ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models | Paper Citation Record

Observation 6476cf82-976f-4d62-9052-2faffe87043e · outbound

This paper cites Unseen object instance segmentation for robotic environments,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Unseen object instance segmentation for robotic environments,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.538866Z

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Observation a9ff49f9-caeb-4e95-9fc9-6544cfc46715 · outbound

This paper cites Taylor neural network for unseen object instance segmentation in hierarchical grasping,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Taylor neural network for unseen object instance segmentation in hierarchical grasping,

Reference 2

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T05:23:20.753148Z

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Observation 0f5676cf-fa96-47d6-88de-31117bfd6235 · outbound

This paper cites Segmentation of unknown objects in indoor environments,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Segmentation of unknown objects in indoor environments,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.524604Z

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Observation 32bbd310-4b23-4ccb-bb53-50c4a2a28aea · outbound

This paper cites Learn fast, segment well: Fast object segmentation learning on the icub robot,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Learn fast, segment well: Fast object segmentation learning on the icub robot,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.510313Z

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Observation 701e7110-bfa6-4cea-86c6-0562c7f722d7 · outbound

This paper cites Learning rgb-d feature embeddings for unseen object instance segmentation,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Learning rgb-d feature embeddings for unseen object instance segmentation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.495522Z

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Observation 89b0c793-5613-46c7-8530-513209a7a5ef · outbound

This paper cites Stow: Discrete-frame segmentation and tracking of unseen objects for warehouse picking robots,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Stow: Discrete-frame segmentation and tracking of unseen objects for warehouse picking robots,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.479687Z

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Observation 56fd16fb-ca0a-4e9f-b58b-c3df087b9fb4 · outbound

This paper cites Mask r-cnn,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Mask r-cnn,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.464135Z

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Observation e91b3374-1d2b-40b6-a32b-7a6ae767de25 · outbound

This paper cites Image segmentation using deep learning: A survey,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Image segmentation using deep learning: A survey,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.448449Z

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Observation 156047b9-bce1-4afe-898f-91216fbd73b9 · outbound

This paper cites Imagenet large scale visual recognition challenge,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Imagenet large scale visual recognition challenge,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.434253Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6b348b9e-beca-40f6-8e75-ab294a2a174b · outbound

This paper cites Microsoft coco: Common objects in context,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Microsoft coco: Common objects in context,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.419748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7b88ad0c-6a85-4e6c-88c4-e3f8135b33c5 · outbound

This paper cites Unseen object amodal instance segmentation via hierarchical occlusion mod- eling,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Unseen object amodal instance segmentation via hierarchical occlusion mod- eling,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.405133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1b422727-4637-4608-a5f0-b54a6d5076bd · outbound

This paper cites Unknown object segmentation from stereo images,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Unknown object segmentation from stereo images,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.391631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5288e27f-be1a-4548-8949-cd5d4a7df62c · outbound

This paper cites The best of both modes: Separately leveraging rgb and depth for unseen object instance segmentation,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models The best of both modes: Separately leveraging rgb and depth for unseen object instance segmentation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.377280Z

Source-reported events for the cited work

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Observation e81d89b0-0161-428a-b49d-365064c0f7ed · outbound

This paper cites Segmenting unknown 3d objects from real depth images using mask r-cnn trained on synthetic data,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Segmenting unknown 3d objects from real depth images using mask r-cnn trained on synthetic data,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.363133Z

Source-reported events for the cited work

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Observation 3c3a61b3-9ab9-42fa-9fa4-0bdebabd04f9 · outbound

This paper cites Unseen object instance segmentation with fully test-time rgb-d embeddings adaptation,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Unseen object instance segmentation with fully test-time rgb-d embeddings adaptation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.349197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 54a555ba-77d1-47c8-8ee3-4fa17c30d32e · outbound

This paper cites Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-09T05:23:20.511015Z

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Observation e742de18-d91b-4578-ab4f-44137aa2c639 · outbound

This paper cites Self-supervised interactive object segmentation through a singulation-and-grasping approach,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Self-supervised interactive object segmentation through a singulation-and-grasping approach,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.228756Z

Source-reported events for the cited work

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Observation e7dcfb8a-91a4-45df-a579-bd74a435c361 · outbound

This paper cites Self-supervised transfer learning for instance segmentation through physical interaction,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Self-supervised transfer learning for instance segmentation through physical interaction,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.214304Z

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Observation 95507e09-a7f3-4c99-a672-9eb5ee5a684e · outbound

This paper cites Segment Anything.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Segment Anything

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T05:23:20.192654Z

Source-reported events for the cited work

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Observation c93e2336-db87-44bb-b734-2b53dbea134c · outbound

This paper cites Seggpt: Segmenting everything in context,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Seggpt: Segmenting everything in context,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.199626Z

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Observation be13fd24-5fac-462d-b587-dfac89a9a6a6 · outbound

This paper cites Semantic segment anything,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Semantic segment anything,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.185246Z

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Observation 11f14516-8011-4a2d-a08c-4ad1cfb20a61 · outbound

This paper cites Segment everything everywhere all at once,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Segment everything everywhere all at once,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.170197Z

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Observation c3ebe5a3-47e2-4d2f-a81e-bcd11c7ebae1 · outbound

This paper cites Matplotlib: A 2d graphics environment,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Matplotlib: A 2d graphics environment,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.155592Z

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Observation a6af7ca2-ad80-4888-aca4-828a58c583ca · outbound

This paper cites Dinov2: Learning robust visual features without supervision,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Dinov2: Learning robust visual features without supervision,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.138516Z

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Observation 9c715dcf-c307-4f6e-852e-8d745e795e56 · outbound

This paper cites Rice: Refining instance masks in cluttered environments with graph neural networks,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Rice: Refining instance masks in cluttered environments with graph neural networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.119147Z

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Observation ca2cf670-a8f0-4e9e-ab94-0ceb2148eeac · outbound

This paper cites ZeroPose: CAD-Prompted Zero-shot Object 6D Pose Estimation in Cluttered Scenes.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models ZeroPose: CAD-Prompted Zero-shot Object 6D Pose Estimation in Cluttered Scenes

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T05:23:20.221643Z

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Observation 8edf030d-e922-4308-acc7-59dba7b8c472 · outbound

This paper cites Cnos: A strong baseline for cad-based novel object segmentation,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Cnos: A strong baseline for cad-based novel object segmentation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.100270Z

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Observation b0e594ad-1030-4e50-b31b-b500dd096b6f · outbound

This paper cites Panoptic seg- mentation,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Panoptic seg- mentation,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.082316Z

Source-reported events for the cited work

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Observation f36af7de-0bf3-46fc-838a-0d4000357252 · outbound

This paper cites Yolact: Real-time instance segmentation,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Yolact: Real-time instance segmentation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.066464Z

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Observation a1f3a908-cb16-4706-a202-47ff3b62f363 · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Fully convolutional networks for semantic segmentation,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T05:23:20.238745Z

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Observation 4518110c-7b4c-41a6-8550-d103d3dac445 · outbound

This paper cites Foundational Models Defining a New Era in Vision: A Survey and Outlook.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Foundational Models Defining a New Era in Vision: A Survey and Outlook

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T05:23:20.242929Z

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Observation 412b69be-84f8-43fe-aa63-2a92b85082fb · outbound

This paper cites Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T05:23:20.248723Z

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Observation ed34e449-ddcb-432b-8bfb-19ae0bc3e577 · outbound

This paper cites Segment and Track Anything.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Segment and Track Anything

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T05:23:20.253205Z

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Observation b7fe7a0e-b6f6-4d57-99a2-be9a911bc198 · outbound

This paper cites Inpaint Anything: Segment Anything Meets Image Inpainting.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Inpaint Anything: Segment Anything Meets Image Inpainting

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-09T05:23:20.257724Z

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Observation ecd83b3c-2fcb-4424-8d9d-316b72ab7518 · outbound

This paper cites All-in-sam: from weak annotation to pixel-wise nuclei segmentation with prompt-based finetuning,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models All-in-sam: from weak annotation to pixel-wise nuclei segmentation with prompt-based finetuning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.038696Z

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Observation f781c3d2-c427-4d55-9b9d-3fe9f2feb0d0 · outbound

This paper cites UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-09T05:23:20.266079Z

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Observation be8d51ea-8808-441f-a96f-bad992dbb5f5 · outbound

This paper cites Attention is all you need,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Attention is all you need,

Reference 37

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Observation 4269a717-34a4-45f6-93c1-24e7d34fbf3e · outbound

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

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:21.010904Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 20554afa-5199-4459-9c72-eaa5c52757e3 · outbound

This paper cites Cross-level multi-modal features learning with transformer for rgb-d object recognition,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Cross-level multi-modal features learning with transformer for rgb-d object recognition,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:20.994413Z

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Observation c9031c0f-ebf8-40a0-8268-ec954db31cc9 · outbound

This paper cites An empirical study of training self- supervised vision transformers,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models An empirical study of training self- supervised vision transformers,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:20.975933Z

Source-reported events for the cited work

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Observation 28042dbc-bfda-4ca7-80c6-b00ac6c5133a · outbound

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

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Emerging properties in self-supervised vision transformers,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-09T05:23:20.286973Z

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Observation a832af2f-8466-4a1f-8945-73f18be8d8ff · outbound

This paper cites Mst: Masked self-supervised transformer for visual representation,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Mst: Masked self-supervised transformer for visual representation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:20.947889Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 332eaa78-d809-4aff-b197-324ab4bcbaca · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:20.925421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 38131734-cc85-4094-b2e9-d47bfabdcf96 · outbound

This paper cites Beit: Bert pre-training of image transformers,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Beit: Bert pre-training of image transformers,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:20.910615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b0c9f7ec-a1b4-45c0-9037-355706538299 · outbound

This paper cites Masked au- toencoders are scalable vision learners,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Masked au- toencoders are scalable vision learners,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:20.893921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b88df0a6-50b2-486c-b9a4-a3bba402e3ec · outbound

This paper cites Deep vit features as dense visual descriptors,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Deep vit features as dense visual descriptors,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:20.878823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7f9315ac-fb86-49a7-a18d-0a36d066e887 · outbound

This paper cites Easylabel: A semi- automatic pixel-wise object annotation tool for creating robotic rgb-d datasets,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Easylabel: A semi- automatic pixel-wise object annotation tool for creating robotic rgb-d datasets,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:20.864158Z

Source-reported events for the cited work

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Observation 94527a10-78ad-427a-a527-c295b525417a · outbound

This paper cites A simple and fast algorithm for k-medoids clustering,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models A simple and fast algorithm for k-medoids clustering,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:20.849368Z

Source-reported events for the cited work

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Observation 25f14822-4517-42fc-a867-bf4e7cc5dec9 · outbound

This paper cites Vision transformers need registers,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Vision transformers need registers,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:20.834488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7095e00d-a8d3-42ff-a15f-5cc52d5bed30 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Pytorch: An imperative style, high-performance deep learning library,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-09T05:23:20.325001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7774d24-fdbe-4001-8428-e53298ab36dc · outbound

This paper cites Towards segmenting anything that moves,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Towards segmenting anything that moves,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:20.810502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 59362ca4-dd32-4592-90de-a181d5af94f6 · outbound

This paper cites Mean Shift Mask Transformer for Unseen Object Instance Segmentation.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Mean Shift Mask Transformer for Unseen Object Instance Segmentation

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-09T05:23:20.388116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c42e1cbc-0e51-4ebc-9d04-84dfdf1de024 · outbound

This paper cites Contact- graspnet: Efficient 6-dof grasp generation in cluttered scenes,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Contact- graspnet: Efficient 6-dof grasp generation in cluttered scenes,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-09T05:23:20.337953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebbd8654-fb32-41b3-a956-c6a916b427b4 · outbound

This paper cites Safe and efficient robot manipulation: Task-oriented environment modeling and object pose estimation,.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Safe and efficient robot manipulation: Task-oriented environment modeling and object pose estimation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:20.785710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2ca36434-fd16-4e2a-a0e8-49436e369490 · outbound

This paper cites Moveit![ros topics],.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Moveit![ros topics],

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:23:20.769931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Reference resolution

Outbound references

Pith citing papers