3 Herd of Models.

Weakly-Supervised Learning of Dense Functional Correspondences The Llama 3 Herd of Models

Reference 15

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Observation 5ad74b88-7cd5-4ac3-b5be-dfb9c302342a · outbound

This paper cites Graspnet-1billion: A large-scale benchmark for general ob- ject grasping.

Weakly-Supervised Learning of Dense Functional Correspondences Graspnet-1billion: A large-scale benchmark for general ob- ject grasping

Reference 16

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Observation 805e4eed-8128-42bd-8c9f-e7c0ddab8d5c · outbound

This paper cites Dense object nets: Learning dense visual object descriptors by and for robotic manipulation.

Weakly-Supervised Learning of Dense Functional Correspondences Dense object nets: Learning dense visual object descriptors by and for robotic manipulation

Reference 17

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Observation 11335ea5-530d-4126-8e5f-97503cd6eeb3 · outbound

This paper cites The ecological approach to visual percep- tion: Classic edition.

Weakly-Supervised Learning of Dense Functional Correspondences The ecological approach to visual percep- tion: Classic edition

Reference 18

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Observation 252f9c99-9dae-4ab4-b010-47599daa734d · outbound

This paper cites Videoswap: Customized video subject swapping with interactive semantic point cor- respondence.

Weakly-Supervised Learning of Dense Functional Correspondences Videoswap: Customized video subject swapping with interactive semantic point cor- respondence

Reference 19

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Observation 774cdb51-38b9-4097-a3be-2099f0e906ac · outbound

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

Weakly-Supervised Learning of Dense Functional Correspondences Handal: A dataset of real-world manipulable object categories with pose annotations, affordances, and reconstructions

Reference 20

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Observation 2e9acd83-2075-411c-9246-1d72a3eeccb4 · outbound

This paper cites Hdri haven.

Weakly-Supervised Learning of Dense Functional Correspondences Hdri haven

Reference 21

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Observation 64ff4210-52f0-41f5-bdb6-5e4487196025 · outbound

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

Weakly-Supervised Learning of Dense Functional Correspondences Momentum contrast for unsupervised visual rep- resentation learning

Reference 22

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Observation 4c324b92-e024-4ee6-8562-c5df60189a38 · outbound

This paper cites Ditto: Demonstration imitation by trajectory transformation.

Weakly-Supervised Learning of Dense Functional Correspondences Ditto: Demonstration imitation by trajectory transformation

Reference 23

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Observation 5e1f652f-91ac-4bf0-9ed4-eefeb14cde8c · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Weakly-Supervised Learning of Dense Functional Correspondences Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 24

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Observation c0421c3f-3c1c-4cbf-bc3e-900d0d8c6ad3 · outbound

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

Weakly-Supervised Learning of Dense Functional Correspondences ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models

Reference 25

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Observation ae6e6a7f-4a1b-4f0d-854f-d82c2bea4d69 · outbound

This paper cites Flowformer: A transformer architecture for optical flow.

Weakly-Supervised Learning of Dense Functional Correspondences Flowformer: A transformer architecture for optical flow

Reference 26

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Observation 141b1507-215e-4661-9582-b85cf47102cd · outbound

This paper cites Flownet 2.0: Evolu- tion of optical flow estimation with deep networks.

Weakly-Supervised Learning of Dense Functional Correspondences Flownet 2.0: Evolu- tion of optical flow estimation with deep networks

Reference 27

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Observation e6bf1e14-8e5e-420a-91ea-7c700c496f3e · outbound

This paper cites Cotr: Correspondence transformer for matching across images.

Weakly-Supervised Learning of Dense Functional Correspondences Cotr: Correspondence transformer for matching across images

Reference 28

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Observation a830a10a-467b-47d2-8053-e1e92cb5f87d · outbound

This paper cites Doduo: Learning dense visual correspondence from unsupervised semantic-aware flow.

Weakly-Supervised Learning of Dense Functional Correspondences Doduo: Learning dense visual correspondence from unsupervised semantic-aware flow

Reference 29

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Observation 74b4b54d-6b60-4cf7-ad31-e95662366bf5 · outbound

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

Weakly-Supervised Learning of Dense Functional Correspondences Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation

Reference 30

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Observation 45b9f8d8-23f4-40d4-97a2-7002450c8c2e · outbound

This paper cites Segment anything in high qual- ity.

Weakly-Supervised Learning of Dense Functional Correspondences Segment anything in high qual- ity

Reference 31

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Observation c819f948-fc97-400b-a30c-aea042966300 · outbound

This paper cites Two-year-olds will name artifacts by their func- tions.

Weakly-Supervised Learning of Dense Functional Correspondences Two-year-olds will name artifacts by their func- tions

Reference 32

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Observation f11b49e1-c60c-462d-ab03-d8fd3f196070 · outbound

This paper cites Kingma and Jimmy Ba.

Weakly-Supervised Learning of Dense Functional Correspondences Kingma and Jimmy Ba

Reference 33

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This paper cites Segment any- thing.

Weakly-Supervised Learning of Dense Functional Correspondences Segment any- thing

Reference 34

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Observation ac6f7933-b605-49a4-809e-6dbe25049322 · outbound

This paper cites OmniNOCS: A unified NOCS dataset and model for 3D lifting of 2D objects.

Weakly-Supervised Learning of Dense Functional Correspondences OmniNOCS: A unified NOCS dataset and model for 3D lifting of 2D objects

Reference 35

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Observation 26903194-4257-48b8-847a-924037ce4209 · outbound

This paper cites RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation.

Weakly-Supervised Learning of Dense Functional Correspondences RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation

Reference 36

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Observation e7650c9c-57f8-4fbb-a43e-056cd93c296f · outbound

This paper cites Ge- ometry and context for semantic correspondences and func- tionality recognition in man-made 3d shapes.

Weakly-Supervised Learning of Dense Functional Correspondences Ge- ometry and context for semantic correspondences and func- tionality recognition in man-made 3d shapes

Reference 37

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Observation 9c3a4e6c-ff63-4c49-a184-7fb517408a71 · outbound

This paper cites The functional correspondence problem.

Weakly-Supervised Learning of Dense Functional Correspondences The functional correspondence problem

Reference 38

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This paper cites Object shape, object function, and object name.

Weakly-Supervised Learning of Dense Functional Correspondences Object shape, object function, and object name

Reference 39

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This paper cites Corresnerf: Image correspondence priors for neural radiance fields.

Weakly-Supervised Learning of Dense Functional Correspondences Corresnerf: Image correspondence priors for neural radiance fields

Reference 40

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

Weakly-Supervised Learning of Dense Functional Correspondences Locate: Localize and transfer object parts for weakly supervised affordance grounding

Reference 41

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Observation d8bdbe93-329c-41ad-a4b1-9b7dcde16ba6 · outbound

This paper cites Learning precise affordances from egocentric videos for robotic manipulation.

Weakly-Supervised Learning of Dense Functional Correspondences Learning precise affordances from egocentric videos for robotic manipulation

Reference 42

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Observation befb1564-9e7e-4735-af7f-6cd35392db8b · outbound

This paper cites Surface and edge detection for primitive fitting of point clouds.

Weakly-Supervised Learning of Dense Functional Correspondences Surface and edge detection for primitive fitting of point clouds

Reference 43

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This paper cites SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models.

Weakly-Supervised Learning of Dense Functional Correspondences SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models

Reference 44

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This paper cites Pixel-perfect structure-from- motion with featuremetric refinement.

Weakly-Supervised Learning of Dense Functional Correspondences Pixel-perfect structure-from- motion with featuremetric refinement

Reference 45

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Observation 445ad572-1fa9-4d83-a4d3-c7697279f620 · outbound

This paper cites Learning affordance grounding from exocen- tric images.

Weakly-Supervised Learning of Dense Functional Correspondences Learning affordance grounding from exocen- tric images

Reference 46

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Observation 2e503d6a-a5c0-4600-9af1-9a9786ed454b · outbound

This paper cites Scalable 3d captioning with pretrained models.

Weakly-Supervised Learning of Dense Functional Correspondences Scalable 3d captioning with pretrained models

Reference 47

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Observation 6f275994-9109-446b-af6a-bcc2a9de1545 · outbound

This paper cites TrackNeRF: Bundle Adjusting NeRF from Sparse and Noisy Views via Feature Tracks.

Weakly-Supervised Learning of Dense Functional Correspondences TrackNeRF: Bundle Adjusting NeRF from Sparse and Noisy Views via Feature Tracks

Reference 48

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Observation f0354f1f-47b7-4142-bfd0-501b48ffd234 · outbound

This paper cites SPair-71k: A Large-scale Benchmark for Semantic Correspondence.

Weakly-Supervised Learning of Dense Functional Correspondences SPair-71k: A Large-scale Benchmark for Semantic Correspondence

Reference 49

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This paper cites 6-dof graspnet: Variational grasp generation for object manipula- tion.

Weakly-Supervised Learning of Dense Functional Correspondences 6-dof graspnet: Variational grasp generation for object manipula- tion

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:54.106266Z

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Observation 60bc4d76-4c09-4dc9-91cf-bf8b89bf10ab · outbound

This paper cites Same object, differ- ent grasps: Data and semantic knowledge for task-oriented grasping.

Weakly-Supervised Learning of Dense Functional Correspondences Same object, differ- ent grasps: Data and semantic knowledge for task-oriented grasping

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:54.089555Z

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Observation a27c0b81-770e-486a-8e1e-b0dd24c88804 · outbound

This paper cites Affordance detection of tool parts from ge- ometric features.

Weakly-Supervised Learning of Dense Functional Correspondences Affordance detection of tool parts from ge- ometric features

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:54.050508Z

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Observation dffa9ede-c556-42fb-aafa-268e210b95f4 · outbound

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

Weakly-Supervised Learning of Dense Functional Correspondences Grounded human-object interaction hotspots from video

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:54.031080Z

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Observation 414cce45-2e43-4059-bdda-07755a652132 · outbound

This paper cites Caldwell, and Nikos G.

Weakly-Supervised Learning of Dense Functional Correspondences Caldwell, and Nikos G

Reference 54

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This paper cites an unresolved cited work.

Weakly-Supervised Learning of Dense Functional Correspondences Unresolved cited work

Reference 55

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Observation e62f489d-0586-4292-b808-84b58c130e63 · outbound

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

Weakly-Supervised Learning of Dense Functional Correspondences DINOv2: Learning Robust Visual Features without Supervision

Reference 56

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Observation 156d82b5-b672-4545-92af-9859dd106ded · outbound

This paper cites A threshold selection method from gray-level histograms.

Weakly-Supervised Learning of Dense Functional Correspondences A threshold selection method from gray-level histograms

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.971583Z

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Observation ba857c8b-fe11-4fe6-9f43-f17896f923de · outbound

This paper cites Dinobot: Robot ma- nipulation via retrieval and alignment with vision foundation models.

Weakly-Supervised Learning of Dense Functional Correspondences Dinobot: Robot ma- nipulation via retrieval and alignment with vision foundation models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.956189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3ff3061a-53d8-4a63-8e8a-327295ed6e5f · outbound

This paper cites Miles: Making imitation learning easy with self-supervision.

Weakly-Supervised Learning of Dense Functional Correspondences Miles: Making imitation learning easy with self-supervision

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.938034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 11d20068-4d25-47f4-9284-7fbcc3399a3d · outbound

This paper cites Film: Visual reasoning with a general conditioning layer, 2017.

Weakly-Supervised Learning of Dense Functional Correspondences Film: Visual reasoning with a general conditioning layer, 2017

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.923043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6d48566b-1954-42ae-a044-6da3a5db9e78 · outbound

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

Weakly-Supervised Learning of Dense Functional Correspondences Affordancellm: Grounding affordance from vision language models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.907173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 00dde4c0-7462-4aed-b7ee-8b63a61285b7 · outbound

This paper cites Keto: Learning keypoint representations for tool manipulation.

Weakly-Supervised Learning of Dense Functional Correspondences Keto: Learning keypoint representations for tool manipulation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.888885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2e61bec9-cf25-4592-ad34-0fb9f9580942 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Weakly-Supervised Learning of Dense Functional Correspondences Learning transferable visual models from natural language supervi- sion

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-05T10:37:52.599482Z

Source-reported events for the cited work

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Observation adef183f-a389-456b-a2c6-11c3b91f7726 · outbound

This paper cites an unresolved cited work.

Weakly-Supervised Learning of Dense Functional Correspondences Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:37:53.862528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e8e320f1-62d8-44f4-9e41-a7972b447a30 · outbound

This paper cites Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction.

Weakly-Supervised Learning of Dense Functional Correspondences Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.846206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 19778d75-ae1e-4202-9d17-9a8a2a3334e3 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Weakly-Supervised Learning of Dense Functional Correspondences High-resolution image synthesis with latent diffusion models

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.830662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cf8c2dcd-3829-4db9-9deb-caf3c4c73a55 · outbound

This paper cites Superglue: Learning feature matching with graph neural networks.

Weakly-Supervised Learning of Dense Functional Correspondences Superglue: Learning feature matching with graph neural networks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.815886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 49891fc6-66b8-4965-9572-9623936f7c57 · outbound

This paper cites Learning dense object descriptors from multiple views for low-shot category generalization.

Weakly-Supervised Learning of Dense Functional Correspondences Learning dense object descriptors from multiple views for low-shot category generalization

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.801070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3936c8e0-37b4-4dd1-a4d6-12e50ace7511 · outbound

This paper cites Loftr: Detector-free local feature matching with transformers.

Weakly-Supervised Learning of Dense Functional Correspondences Loftr: Detector-free local feature matching with transformers

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.787455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0df06f4d-5956-42b6-96b0-262f4d841fc0 · outbound

This paper cites Misc210k: A large-scale dataset for multi-instance seman- tic correspondence.

Weakly-Supervised Learning of Dense Functional Correspondences Misc210k: A large-scale dataset for multi-instance seman- tic correspondence

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.769660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3600e6a1-1023-49a7-a473-86fa01fbe90b · outbound

This paper cites Emergent correspondence from image diffusion.

Weakly-Supervised Learning of Dense Functional Correspondences Emergent correspondence from image diffusion

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.754669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1aa0ab32-e8cb-417e-8bfe-d1b0c30c762e · outbound

This paper cites Joint re- covery of dense correspondence and cosegmentation in two images.

Weakly-Supervised Learning of Dense Functional Correspondences Joint re- covery of dense correspondence and cosegmentation in two images

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.741014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d3359896-4eff-4007-b37a-6d08c590e6c8 · outbound

This paper cites Raft: Recurrent all-pairs field transforms for optical flow.

Weakly-Supervised Learning of Dense Functional Correspondences Raft: Recurrent all-pairs field transforms for optical flow

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.725721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c8b627d9-d2d1-4326-929e-16ac7b168656 · outbound

This paper cites Shape-based transfer of generic skills.

Weakly-Supervised Learning of Dense Functional Correspondences Shape-based transfer of generic skills

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.706169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bfb6c430-c124-4ad3-be10-5c98fdd0ab1f · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Weakly-Supervised Learning of Dense Functional Correspondences LLaMA: Open and Efficient Foundation Language Models

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-05T10:37:52.647487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 057bb059-07ce-4d63-8f82-71f859fe7b25 · outbound

This paper cites Sparf: Neural radiance fields from sparse and noisy poses.

Weakly-Supervised Learning of Dense Functional Correspondences Sparf: Neural radiance fields from sparse and noisy poses

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.690609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b1d4f96b-b3fa-4515-8ea1-5d9d9e202847 · outbound

This paper cites GIFT: Generalizable Interaction-aware Functional Tool Affordances without Labels.

Weakly-Supervised Learning of Dense Functional Correspondences GIFT: Generalizable Interaction-aware Functional Tool Affordances without Labels

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-05T10:37:52.864330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5d681bf2-74ff-47a0-b360-7b799fed21a5 · outbound

This paper cites Repre- sentation learning with contrastive predictive coding, 2019.

Weakly-Supervised Learning of Dense Functional Correspondences Repre- sentation learning with contrastive predictive coding, 2019

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.675117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c5bb8dca-4a3f-4670-ae34-beef66388a08 · outbound

This paper cites Knowledge transfer from vision foundation models for efficient training of small task-specific models.

Weakly-Supervised Learning of Dense Functional Correspondences Knowledge transfer from vision foundation models for efficient training of small task-specific models

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.660013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aa12bbd5-ce77-4120-9f25-22e709f3612d · outbound

This paper cites Socs: Semantically- aware object coordinate space for category-level 6d object pose estimation under large shape variations.

Weakly-Supervised Learning of Dense Functional Correspondences Socs: Semantically- aware object coordinate space for category-level 6d object pose estimation under large shape variations

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.643381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7e83b1b7-842e-4eb3-a0e5-320d989149c0 · outbound

This paper cites Normalized object coordinate space for category-level 6d object pose and size estimation.

Weakly-Supervised Learning of Dense Functional Correspondences Normalized object coordinate space for category-level 6d object pose and size estimation

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.619800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 09969101-a23d-42d7-af53-c1a8064571d9 · outbound

This paper cites CogVLM: Visual Expert for Pretrained Language Models.

Weakly-Supervised Learning of Dense Functional Correspondences CogVLM: Visual Expert for Pretrained Language Models

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-05T10:37:52.676543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce382009-6a1f-4144-94bd-16631a9819bd · outbound

This paper cites Form follows function: Learning about function helps children learn about shape.

Weakly-Supervised Learning of Dense Functional Correspondences Form follows function: Learning about function helps children learn about shape

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.603208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 13dfdab1-4c15-4cc6-8dde-dd3818173f95 · outbound

This paper cites Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation.

Weakly-Supervised Learning of Dense Functional Correspondences Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.580653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1e10108a-7c7c-4f68-83d7-6192feeb670d · outbound

This paper cites An affordance keypoint detection network for robot manipulation.

Weakly-Supervised Learning of Dense Functional Correspondences An affordance keypoint detection network for robot manipulation

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.560203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 319be8d4-9162-4c3d-93a7-3e8d8ffa7620 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data.

Weakly-Supervised Learning of Dense Functional Correspondences Depth anything: Unleashing the power of large-scale unlabeled data

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.538657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aff55648-00e4-4d82-b26e-f5f7cc19fad9 · outbound

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

Weakly-Supervised Learning of Dense Functional Correspondences Grounding 3d object affordance from 2d interactions in images

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.523771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 14408208-d319-4a35-8cc3-460ff8778de3 · outbound

This paper cites Mvimgnet: A large-scale dataset of multi-view images.

Weakly-Supervised Learning of Dense Functional Correspondences Mvimgnet: A large-scale dataset of multi-view images

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.507689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation edd14926-7aa5-46c8-882f-ee5306c133e8 · outbound

This paper cites RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics.

Weakly-Supervised Learning of Dense Functional Correspondences RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-05T10:37:52.706273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d001b42-b607-4c70-acbc-e18784ca4487 · outbound

This paper cites Sigmoid loss for language image pre-training.

Weakly-Supervised Learning of Dense Functional Correspondences Sigmoid loss for language image pre-training

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.488567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 814112bc-033c-41f9-b4b0-b26ccc74ca13 · outbound

This paper cites A tale of two features: Stable diffusion complements dino for zero-shot semantic correspondence, 2023.

Weakly-Supervised Learning of Dense Functional Correspondences A tale of two features: Stable diffusion complements dino for zero-shot semantic correspondence, 2023

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.469499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0d179c6e-e5d8-4831-88f9-f248570541e6 · outbound

This paper cites Egoobjects: A large-scale egocentric dataset for fine-grained object understanding.

Weakly-Supervised Learning of Dense Functional Correspondences Egoobjects: A large-scale egocentric dataset for fine-grained object understanding

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.441781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7b688654-883f-4b38-ab34-2628b908ba3f · outbound

This paper cites Understanding tools: Task-oriented object modeling, learning and recogni- tion.

Weakly-Supervised Learning of Dense Functional Correspondences Understanding tools: Task-oriented object modeling, learning and recogni- tion

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.423154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 36dc9322-e1dd-407f-9e1c-463dc66d6069 · outbound

This paper cites Vision-based Manipulation from Single Human Video with Open-World Object Graphs.

Weakly-Supervised Learning of Dense Functional Correspondences Vision-based Manipulation from Single Human Video with Open-World Object Graphs

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-05T10:37:52.726480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e4bb08a-8ac2-40de-8331-eefd83cd63e0 · outbound

This paper cites Given this list of functions, generate more options for object func- tions.

Weakly-Supervised Learning of Dense Functional Correspondences Given this list of functions, generate more options for object func- tions

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.400304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7c3eac04-823e-41ba-bbf5-4d6c9a81521f · outbound

This paper cites an unresolved cited work.

Weakly-Supervised Learning of Dense Functional Correspondences Unresolved cited work

Reference 97

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:37:53.373640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9b8d4b22-e59f-45ed-a17f-4836a3957e7b · outbound

This paper cites For small parts like points or tips, we do a second iteration that zooms into the initial bounding boxes to improve precision.

Weakly-Supervised Learning of Dense Functional Correspondences For small parts like points or tips, we do a second iteration that zooms into the initial bounding boxes to improve precision

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.352598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f54116b0-d2f4-4773-9d29-59f10317171b · outbound

This paper cites Ev- ery time a given point in the point cloud gets labeled by a bounding box in a different view, we increment its score.

Weakly-Supervised Learning of Dense Functional Correspondences Ev- ery time a given point in the point cloud gets labeled by a bounding box in a different view, we increment its score

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.338351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 46701a53-6c6c-44c4-8ab3-aafaf3d4aec8 · outbound

This paper cites an unresolved cited work.

Weakly-Supervised Learning of Dense Functional Correspondences Unresolved cited work

Reference 100

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:37:53.321941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a1963594-fb98-4dd8-9c05-948193a3c314 · outbound

This paper cites Example outputs of this procedure are shown in Figure 7.

Weakly-Supervised Learning of Dense Functional Correspondences Example outputs of this procedure are shown in Figure 7

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:37:53.306786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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