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Paper Citation Record · LEDGER

Multimodal SAM-adapter for Semantic Segmentation

As of 20 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2509.10408.

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

pith.paper-citation-record.v1
2509.10408 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:57:10.303017Z

measured 51 of 51 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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51 of 51 outbound references displayed

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Outbound references

Observation 7a347fee-22db-4bd8-9b08-f49160e3a192 · outbound

This paper cites Layer Normalization.

Multimodal SAM-adapter for Semantic Segmentation Layer Normalization

Reference 1

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Observation 321ea4a8-aa7b-4ba6-a1fc-0a6ff5d5630d · outbound

This paper cites Muses: The multi- sensor semantic perception dataset for driving un- der uncertainty.

Multimodal SAM-adapter for Semantic Segmentation Muses: The multi- sensor semantic perception dataset for driving un- der uncertainty

Reference 2

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Observation 8d2299ab-02ac-4355-99ef-1496ca893c8b · outbound

This paper cites Cafuser: Condition-aware mul- timodal fusion for robust semantic perception of driving scenes.IEEE Robotics and Automation Letters, 10(4):3134–3141, 2025.

Multimodal SAM-adapter for Semantic Segmentation Cafuser: Condition-aware mul- timodal fusion for robust semantic perception of driving scenes.IEEE Robotics and Automation Letters, 10(4):3134–3141, 2025

Reference 3

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Observation b26ff9ed-ab65-4d90-8eec-76772bcc0518 · outbound

This paper cites Collaborative compensative trans- former network for salient object detection.Pattern Recognition, 154:110600, 2024.

Multimodal SAM-adapter for Semantic Segmentation Collaborative compensative trans- former network for salient object detection.Pattern Recognition, 154:110600, 2024

Reference 4

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Observation 10b016f4-dd05-416a-99f8-cfe7d4c2ed01 · outbound

This paper cites SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More.

Multimodal SAM-adapter for Semantic Segmentation SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 5

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source=pdf_text observed=2026-08-04T17:57:10.197542Z digest=sha256:9956655d95ab34a78399636aeec0f546c6249ec7ca2f48adb4be3d12b6b7d0ea

Observation ef1c3248-cc62-4eb5-ae67-4aa45eceb3e0 · outbound

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

Multimodal SAM-adapter for Semantic Segmentation Sam- adapter: Adapting segment anything in underper- formed scenes

Reference 6

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Observation 2161ec4c-33da-43f1-80c4-97cd035e5390 · outbound

This paper cites Vision transformer adapter for dense predictions.

Multimodal SAM-adapter for Semantic Segmentation Vision transformer adapter for dense predictions

Reference 7

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source=pdf_text observed=2026-08-04T17:57:10.203558Z digest=sha256:93d7019ce6ee7d73104d54daf15058bc2f7d59df3a79db925b2eb9c47fd42b59

Observation ffbe9424-33be-486b-b66e-6f15318b9758 · outbound

This paper cites Segment any event streams via weighted adaptation of piv- otal tokens.

Multimodal SAM-adapter for Semantic Segmentation Segment any event streams via weighted adaptation of piv- otal tokens

Reference 8

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Observation 8c9c4d75-18c7-40d4-a1c8-5299140f9a16 · outbound

This paper cites Schwing, Alexander Kirillov, and Rohit Girdhar.

Multimodal SAM-adapter for Semantic Segmentation Schwing, Alexander Kirillov, and Rohit Girdhar

Reference 9

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Observation 9587c8bd-cda9-4193-b365-0ce82db1a7d1 · outbound

This paper cites Schwing, and Alexander Kirillov.

Multimodal SAM-adapter for Semantic Segmentation Schwing, and Alexander Kirillov

Reference 10

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source=pdf_text observed=2026-08-04T17:57:10.210287Z digest=sha256:ddde1b414216731ba8a98394213b8295f3e9146ae6e0ec35d301c177e2c44acd

Observation 4ff29033-4b1c-49c1-bb6b-7118102e7bfe · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3213–3223, 2016.

Multimodal SAM-adapter for Semantic Segmentation The cityscapes dataset for semantic urban scene understanding.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3213–3223, 2016

Reference 11

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Observation 6e0a3a0e-2d4f-4290-9ec7-9f7e61785b99 · outbound

This paper cites Indoor Semantic Segmentation using depth information.

Multimodal SAM-adapter for Semantic Segmentation Indoor Semantic Segmentation using depth information

Reference 12

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source=pdf_text observed=2026-08-04T17:57:10.214744Z digest=sha256:f2b905beb47e4e9f697f9c88d2657ae0ddb53da5181735d8a0534b81df7fa194

Observation 14aa2558-9aad-4a5a-b0b4-4da990cd9c00 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Multimodal SAM-adapter for Semantic Segmentation Imagenet: A large-scale hierarchical image database

Reference 13

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source=pdf_text observed=2026-08-04T17:57:10.217238Z digest=sha256:43c7d5bbdc648789f26de85ff536cfa150a4e14376956bcff9258d6fc83cba0c

Observation afa845c1-4515-4aef-a7e1-0fb2a2eac19c · outbound

This paper cites Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning.

Multimodal SAM-adapter for Semantic Segmentation Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning

Reference 14

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source=pdf_text observed=2026-08-04T17:57:10.219394Z digest=sha256:59413a410b5e3a6f9e6a0531063447a7d2ffd0a53d71d3ceb9a4a7fa2e682947

Observation 24009656-2c0f-42c1-ae18-1aaeb7b35a1e · outbound

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

Multimodal SAM-adapter for Semantic Segmentation An image is worth 16x16 words: Transformers for image recognition at scale

Reference 15

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source=pdf_text observed=2026-08-04T17:57:10.221729Z digest=sha256:d9bbc81ea4ce2d573cbbb6b348e95e2ea71a90d0d7ae25ee9e7a4520616e470b

Observation d4ffa69b-a290-4283-8e8a-afb9eea561ea · outbound

This paper cites Mfnet: Towards real-time semantic segmentation for au- tonomous vehicles with multi-spectral scenes.

Multimodal SAM-adapter for Semantic Segmentation Mfnet: Towards real-time semantic segmentation for au- tonomous vehicles with multi-spectral scenes

Reference 16

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Observation 5af5a4f5-2dd9-4ee7-9551-fc2f0b2ac079 · outbound

This paper cites A survey on instance segmentation: state of the art.

Multimodal SAM-adapter for Semantic Segmentation A survey on instance segmentation: state of the art

Reference 17

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source=pdf_text observed=2026-08-04T17:57:10.226067Z digest=sha256:3af407df9ec9fa594cbacf56c419887a38e1685ed942fdcc020c540970eb6c30

Observation 6e8a6288-2c2d-4e71-9f30-7910b71dabe2 · outbound

This paper cites Fusenet: Incorporating depth into semantic segmentation via fusion-based cnn architecture.

Multimodal SAM-adapter for Semantic Segmentation Fusenet: Incorporating depth into semantic segmentation via fusion-based cnn architecture

Reference 18

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Observation 01bcface-043d-41b7-b0fa-1fc4fa44f862 · outbound

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

Multimodal SAM-adapter for Semantic Segmentation Masked au- toencoders are scalable vision learners

Reference 19

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source=pdf_text observed=2026-08-04T17:57:10.230271Z digest=sha256:ddb50de3e505d6e12d56a7198c1a4f0ff9473c107a78086a8962293067203e9a

Observation 5b6c5c2f-fa9d-4b74-8902-b85958b2699d · outbound

This paper cites Parameter-efficient transfer learning for NLP.

Multimodal SAM-adapter for Semantic Segmentation Parameter-efficient transfer learning for NLP

Reference 20

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source=pdf_text observed=2026-08-04T17:57:10.232394Z digest=sha256:64e76ad0026b358e655b9d5485c506b75511a109c169a4f80b06313e8fd83030

Observation 7805319b-164c-4085-8c3d-e8adddebd491 · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

Multimodal SAM-adapter for Semantic Segmentation Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 21

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source=pdf_text observed=2026-08-04T17:57:10.234871Z digest=sha256:48aec53c58067ef2def64e6275089e5617f38e3cbf9e4ac688c68f0879f26d90

Observation 8f1a1af5-46f5-4160-aba9-c778f5cca0ee · outbound

This paper cites Roadformer+: Delivering rgb-x scene parsing through scale-aware information decou- pling and advanced heterogeneous feature fusion.

Multimodal SAM-adapter for Semantic Segmentation Roadformer+: Delivering rgb-x scene parsing through scale-aware information decou- pling and advanced heterogeneous feature fusion

Reference 22

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source=pdf_text observed=2026-08-04T17:57:10.237025Z digest=sha256:dad86413531da243149a6715aeb0e9e3821404c8616444103a1b5966edd6a634

Observation c52c5a3c-426e-4b1d-a1b9-785491ca7b29 · outbound

This paper cites OneFormer: One Transformer to Rule Universal Image Segmenta- tion.

Multimodal SAM-adapter for Semantic Segmentation OneFormer: One Transformer to Rule Universal Image Segmenta- tion

Reference 23

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source=pdf_text observed=2026-08-04T17:57:10.239078Z digest=sha256:026e517eca70853e7cd959f37279edd88b9da8fa54961a5edb4326b9ffa77f11

Observation dbb802b1-44af-4d7a-b472-6ac5f4943948 · outbound

This paper cites Gemini- Fusion: Efficient pixel-wise multimodal fusion for vision transformer.

Multimodal SAM-adapter for Semantic Segmentation Gemini- Fusion: Efficient pixel-wise multimodal fusion for vision transformer

Reference 24

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source=pdf_text observed=2026-08-04T17:57:10.241304Z digest=sha256:6e08958ef7f18b1ce1ff0e87e18258fc50e1749931df2b63a109a07e8e242549

Observation c63e47d3-4580-448f-b9bb-c545acac4d55 · outbound

This paper cites Attention enhanced machine instinctive vision with human-inspired saliency detection.Image and Vision Computing, 152:105308, 2024.

Multimodal SAM-adapter for Semantic Segmentation Attention enhanced machine instinctive vision with human-inspired saliency detection.Image and Vision Computing, 152:105308, 2024

Reference 25

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source=pdf_text observed=2026-08-04T17:57:10.243502Z digest=sha256:e1a30965b47009133c2728fe189aeb0976c347db05d966324e4f7e5113c7974b

Observation 13a48c4b-9b69-475e-b3fb-cf79660adc78 · outbound

This paper cites Berg, Wan-Yen Lo, Piotr Dollar, and Ross Gir- shick.

Multimodal SAM-adapter for Semantic Segmentation Berg, Wan-Yen Lo, Piotr Dollar, and Ross Gir- shick

Reference 26

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source=pdf_text observed=2026-08-04T17:57:10.245592Z digest=sha256:eb2a030f9b1a45e5147a7097d74ca9f7dddbae3490d877ed7a1fa7f81f0a9b82

Observation bff41114-5f22-4ec9-bd92-f5d60a20d825 · outbound

This paper cites StitchFusion: Weaving Any Visual Modalities to Enhance Multimodal Semantic Segmentation.

Multimodal SAM-adapter for Semantic Segmentation StitchFusion: Weaving Any Visual Modalities to Enhance Multimodal Semantic Segmentation

Reference 27

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source=pdf_text observed=2026-08-04T17:57:10.247701Z digest=sha256:d58f06083c53b5ac8fbc7c41472670ba9d01ac89a64fcea2df98c2d6f93617bd

Observation 10295fea-fd74-43cf-9b98-a0bfe513a6f1 · outbound

This paper cites Roadformer: Duplex transformer for rgb-normal semantic road scene parsing.IEEE Transactions on Intelligent Vehicles, 2024.

Multimodal SAM-adapter for Semantic Segmentation Roadformer: Duplex transformer for rgb-normal semantic road scene parsing.IEEE Transactions on Intelligent Vehicles, 2024

Reference 28

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source=pdf_text observed=2026-08-04T17:57:10.250484Z digest=sha256:5da1538b71094ab4fb5cd1eacb02c36d5fbb1cba6571444b82da05d864cd7bf8

Observation 5287e19c-3f4c-42aa-8e6d-c9397b8c784e · outbound

This paper cites Multi-interactive feature learning and a full- time multi-modality benchmark for image fusion and segmentation.

Multimodal SAM-adapter for Semantic Segmentation Multi-interactive feature learning and a full- time multi-modality benchmark for image fusion and segmentation

Reference 29

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source=pdf_text observed=2026-08-04T17:57:10.252838Z digest=sha256:8395dec9f2ce92cfcdcd956807e61e806467ba611011df1a5a25ee0cee26cfad

Observation 2ffc6ece-79bf-48fd-a552-5ceb07405b8e · outbound

This paper cites Segmenting anything in the dark via depth perception.IEEE Transactions on Multime- dia, pages 1–12, 2025.

Multimodal SAM-adapter for Semantic Segmentation Segmenting anything in the dark via depth perception.IEEE Transactions on Multime- dia, pages 1–12, 2025

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source=pdf_text observed=2026-08-04T17:57:10.255063Z digest=sha256:380b1a9f7c448ca617a6a18435a86b583fbc644d3972adacfef02f69b8e6184b

Observation c5fea37f-8238-43df-aa41-f3988d9643af · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Multimodal SAM-adapter for Semantic Segmentation Swin transformer: Hierarchical vision transformer using shifted windows

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source=pdf_text observed=2026-08-04T17:57:10.257218Z digest=sha256:6921235fe0a1e140df9f87c0d58ef0f546e2a856073ae8c621f6ceee0ae61602

Observation 5992e257-fcb6-49f8-8eb5-100c7c6567e3 · outbound

This paper cites A convnet for the 2020s.Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

Multimodal SAM-adapter for Semantic Segmentation A convnet for the 2020s.Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022

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source=pdf_text observed=2026-08-04T17:57:10.259399Z digest=sha256:f20906d29e762c15ddbc224821c32afefe6dc7afbccdcecac2d186957b1db1ea

Observation c126f55d-efe6-4849-b253-45b5d7c9c018 · outbound

This paper cites Decoupled weight decay regularization.

Multimodal SAM-adapter for Semantic Segmentation Decoupled weight decay regularization

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source=pdf_text observed=2026-08-04T17:57:10.261526Z digest=sha256:2f0f9a0200a734ed53dcd98cb046e22ab9fec1f251236122b538da134007e183

Observation 2d1f97ab-94f3-4564-848e-9ba6dcd619ad · outbound

This paper cites Image segmentation using deep learn- ing: A survey.IEEE transactions on pattern anal- ysis and machine intelligence, 44(7):3523–3542, 2021.

Multimodal SAM-adapter for Semantic Segmentation Image segmentation using deep learn- ing: A survey.IEEE transactions on pattern anal- ysis and machine intelligence, 44(7):3523–3542, 2021

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source=pdf_text observed=2026-08-04T17:57:10.263645Z digest=sha256:1206d91cd539c6499459849ba69d41b99cf0a02f40968f93df285291d950ed99

Observation f9264136-54ee-45f1-a977-77d6d5f7c882 · outbound

This paper cites SAM 2: Segment any- thing in images and videos.

Multimodal SAM-adapter for Semantic Segmentation SAM 2: Segment any- thing in images and videos

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source=pdf_text observed=2026-08-04T17:57:10.265986Z digest=sha256:87efaf78485295cf570adebba08cc030110ff5e6505f98bbb597c20ca05df204

Observation 32733f63-bd37-4c62-b959-6b3275abae41 · outbound

This paper cites SU-SAM: A Simple Unified Framework for Adapting Segment Anything Model in Underperformed Scenes.

Multimodal SAM-adapter for Semantic Segmentation SU-SAM: A Simple Unified Framework for Adapting Segment Anything Model in Underperformed Scenes

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source=pdf_text observed=2026-08-04T17:57:10.268219Z digest=sha256:708c39232a0c95f2f6890ded0b7b586f0096da6fe7f785f83e4a9b3fdaf4137d

Observation fc436ae8-5d0b-4d19-bcaf-486117b5da63 · outbound

This paper cites Rtfnet: Rgb-thermal fusion network for semantic segmen- tation of urban scenes.IEEE Robotics and Au- tomation Letters, 4(3):2576–2583, 2019.

Multimodal SAM-adapter for Semantic Segmentation Rtfnet: Rgb-thermal fusion network for semantic segmen- tation of urban scenes.IEEE Robotics and Au- tomation Letters, 4(3):2576–2583, 2019

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source=pdf_text observed=2026-08-04T17:57:10.270722Z digest=sha256:f4d3d300f85111c503484433cca183c14125a05d4371fa19db166862f2a991c7

Observation 230a0912-8149-4038-a4b9-d6d09373d600 · outbound

This paper cites Semantic segmentation using vision transformers: A survey.

Multimodal SAM-adapter for Semantic Segmentation Semantic segmentation using vision transformers: A survey

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no resolver link, observed 2026-08-04T17:57:10.272981Z

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source=pdf_text observed=2026-08-04T17:57:10.272981Z digest=sha256:ef45d97bdd1d48c6e0f820298f6a20d8be2b7e17bdac77c12a250a2e91433645

Observation 2082cd3f-be90-465e-84f5-8346df53bdd3 · outbound

This paper cites Adapting Segment Anything Model to Multi-modal Salient Object Detection with Semantic Feature Fusion Guidance.

Multimodal SAM-adapter for Semantic Segmentation Adapting Segment Anything Model to Multi-modal Salient Object Detection with Semantic Feature Fusion Guidance

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no resolver link, observed 2026-08-04T17:57:10.275121Z

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source=pdf_text observed=2026-08-04T17:57:10.275121Z digest=sha256:9771f093e9fb4875138747fee2742409f56739b86cb00cfa5f967fc31c9d0072

Observation 834e7e1b-6041-4db0-a64c-9c8b88137669 · outbound

This paper cites Multi- modal token fusion for vision transformers.

Multimodal SAM-adapter for Semantic Segmentation Multi- modal token fusion for vision transformers

Reference 40

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no resolver link, observed 2026-08-04T17:57:10.277486Z

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source=pdf_text observed=2026-08-04T17:57:10.277486Z digest=sha256:f642db67865922557bf133cd9de249388be6fa4720d7c866662442ee430e1ddc

Observation e11ff524-d44d-4b46-b372-17654bd44545 · outbound

This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.

Multimodal SAM-adapter for Semantic Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

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no resolver link, observed 2026-08-04T17:57:10.279609Z

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source=pdf_text observed=2026-08-04T17:57:10.279609Z digest=sha256:db711b808e88806e5ba71186f50881aecd4542327e5dbe790b844738f160a1a3

Observation 0679f528-db9c-4605-b29c-069c9e0a19ba · outbound

This paper cites Segment Anything with Multiple Modalities.

Multimodal SAM-adapter for Semantic Segmentation Segment Anything with Multiple Modalities

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no resolver link, observed 2026-08-04T17:57:10.281980Z

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source=pdf_text observed=2026-08-04T17:57:10.281980Z digest=sha256:7e07ea8c96f1cfadb4a1f69f32708df17b666c42bd314de77df3140c8ac5d40e

Observation 56391f36-4c52-4d92-a294-5cfd60b7218b · outbound

This paper cites Seg- former: Simple and efficient design for semantic segmentation with transformers.

Multimodal SAM-adapter for Semantic Segmentation Seg- former: Simple and efficient design for semantic segmentation with transformers

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no resolver link, observed 2026-08-04T17:57:10.284408Z

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source=pdf_text observed=2026-08-04T17:57:10.284408Z digest=sha256:08409874f40c1cae0d17d6dab2b17b64dbf4081521014a0cb2fa78a466a16be7

Observation 22e54170-aa7d-43a2-9e10-e363942b4729 · outbound

This paper cites Parameter-efficient fine-tuning for pre- trained vision models: A survey.arXiv preprint arXiv:2402.02242, 2024.

Multimodal SAM-adapter for Semantic Segmentation Parameter-efficient fine-tuning for pre- trained vision models: A survey.arXiv preprint arXiv:2402.02242, 2024

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source=pdf_text observed=2026-08-04T17:57:10.287014Z digest=sha256:fae8c08fac4514133601b334b4257ca549f8978f4bc3fcc4ccaef0f70cd4a6d4

Observation e72d7695-c7bd-40df-9a41-d37a777a0e66 · outbound

This paper cites Sam-event-adapter: Adapting segment anything model for event-rgb se- mantic segmentation.

Multimodal SAM-adapter for Semantic Segmentation Sam-event-adapter: Adapting segment anything model for event-rgb se- mantic segmentation

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no resolver link, observed 2026-08-04T17:57:10.289144Z

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source=pdf_text observed=2026-08-04T17:57:10.289144Z digest=sha256:56eecffd89817455cf089e1ce8e8e57e6d79cd9fb3072c064729d78f762bb3dc

Observation e7fa07fc-aeb5-4860-ae5d-b375f634f202 · outbound

This paper cites Zeiler, Dilip Krishnan, Geoffrey W.

Multimodal SAM-adapter for Semantic Segmentation Zeiler, Dilip Krishnan, Geoffrey W

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no resolver link, observed 2026-08-04T17:57:10.291313Z

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source=pdf_text observed=2026-08-04T17:57:10.291313Z digest=sha256:cc66ce1eda7b17132c3acdcbac3eda5a8923abfde43a262f667ff5cea1b5ef90

Observation 9a39c179-554c-4663-8330-f54d22db7bae · outbound

This paper cites Cmx: Cross-modal fusion for rgb-x semantic segmen- tation with transformers.IEEE Transactions on Intelligent Transportation Systems, 2023.

Multimodal SAM-adapter for Semantic Segmentation Cmx: Cross-modal fusion for rgb-x semantic segmen- tation with transformers.IEEE Transactions on Intelligent Transportation Systems, 2023

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no resolver link, observed 2026-08-04T17:57:10.294088Z

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source=pdf_text observed=2026-08-04T17:57:10.294088Z digest=sha256:6f7645dc6048af4352d0b3aebeed15b2b90775a9aa2a53721e8e4b7f632f4942

Observation 1a73dad3-2c41-4073-afdc-9abc20864828 · outbound

This paper cites Delivering arbitrary-modal semantic segmentation.

Multimodal SAM-adapter for Semantic Segmentation Delivering arbitrary-modal semantic segmentation

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no resolver link, observed 2026-08-04T17:57:10.296256Z

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source=pdf_text observed=2026-08-04T17:57:10.296256Z digest=sha256:fa0db88e03e93b63d7182aa67f7265039dc2ea4f5915d517d8cafd69ca1fd914

Observation 749ab53f-542a-47d8-a558-4616d11995ab · outbound

This paper cites Deep multimodal fusion for semantic image segmentation: A survey.Image and Vision Computing, 105:104042, 2021.

Multimodal SAM-adapter for Semantic Segmentation Deep multimodal fusion for semantic image segmentation: A survey.Image and Vision Computing, 105:104042, 2021

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source=pdf_text observed=2026-08-04T17:57:10.298400Z digest=sha256:818860a1dc086f14bd9af368a9518bd61ef931533f59ba7712b39e9d94fe4932

Observation 4b1635b9-56b0-4d54-be00-68dd0d04c5c0 · outbound

This paper cites Deformable{detr}: Deformable transformers for end-to-end object de- tection.

Multimodal SAM-adapter for Semantic Segmentation Deformable{detr}: Deformable transformers for end-to-end object de- tection

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no resolver link, observed 2026-08-04T17:57:10.300803Z

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source=pdf_text observed=2026-08-04T17:57:10.300803Z digest=sha256:da8effdb6eb6b245d57ff30d63abf715a35a3903987a03b20ecbfffd26a0b8da

Observation 77138987-85c1-44ac-9295-184ca60b8ac8 · outbound

This paper cites Segment everything every- where all at once.Advances in Neural Information Processing Systems, 36, 2024.

Multimodal SAM-adapter for Semantic Segmentation Segment everything every- where all at once.Advances in Neural Information Processing Systems, 36, 2024

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source=pdf_text observed=2026-08-04T17:57:10.303017Z digest=sha256:5f251ec0e11f4df4ccd357be316fcc8eae70f99bcaeb5fc296603d865e6e7d3e

Pith citing papers

No inbound Pith citation observations are available.