SAMIC: Segment Anything with In-Context Spatial Prompt Engineering | Paper Citation Record

Observation 49ecbd45-b635-4780-8baf-9cee37a53241 · outbound

This paper cites Deep Learning using Rectified Linear Units (ReLU).

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Deep Learning using Rectified Linear Units (ReLU)

Reference 1

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Observation 71842d88-60d0-4c31-97f5-17eecb870a16 · outbound

This paper cites Vqa: Visual question answering.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Vqa: Visual question answering

Reference 2

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Observation 73cc8ffd-45af-49d3-9393-bc7287800420 · outbound

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

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering k-means++: The advantages of careful seeding

Reference 3

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Observation 95f5a19a-a85f-478a-bef1-2fa10a667d46 · outbound

This paper cites Sequential modeling enables scalable learn- ing for large vision models.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Sequential modeling enables scalable learn- ing for large vision models

Reference 4

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Observation 24796727-24ac-4116-94f7-dc1da6453b38 · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering BEiT: BERT Pre-Training of Image Transformers

Reference 5

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Observation 5b48ac84-5ae4-4165-a26a-2d2377426d21 · outbound

This paper cites Visual prompting via image inpaint- ing.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Visual prompting via image inpaint- ing

Reference 6

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Observation 081d7cb3-9bd1-401a-81e9-c7a9c3acd7d1 · outbound

This paper cites an unresolved cited work.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Unresolved cited work

Reference 7

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Observation f174d2c9-7e85-4447-8d7c-581da843a065 · outbound

This paper cites Training stochastic model recognition algo- rithms as networks can lead to maximum mutual information estimation of parameters.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Training stochastic model recognition algo- rithms as networks can lead to maximum mutual information estimation of parameters

Reference 8

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Observation 143bb07a-e0f2-4abf-95d5-f6bf50c09356 · outbound

This paper cites What do different evaluation metrics tell us about saliency models? IEEE transactions on pattern analysis and machine intelligence, 41(3):740–757, 2018.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering What do different evaluation metrics tell us about saliency models? IEEE transactions on pattern analysis and machine intelligence, 41(3):740–757, 2018

Reference 9

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Observation 90d8b936-ef88-49fe-b479-afb4f3d46841 · outbound

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

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Emerg- ing properties in self-supervised vision transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.498119Z

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Observation 6c751a9c-cb6e-4dcf-9d46-8d5fe5a913a9 · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.503858Z

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Observation 50d335b2-35e5-437e-913f-f05c3e6484aa · outbound

This paper cites Visual and tex- tual prior guided mask assemble for few-shot segmentation and beyond.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Visual and tex- tual prior guided mask assemble for few-shot segmentation and beyond

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.509772Z

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Observation d508cbb1-ac5f-43dc-9686-763f7b882752 · outbound

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

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering A simple framework for contrastive learning of visual representations

Reference 13

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Observation da2a37b3-cff5-453d-b851-c427fa771ea0 · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Masked-attention mask transformer for universal image segmentation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.520275Z

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Observation 7a9ad6f3-d031-44bb-85dd-652330d18281 · outbound

This paper cites Xmem: Long- term video object segmentation with an atkinson-shiffrin memory model.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Xmem: Long- term video object segmentation with an atkinson-shiffrin memory model

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.526129Z

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Observation eadc6d6f-f015-41ac-a1b5-74bbe76a05ed · outbound

This paper cites 4d spatio-temporal convnets: Minkowski convolutional neural 9 networks.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering 4d spatio-temporal convnets: Minkowski convolutional neural 9 networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.498952Z

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Observation e37f7bc6-76f8-4d58-8d89-3569d3a1d3e3 · outbound

This paper cites Pearson correlation coefficient.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Pearson correlation coefficient

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.537506Z

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Observation 05b07543-2fdf-483a-a397-0e69040a7645 · outbound

This paper cites Semantic image segmentation: Two decades of research.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Semantic image segmentation: Two decades of research

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.466965Z

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Observation 5fffa3ae-01f0-4cba-b650-13b8ff5bb42d · outbound

This paper cites Learning Dynamics from Kinematics: Estimating 2D Foot Pressure Maps from Video Frames.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Learning Dynamics from Kinematics: Estimating 2D Foot Pressure Maps from Video Frames

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:27:24.335990Z

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Observation 8be46628-3b88-4c5c-adfb-7f0061f925ea · outbound

This paper cites A survey on instance segmentation: state of the art.International jour- nal of multimedia information retrieval, 9(3):171–189, 2020.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering A survey on instance segmentation: state of the art.International jour- nal of multimedia information retrieval, 9(3):171–189, 2020

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.554917Z

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Observation a8523b2d-783b-4aa3-8cdb-cfa85d615b09 · outbound

This paper cites Deep residual learning for image recognition.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Deep residual learning for image recognition

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.427125Z

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Observation 9c0871d5-c975-468e-a93f-0cbbc260dad5 · outbound

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

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Momentum contrast for unsupervised visual rep- resentation learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.565310Z

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Observation 865cec87-0901-4a78-9140-b88c6a3cd0ff · outbound

This paper cites Masked autoencoders are scalable vision learners.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Masked autoencoders are scalable vision learners

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.391170Z

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Observation cdf83a3d-3616-4c2f-bc96-ee8cd395ee46 · outbound

This paper cites Cost Aggregation Is All You Need for Few-Shot Segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Cost Aggregation Is All You Need for Few-Shot Segmentation

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:27:24.308126Z

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Observation 3748f11c-6878-4d52-a8b3-2cf9ebfc608e · outbound

This paper cites Cost aggregation with 4d convolutional swin transformer for few-shot segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Cost aggregation with 4d convolutional swin transformer for few-shot segmentation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.582198Z

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Observation e323b08e-3f1e-4c61-bc16-2794e12ace10 · outbound

This paper cites Determining opti- cal flow.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Determining opti- cal flow

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.356984Z

Source-reported events for the cited work

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Observation 283c7306-e868-4b65-a0dc-aa74a67cf30b · outbound

This paper cites Visual pattern recognition by moment invari- ants.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Visual pattern recognition by moment invari- ants

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.337360Z

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Observation 9b46863f-93d1-421c-9e84-c1efe1655a26 · outbound

This paper cites Openclip, 2021.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Openclip, 2021

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.317734Z

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Observation 9b2f59a9-a3d5-4790-87d7-a53fa58c8702 · outbound

This paper cites Kvasir-seg: A segmented polyp dataset.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Kvasir-seg: A segmented polyp dataset

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.298862Z

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Observation 71089213-6c2e-4c6b-8d04-e4154cc59d5f · outbound

This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.615376Z

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Observation 41ae6be0-6b5e-49e7-896a-db018386f3f4 · outbound

This paper cites Eml-net: An expandable multi- layer network for saliency prediction.Image and vision com- puting, 95:103887, 2020.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Eml-net: An expandable multi- layer network for saliency prediction.Image and vision com- puting, 95:103887, 2020

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.246482Z

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Observation 33a418c7-7480-4241-a86d-0c948eb041fc · outbound

This paper cites Labelme: Image polygonal annotation with python, 2021.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Labelme: Image polygonal annotation with python, 2021

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.227228Z

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Observation b8bed304-8554-4ec0-b5bf-9a2ef5c106d4 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Adam: A Method for Stochastic Optimization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.633812Z

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Observation fc4e882b-1161-4d28-bb60-30d665785af1 · outbound

This paper cites Panoptic segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Panoptic segmentation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.208452Z

Source-reported events for the cited work

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Observation 2f563cbd-7e41-4e7c-b536-7ecc26097e77 · outbound

This paper cites Segment any- thing.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Segment any- thing

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.188382Z

Source-reported events for the cited work

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Observation dc45b233-0c85-4ad3-b176-46bfde4ec016 · outbound

This paper cites Align before fuse: Vision and language representation learn- ing with momentum distillation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Align before fuse: Vision and language representation learn- ing with momentum distillation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.651202Z

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Observation 4528be90-8ff9-4701-b3d4-803d826c4fc6 · outbound

This paper cites Fss-1000: A 1000-class dataset for few- shot segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Fss-1000: A 1000-class dataset for few- shot segmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.155058Z

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Observation 69506563-de6c-42ac-ba6a-632eed7fafe7 · outbound

This paper cites Microsoft coco: Common objects in context.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Microsoft coco: Common objects in context

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.134175Z

Source-reported events for the cited work

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Observation a1b4218f-0461-4e62-bbfc-09153735f591 · outbound

This paper cites Swem: Towards real- time video object segmentation with sequential weighted expectation-maximization.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Swem: Towards real- time video object segmentation with sequential weighted expectation-maximization

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.112968Z

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Observation c25a3775-ca2d-495d-bba6-1c462a06147d · outbound

This paper cites A new rainfall-induced deep learning strategy for landslide susceptibility prediction.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering A new rainfall-induced deep learning strategy for landslide susceptibility prediction

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.093660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 74c8ccf6-55f5-47c5-a162-18318c0f2444 · outbound

This paper cites PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.678542Z

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Observation 86e73493-5aef-44f8-8424-4016b0a4daa8 · outbound

This paper cites Part-aware prototype network for few-shot semantic segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Part-aware prototype network for few-shot semantic segmentation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.073521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9d95a41c-7437-4ad1-bf11-eb1081705dc5 · outbound

This paper cites Semantic correspondence as an optimal transport problem.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Semantic correspondence as an optimal transport problem

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.053810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 61a607d5-92a5-413b-b502-c1d323c807dc · outbound

This paper cites Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.695896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ef5c100-70be-4ffe-a345-5ab6ff2c7d37 · outbound

This paper cites Transalnet: Towards perceptually relevant visual saliency prediction.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Transalnet: Towards perceptually relevant visual saliency prediction

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.034399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4ccfd482-17ef-4444-9544-a45e60e1e941 · outbound

This paper cites Hyperpixel flow: Semantic correspondence with multi-layer neural features.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Hyperpixel flow: Semantic correspondence with multi-layer neural features

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.707030Z

Source-reported events for the cited work

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Observation 67255e5f-17ac-4dad-ac2d-9b4bc0b9f8b3 · outbound

This paper cites Hypercorrela- tion squeeze for few-shot segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Hypercorrela- tion squeeze for few-shot segmentation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.002726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 629a2cfe-30c6-483f-a801-ae4965a812b1 · outbound

This paper cites Simple Open-Vocabulary Object Detection with Vision Transformers.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Simple Open-Vocabulary Object Detection with Vision Transformers

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.717941Z

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Observation 8e69a46a-23b8-42e5-bc2c-4bdcce719d32 · outbound

This paper cites Thermal Analysis for NVIDIA GTX480 Fermi GPU Architecture.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Thermal Analysis for NVIDIA GTX480 Fermi GPU Architecture

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.723465Z

Source-reported events for the cited work

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Observation 0ccbec66-778f-4603-b021-0e4e2dda83b6 · outbound

This paper cites Patchrefinenet: Im- proving binary segmentation by incorporating signals from optimal patch-wise binarization.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Patchrefinenet: Im- proving binary segmentation by incorporating signals from optimal patch-wise binarization

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.982709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7c126114-d404-44b9-b2c9-c2cac658f1ea · outbound

This paper cites Emotion Recognition from the perspective of Activity Recognition.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Emotion Recognition from the perspective of Activity Recognition

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:27:24.179397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9d211a37-ef27-40df-9695-e3e3c0f38f8d · outbound

This paper cites Comparison of reinforcement learning al- gorithms applied to the cart-pole problem.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Comparison of reinforcement learning al- gorithms applied to the cart-pole problem

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.961794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 77bca4cf-006f-4651-901a-e22bfa5e5b44 · outbound

This paper cites An efficient deep learn- ing mechanism for cross-region generalization of landslide events.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering An efficient deep learn- ing mechanism for cross-region generalization of landslide events

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.940950Z

Source-reported events for the cited work

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Observation 64d17e37-5056-483f-b982-1fdbe68ecf56 · outbound

This paper cites Cloud-based interactive database man- agement suite integrated with deep learning-based annota- tion tool for landslide mapping.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Cloud-based interactive database man- agement suite integrated with deep learning-based annota- tion tool for landslide mapping

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.921335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 27a86f76-dc17-4f17-9c4b-ce97cb807f5b · outbound

This paper cites Constructing a large-scale landslide database across heterogeneous environ- ments using task-specific model updates.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Constructing a large-scale landslide database across heterogeneous environ- ments using task-specific model updates

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.901983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f667f2a6-0160-467e-8aa3-c77abf454738 · outbound

This paper cites PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:27:24.151160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1c783e47-65e4-4392-91f3-dc28d4965cbd · outbound

This paper cites Estimating Uncertainty in Landslide Segmentation Models.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Estimating Uncertainty in Landslide Segmentation Models

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:27:24.123936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f3cf1714-5aad-4b58-8a1c-d3a24298040d · outbound

This paper cites Feature weighting and boosting for few-shot segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Feature weighting and boosting for few-shot segmentation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.881864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 97db25b1-5cce-45fd-b399-9534167f412c · outbound

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

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering DINOv2: Learning Robust Visual Features without Supervision

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.781516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c20b684b-70ed-45c6-a668-d07cb6f7e055 · outbound

This paper cites Utilizing an interactive ai-empowered web portal for land- slide labeling for establishing a landslide database in wash- ington state, usa.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Utilizing an interactive ai-empowered web portal for land- slide labeling for establishing a landslide database in wash- ington state, usa

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.862958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0640b38d-7b08-443a-9d80-70a0cd173075 · outbound

This paper cites A benchmark dataset and evaluation methodology for video object segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering A benchmark dataset and evaluation methodology for video object segmentation

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.793074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e3d0de0-e73e-4da0-9ed9-1234ba84fe46 · outbound

This paper cites Components of bottom-up gaze allocation in natural images.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Components of bottom-up gaze allocation in natural images

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.831627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c4951547-7b8e-4e82-a49d-10906f2ef882 · outbound

This paper cites Foundations of json schema.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Foundations of json schema

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.812113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 513e4183-e86e-4240-b96f-0706f864826b · outbound

This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering The 2017 DAVIS Challenge on Video Object Segmentation

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.809375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6be3ea15-429a-426f-ab7b-b6a62afd3094 · outbound

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

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Learning transferable visual models from natural language supervi- sion

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.815306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1df494b8-d396-42ea-8e85-3eb4928f6a6c · outbound

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

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering High-resolution image synthesis with latent diffusion models

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.780595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8bd78c80-fa43-41ef-b1f5-d09a821fa0d9 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering U-net: Convolutional networks for biomedical image segmentation,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.826330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b36153fa-dcba-44e8-8bdf-9532d07df8a7 · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.748447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 729004a7-19e8-4465-8b80-20711ac438fd · outbound

This paper cites Imagenet large scale visual recognition challenge.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Imagenet large scale visual recognition challenge

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.837080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4b73c0d-abfd-4f16-9804-4442d898afc4 · outbound

This paper cites One-Shot Learning for Semantic Segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering One-Shot Learning for Semantic Segmentation

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.842240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93673dc2-4179-4e88-95be-1f1ffff09c2c · outbound

This paper cites Ssformer: A lightweight transformer for semantic segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Ssformer: A lightweight transformer for semantic segmentation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.717481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0a4a92fe-768a-4a0e-9fb0-fe09c4f7c0a6 · outbound

This paper cites Object detection and instance segmentation in remote sensing imagery based on precise mask r-cnn.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Object detection and instance segmentation in remote sensing imagery based on precise mask r-cnn

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.694452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 59f0e869-9863-4235-9edb-d4802e1ade51 · outbound

This paper cites Topological structural analysis of dig- itized binary images by border following.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Topological structural analysis of dig- itized binary images by border following

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.858706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb4452fe-ab36-48e7-a957-e4304e610bf9 · outbound

This paper cites Emergent correspondence from image diffusion.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Emergent correspondence from image diffusion

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.864544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c447c5de-fc9b-41b5-835a-b2720f9a93bf · outbound

This paper cites A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.648253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4c1f311c-e597-44b5-9169-1f20e37cbf9e · outbound

This paper cites Prior guided feature enrich- ment network for few-shot segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Prior guided feature enrich- ment network for few-shot segmentation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.626983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dde4a7b5-a4c0-4107-8144-fe75ec292b91 · outbound

This paper cites Panet: Few-shot image semantic seg- mentation with prototype alignment.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Panet: Few-shot image semantic seg- mentation with prototype alignment

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.607566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2f670d7e-ccf3-4986-bee4-7f1df3ad2c3b · outbound

This paper cites Images speak in images: A generalist painter for in-context visual learning.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Images speak in images: A generalist painter for in-context visual learning

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.588566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 79487b82-5429-4144-98df-4129d78e03be · outbound

This paper cites Seggpt: Towards seg- menting everything in context.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Seggpt: Towards seg- menting everything in context

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.570026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 764173d1-e17e-45b9-9a05-db443b99f646 · outbound

This paper cites Con- vnext v2: Co-designing and scaling convnets with masked autoencoders.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Con- vnext v2: Co-designing and scaling convnets with masked autoencoders

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.550929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4f565554-5f7f-46b9-a3d2-78b1754f775d · outbound

This paper cites Group normalization.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Group normalization

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.902668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a816a476-2361-4a37-820f-4258d887d26a · outbound

This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.908071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abfd93b8-a21d-48bd-90c5-0ae0cbfea43a · outbound

This paper cites Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.913494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efaa0ab3-7273-4064-bc0f-c779063be6f6 · outbound

This paper cites Improving the generalization of segmentation foundation model under distribution shift via weakly supervised adaptation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Improving the generalization of segmentation foundation model under distribution shift via weakly supervised adaptation

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.504989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 866c36f0-07a3-4c23-b9cf-b21159753c41 · outbound

This paper cites Feature- proxy transformer for few-shot segmentation.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Feature- proxy transformer for few-shot segmentation

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.485793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f30d7533-574b-42a1-9d19-59e31452f4a6 · outbound

This paper cites Personalize Segment Anything Model with One Shot.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Personalize Segment Anything Model with One Shot

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.928664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5063513a-72c9-432e-af8b-d369549868e5 · outbound

This paper cites Pyramid scene parsing network.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Pyramid scene parsing network

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.934267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfc98228-9109-4ca5-baa8-342bc337c6f9 · outbound

This paper cites A rapid and realistic 3d stratigraphic model generator con- ditioned on reference well log data.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering A rapid and realistic 3d stratigraphic model generator con- ditioned on reference well log data

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.454915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1917d0c3-b7eb-419a-b33e-bba63efd26d7 · outbound

This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.944845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96ee4995-8e19-4d4b-a315-b94960a55852 · outbound

This paper cites ing object boundaries, differentiating instances, and group- ing semantic regions.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering ing object boundaries, differentiating instances, and group- ing semantic regions

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.435552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 03150c7c-117b-4217-8674-3d6af9e084c0 · outbound

This paper cites SAM is designed to gen- erate a valid mask for any prompt, even ambiguous ones.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering SAM is designed to gen- erate a valid mask for any prompt, even ambiguous ones

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:24.416316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 050302fd-709c-4335-a2b7-570a2411c6d9 · outbound

This paper cites 2, 3, 6, 8, 9.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering 2, 3, 6, 8, 9

Reference 462

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:27:25.278687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers