Paper Citation Record · LEDGER

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data

As of 23 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2602.11804.

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

pith.paper-citation-record.v1
2602.11804 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T00:02:11.879023Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T00:02:10.585597Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

verified exact0
verified fuzzy0
unresolved25
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f5a11bd2-e1ca-4f5d-a908-d5c70c878935 · outbound

This paper cites Its success is largely attributed to large-scale pretraining on SA-1B [1] dataset which contains 11M images and 1B masks.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Its success is largely attributed to large-scale pretraining on SA-1B [1] dataset which contains 11M images and 1B masks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:10.531323Z

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Unavailable: canonical work link unavailable.

Observation 3f326db7-26ca-4ddd-9dfc-4591ce544d38 · outbound

This paper cites Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:10.585597Z

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Observation 720d4a49-6080-4fa1-bc95-ce0ad0f5bfb6 · outbound

This paper cites EfficientViT [4] further accelerates SAM with a lightweight vision transformer.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data EfficientViT [4] further accelerates SAM with a lightweight vision transformer

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:10.627809Z

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Observation 1544fcde-28f0-4aeb-a5fa-78ad0ed84811 · outbound

This paper cites We keep the original SAM prompt encoder and mask decoder unchanged, while replac- ing SAM’s heavy image encoder with EfficientViT to achieve efficiency.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data We keep the original SAM prompt encoder and mask decoder unchanged, while replac- ing SAM’s heavy image encoder with EfficientViT to achieve efficiency

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:10.667456Z

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Observation 08450c05-76d3-4ac5-9e83-e20d31b93d6f · outbound

This paper cites Experimental Settings We build on EfficientViT-SAM-L2, which offers the best trade-off between accuracy and computation cost, and extend it with our depth-aware framework.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Experimental Settings We build on EfficientViT-SAM-L2, which offers the best trade-off between accuracy and computation cost, and extend it with our depth-aware framework

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:10.721713Z

Source-reported events for the cited work

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Observation b39146f1-7168-4882-a8bb-0fb5b8dc32d0 · outbound

This paper cites an unresolved cited work.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:10.780862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72e21640-9081-45cf-b8fc-5e1a32f7e49f · outbound

This paper cites Segment anything,.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Segment anything,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:10.827221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d30ac30-d30b-4b9c-8c59-afe7ffb71d6d · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:10.884332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43547a81-86e5-42d4-9bdb-0d6ba2f72d20 · outbound

This paper cites Fast Segment Anything.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Fast Segment Anything

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:10.962607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49522811-113a-4383-87e6-67345a676f41 · outbound

This paper cites Efficientvit: Mem- ory efficient vision transformer with cascaded group at- tention,.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Efficientvit: Mem- ory efficient vision transformer with cascaded group at- tention,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.020771Z

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Observation 1cdda066-df89-4c92-9ff3-5b5967bdfb45 · outbound

This paper cites Efficientvit- sam: Accelerated segment anything model without per- formance loss,.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Efficientvit- sam: Accelerated segment anything model without per- formance loss,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.121438Z

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Observation f5ba3267-7a66-41f5-a20f-f5a2ebfe2801 · outbound

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

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Depth anything: Unleashing the power of large-scale unlabeled data,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.174321Z

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Observation c67fe298-7b32-47f0-aff3-30da96baf5c1 · outbound

This paper cites Segment and Track Anything.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Segment and Track Anything

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.238949Z

Source-reported events for the cited work

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Observation 156d2f43-a9e8-416d-80ea-c4b6e496a31c · outbound

This paper cites Vggt: Visual geometry grounded transformer,.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Vggt: Visual geometry grounded transformer,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.306244Z

Source-reported events for the cited work

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Observation 6eb0102e-36f9-4daa-9d7f-3770d464fea2 · outbound

This paper cites Evaluating mod- ern approaches in 3d scene reconstruction: Nerf vs gaussian-based methods,.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Evaluating mod- ern approaches in 3d scene reconstruction: Nerf vs gaussian-based methods,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.346720Z

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Observation dbcd7b73-58b7-4fac-bcca-8cd47106ef65 · outbound

This paper cites Ddn-slam: Real time dense dynamic neural implicit slam,.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Ddn-slam: Real time dense dynamic neural implicit slam,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.432665Z

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Observation 446b3389-a808-4261-acf0-3f7698b93d35 · outbound

This paper cites Dy3dgs-slam: Monocular 3d gaussian splatting slam for dynamic environments,.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Dy3dgs-slam: Monocular 3d gaussian splatting slam for dynamic environments,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.466222Z

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Observation ce3ce5fa-4e7b-41c1-b94b-627a907ea971 · outbound

This paper cites GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.519487Z

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Unavailable: canonical work link unavailable.

Observation 17807937-e839-4460-8b22-f6fb084a057e · outbound

This paper cites DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.598690Z

Source-reported events for the cited work

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Observation 5a334448-4abc-44e2-8e6f-2b57acf60fdd · outbound

This paper cites Dformerv2: Geometry self-attention for rgbd semantic segmentation,.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Dformerv2: Geometry self-attention for rgbd semantic segmentation,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.631110Z

Source-reported events for the cited work

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Observation 90314db6-3458-4b6c-a1ab-4c50202aa926 · outbound

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

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Microsoft coco: Common objects in context,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.701250Z

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Observation 190e81ed-c8af-42ff-a389-40be23bb47d4 · outbound

This paper cites Lvis: A dataset for large vocabulary instance segmentation,.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Lvis: A dataset for large vocabulary instance segmentation,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.736857Z

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Observation b593a7f9-9be5-4b15-b0af-f766f0aa1392 · outbound

This paper cites Exploring plain vision transformer backbones for object detection,.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Exploring plain vision transformer backbones for object detection,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.772569Z

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Observation c56fa57f-ec58-41eb-b236-518a3a8842e3 · outbound

This paper cites Uav-yolov8: A small-object- detection model based on improved yolov8 for uav aerial photography scenarios,.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Uav-yolov8: A small-object- detection model based on improved yolov8 for uav aerial photography scenarios,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.824158Z

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Observation ed2a4766-63f8-472e-8310-0c809bd05fbb · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object de- tection,.

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data Grounding dino: Marrying dino with grounded pre-training for open-set object de- tection,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T00:02:11.879023Z

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Pith citing papers

Observation 3f326db7-26ca-4ddd-9dfc-4591ce544d38 · inbound