Paper Citation Record · LEDGER

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation

As of 12 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 2 inbound Pith citation observations for arXiv:2412.11890.

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

pith.paper-citation-record.v1
2412.11890 v2

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:32:17.683042Z

measured 69 of 69 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T03:45:07.789096Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T21:22:59.543937Z

Reference resolution

67 of 67 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1c4e6e1c-64d2-427e-b7af-d9c5c1ba32e0 · outbound

This paper cites Coco- stuff: Thing and stuff classes in context.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Coco- stuff: Thing and stuff classes in context

Reference 1

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Observation 298ae1d0-175e-46c7-b6ee-05c9c3be59c9 · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.538833Z

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Observation b95d85bc-0657-45fa-ad01-75d99f7a016b · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 3

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Observation 432f78aa-5465-4233-84ba-201e27851544 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 4

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Source-reported events for the cited work

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Observation 42f73368-9796-4894-91f4-dfab97d32531 · outbound

This paper cites Per- pixel classification is not all you need for semantic segmen- tation.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Per- pixel classification is not all you need for semantic segmen- tation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.519477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3b122fb6-8252-4f3e-b8d5-e574fb16f5d8 · outbound

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

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Masked-attention mask transformer for universal image segmentation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.505464Z

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Observation c9829c6b-e8c2-448d-a78d-4fac6f955e58 · outbound

This paper cites MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark

Reference 7

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Source-reported events for the cited work

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Observation 6203b885-79e8-48a0-b183-ae4c205a51f7 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation The cityscapes dataset for semantic urban scene understanding

Reference 8

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Observation 3b2d99aa-f028-4216-9bcb-577f8f23280f · outbound

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

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Imagenet: A large-scale hierarchical image database

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T14:32:17.420553Z

Source-reported events for the cited work

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Observation 6aa3354c-91e9-49a6-a07a-e6ffd143a5e2 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T14:32:17.425622Z

Source-reported events for the cited work

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Observation 021c1ddf-9c4e-4234-8050-81e008afc1cc · outbound

This paper cites an unresolved cited work.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Unresolved cited work

Reference 11

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Observation 3164a189-a062-45cd-8084-72075b1a07ca · outbound

This paper cites Scalable Diffusion Models with State Space Backbone.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Scalable Diffusion Models with State Space Backbone

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T14:32:17.433997Z

Source-reported events for the cited work

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Observation 8a7c181d-373d-47ad-a408-c6a6a9b53515 · outbound

This paper cites Dual attention network for scene seg- mentation.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Dual attention network for scene seg- mentation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T14:32:17.438687Z

Source-reported events for the cited work

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Observation f797b458-8602-4818-9540-35117f99c199 · outbound

This paper cites LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T14:32:17.443168Z

Source-reported events for the cited work

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Observation e7d02a35-d325-493c-a1c1-bc7059e26189 · outbound

This paper cites Is Attention Better Than Matrix Decomposition?.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Is Attention Better Than Matrix Decomposition?

Reference 15

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Source-reported events for the cited work

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Observation 34a669c8-a262-4c7f-9c0d-3aad7b77cbc8 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 16

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Source-reported events for the cited work

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Observation 38077918-3d49-48fc-bbaa-86c6df2fe391 · outbound

This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Combining recurrent, convolutional, and continuous-time models with linear state space layers

Reference 17

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Source-reported events for the cited work

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Observation 16cfb75b-6264-4bf5-9249-86c01a174407 · outbound

This paper cites Segnext: Rethink- ing convolutional attention design for semantic segmenta- tion.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Segnext: Rethink- ing convolutional attention design for semantic segmenta- tion

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.421639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9e8544e9-ff23-42cb-9407-a4b61cfc9aa9 · outbound

This paper cites Neighborhood attention transformer.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Neighborhood attention transformer

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.405091Z

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Observation c4eb850b-b44e-4058-b772-a9e73f9a8017 · outbound

This paper cites Deep residual learning for image recognition.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Deep residual learning for image recognition

Reference 20

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Source-reported events for the cited work

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Observation 12311090-576d-44ec-9b62-be680cb87eeb · outbound

This paper cites Bag of tricks for image classifica- tion with convolutional neural networks.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Bag of tricks for image classifica- tion with convolutional neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.383590Z

Source-reported events for the cited work

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Observation 54d7860a-d959-46c0-9aa7-1834723e59d2 · outbound

This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.370659Z

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Observation 4c5394e0-8e7a-40d1-8807-5d95277723be · outbound

This paper cites Panoptic feature pyramid networks.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Panoptic feature pyramid networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.357723Z

Source-reported events for the cited work

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Observation 1533cb22-0f70-47b1-be54-f83423974bec · outbound

This paper cites Mask dino: Towards a unified transformer-based framework for object detection and segmentation.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Mask dino: Towards a unified transformer-based framework for object detection and segmentation

Reference 24

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Source-reported events for the cited work

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Observation 198ac291-55be-4e79-9528-53457ee7e13e · outbound

This paper cites Re- thinking vision transformers for mobilenet size and speed.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Re- thinking vision transformers for mobilenet size and speed

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.333128Z

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Observation ea39c6b3-f546-42b8-a7f2-1d2218dd8a40 · outbound

This paper cites Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T14:32:17.495718Z

Source-reported events for the cited work

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Observation 8530181c-db2b-4ad0-84ca-39ecd8ec34d5 · outbound

This paper cites Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy

Reference 27

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Source-reported events for the cited work

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Observation 1a8ccfc5-8e67-478e-a23e-83e8a2700bba · outbound

This paper cites VMamba: Visual State Space Model.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation VMamba: Visual State Space Model

Reference 28

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Source-reported events for the cited work

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Observation e9ba2628-e942-424d-a3da-941841713f9d · outbound

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

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 29

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Observation 2657691e-959d-4bda-88df-4ba850b57f09 · outbound

This paper cites A convnet for the 2020s.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation A convnet for the 2020s

Reference 30

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Observation e7fff1fe-d464-430d-b807-26c6114402ae · outbound

This paper cites Overlock: An overview-first- look-closely-next convnet with context-mixing dynamic ker- nels.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Overlock: An overview-first- look-closely-next convnet with context-mixing dynamic ker- nels

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.296018Z

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Observation c7beb84a-ebbb-43f8-87be-2ab921643e84 · outbound

This paper cites Sparx: A sparse cross-layer connection mechanism for hierarchical vision mamba and transformer networks.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Sparx: A sparse cross-layer connection mechanism for hierarchical vision mamba and transformer networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.282540Z

Source-reported events for the cited work

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Observation ee0dbae8-2476-48d0-8dcf-49cb9f9d7a5c · outbound

This paper cites Transxnet: Learning both global and local dynamics with a dual dynamic token mixer for visual recognition.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Transxnet: Learning both global and local dynamics with a dual dynamic token mixer for visual recognition

Reference 33

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Observation e7ecaafd-c524-42a7-9ff9-c822879c8dd9 · outbound

This paper cites Content- aware token sharing for efficient semantic segmentation with vision transformers.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Content- aware token sharing for efficient semantic segmentation with vision transformers

Reference 34

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Observation 7e7d02ab-0a14-42fd-aa93-e8709ad3fa61 · outbound

This paper cites Image seg- mentation using deep learning: A survey.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Image seg- mentation using deep learning: A survey

Reference 35

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Observation 68bec338-4bba-4c46-9888-2dea44869d8f · outbound

This paper cites Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation

Reference 36

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Observation c6975fc7-0d1c-46f6-a3ff-93da17ae63f9 · outbound

This paper cites On the integration of self- attention and convolution.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation On the integration of self- attention and convolution

Reference 37

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Observation e8fa3437-675b-495f-a962-a1d2c6ab0740 · outbound

This paper cites Designing network design spaces.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Designing network design spaces

Reference 38

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Observation 150ace37-d293-4aa4-b491-4c802baf0e95 · outbound

This paper cites Vi- sion transformers for dense prediction.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Vi- sion transformers for dense prediction

Reference 39

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Observation 947a4213-2311-46f3-877b-e057a626ab99 · outbound

This paper cites VM-UNet: Vision Mamba UNet for Medical Image Segmentation.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation VM-UNet: Vision Mamba UNet for Medical Image Segmentation

Reference 40

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Observation 5518a6da-b504-42e6-abf4-5eb46a7cc596 · outbound

This paper cites Transnext: Robust foveal visual perception for vi- sion transformers.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Transnext: Robust foveal visual perception for vi- sion transformers

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.200211Z

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Observation 8e328484-aee5-483b-a80c-add010bca519 · outbound

This paper cites Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Reference 42

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Observation cd2463da-e5ea-4d12-86ad-9e2850f689d4 · outbound

This paper cites Feedformer: Revisiting transformer decoder for efficient semantic segmentation.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Feedformer: Revisiting transformer decoder for efficient semantic segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.172675Z

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Observation 13e70bc8-70e3-4bc0-8617-b50d9ffe9d6a · outbound

This paper cites Simplified State Space Layers for Sequence Modeling.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Simplified State Space Layers for Sequence Modeling

Reference 44

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Observation 2232a295-14d6-46c2-bc98-64e797a27992 · outbound

This paper cites Dynamic token pruning in plain vision transformers for semantic segmentation.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Dynamic token pruning in plain vision transformers for semantic segmentation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.160572Z

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Observation 2f804264-24b3-4d67-acd4-a5be0224c3e5 · outbound

This paper cites DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis

Reference 46

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Source-reported events for the cited work

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Observation a8caa841-c659-4394-a3bb-28694222835a · outbound

This paper cites Maxvit: Multi-axis vision transformer.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Maxvit: Multi-axis vision transformer

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.147571Z

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Observation 7f2a2d69-92f5-4e10-92d5-00dcaa6e619c · outbound

This paper cites Attention is all you need.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Attention is all you need

Reference 48

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Source-reported events for the cited work

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Observation c7b93108-43a4-4547-a34d-8b57ecef370f · outbound

This paper cites Pyramid vision transformer: A versatile backbone for dense prediction without convolutions.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Pyramid vision transformer: A versatile backbone for dense prediction without convolutions

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.126374Z

Source-reported events for the cited work

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Observation dcb9604a-520e-4b32-aa42-e4ef174fbf48 · outbound

This paper cites Pvt v2: Improved baselines with pyramid vision transformer.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Pvt v2: Improved baselines with pyramid vision transformer

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.112918Z

Source-reported events for the cited work

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Observation 6a91c39a-d14c-4f17-923e-f13125906ce7 · outbound

This paper cites Internimage: Exploring large-scale vi- sion foundation models with deformable convolutions.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Internimage: Exploring large-scale vi- sion foundation models with deformable convolutions

Reference 51

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This paper cites Pytorch image models.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Pytorch image models

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.086691Z

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This paper cites Vit-comer: Vision transformer with convolu- tional multi-scale feature interaction for dense predictions.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Vit-comer: Vision transformer with convolu- tional multi-scale feature interaction for dense predictions

Reference 53

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This paper cites Unified perceptual parsing for scene understand- ing.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Unified perceptual parsing for scene understand- ing

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.060302Z

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Observation 5460f530-8c90-4fba-99c6-cfc75b3f99cb · outbound

This paper cites Mambatree: Tree topology is all you need in state space model.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Mambatree: Tree topology is all you need in state space model

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.046554Z

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Observation 03477e03-b6ad-49f3-b004-06a47e5dc444 · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transform- ers.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Segformer: Simple and efficient design for semantic segmentation with transform- ers

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.031362Z

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Observation 05547792-d35b-43b6-9484-7dd65f8ebc10 · outbound

This paper cites Multi-scale rep- resentations by varying window attention for semantic seg- mentation.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Multi-scale rep- resentations by varying window attention for semantic seg- mentation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.016600Z

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Observation a33b30b3-1e50-48be-9571-181f99c96295 · outbound

This paper cites Lite vision trans- former with enhanced self-attention.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Lite vision trans- former with enhanced self-attention

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:18.002188Z

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Observation 4224bfc1-41d3-43b0-ab72-ef774b3d0fbb · outbound

This paper cites Learning a discriminative fea- ture network for semantic segmentation.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Learning a discriminative fea- ture network for semantic segmentation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:17.988896Z

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Observation fbb57a6f-867e-4886-90e7-f906d94f3c00 · outbound

This paper cites Embedding-free transformer with inference spatial reduction for efficient se- mantic segmentation.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Embedding-free transformer with inference spatial reduction for efficient se- mantic segmentation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:17.976096Z

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Observation a08760ee-7d1a-4580-9b2e-623402e7809e · outbound

This paper cites Object- contextual representations for semantic segmentation.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Object- contextual representations for semantic segmentation

Reference 61

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Observation 9b17a65d-c6e3-4d5d-bfb3-68ec1c6fb587 · outbound

This paper cites Point Cloud Mamba: Point Cloud Learning via State Space Model.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Point Cloud Mamba: Point Cloud Learning via State Space Model

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T14:32:17.660693Z

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Observation 5fcb4ac2-4cbe-4646-8d15-78ef65310234 · outbound

This paper cites Pyramid scene parsing network.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Pyramid scene parsing network

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:17.956197Z

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Observation 38b6c190-b6c0-49f6-89bb-4537eed566cc · outbound

This paper cites Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers

Reference 64

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Observation 388f451a-0fb2-4e14-8071-0719903ff1f5 · outbound

This paper cites Scene parsing through ade20k dataset.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Scene parsing through ade20k dataset

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T14:32:17.673822Z

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Observation bb640085-5a05-4df2-9b7b-e9b0fbcfa234 · outbound

This paper cites Biformer: Vision transformer with bi-level routing attention.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Biformer: Vision transformer with bi-level routing attention

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:32:17.928677Z

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Observation 05b07bff-fa39-40c4-be49-c163622bdc83 · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-11T14:32:17.683042Z

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Pith citing papers

Observation cf0036a1-95cc-4835-843b-e484795a72e6 · inbound

MambaPanoptic: A Vision Mamba-based Structured State Space Framework for Panoptic Segmentation cites this paper.

MambaPanoptic: A Vision Mamba-based Structured State Space Framework for Panoptic Segmentation SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:22:59.545995Z

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Observation 9ae98a44-5601-4b08-a037-6aec071469ec · inbound

DishSeg24k: A Large-Scale Benchmark for Food Segmentation with Stochastic Expert Decoding cites this paper.

DishSeg24k: A Large-Scale Benchmark for Food Segmentation with Stochastic Expert Decoding SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T03:45:07.789096Z

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