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Paper Citation Record · LEDGER

Deformable Mamba for Wide Field of View Segmentation

As of 23 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 5 inbound Pith citation observations for arXiv:2411.16481.

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

pith.paper-citation-record.v1
2411.16481 v2

Coverage vector

measured 76 of 76 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:06:35.606135Z

measured 81 of 81 standing notices

One-hop event checks from named stored sources.

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

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:43:12.722696Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T20:03:56.687356Z

Reference resolution

76 of 76 outbound references displayed

  • verified exact2
  • verified fuzzy42
  • unresolved31
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 72172909-abbc-4c6c-a548-7b8ca6fc6c7b · outbound

This paper cites Joint 2D-3D-Semantic Data for Indoor Scene Understanding.

Deformable Mamba for Wide Field of View Segmentation Joint 2D-3D-Semantic Data for Indoor Scene Understanding

Reference 1

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Observation b3cd30f5-87e5-4960-9f26-6eb4a931050c · outbound

This paper cites Segnet: A deep convolutional encoder-decoder architecture for image segmentation.

Deformable Mamba for Wide Field of View Segmentation Segnet: A deep convolutional encoder-decoder architecture for image segmentation

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 7dfdf7d1-3b43-41e4-8b9f-02466e09ad75 · outbound

This paper cites Matterport3D: Learning from RGB-D Data in Indoor Environments.

Deformable Mamba for Wide Field of View Segmentation Matterport3D: Learning from RGB-D Data in Indoor Environments

Reference 3

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Observation d02c72a5-7538-4f84-9323-06c34e8eea9c · outbound

This paper cites Rsmamba: Remote sens- ing image classification with state space model.

Deformable Mamba for Wide Field of View Segmentation Rsmamba: Remote sens- ing image classification with state space model

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation c0df0306-1b09-4163-b2ca-9a57a6ce84c6 · outbound

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

Deformable Mamba for Wide Field of View Segmentation Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 5

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 42c3052e-d827-414d-9b8e-42d659cc62a2 · outbound

This paper cites CycleMLP: A MLP-like Architecture for Dense Prediction.

Deformable Mamba for Wide Field of View Segmentation CycleMLP: A MLP-like Architecture for Dense Prediction

Reference 6

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Observation bfb19ac4-44da-4dc1-be05-75c51cfe99aa · outbound

This paper cites Cyclemlp: A mlp-like architecture for dense visual predictions.

Deformable Mamba for Wide Field of View Segmentation Cyclemlp: A mlp-like architecture for dense visual predictions

Reference 7

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Observation 66c5bc58-b74a-4cd9-9a0a-003613210798 · outbound

This paper cites Semi-supervised semantic segmentation with cross pseudo supervision.

Deformable Mamba for Wide Field of View Segmentation Semi-supervised semantic segmentation with cross pseudo supervision

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 8c728bd8-78e5-49f9-8a49-414ecb2485fc · outbound

This paper cites Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks.

Deformable Mamba for Wide Field of View Segmentation Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation d4be4445-10b3-40e6-b2b5-0655a4b93f47 · outbound

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

Deformable Mamba for Wide Field of View Segmentation Per- pixel classification is not all you need for semantic segmen- tation

Reference 10

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Observation 0df740f0-7487-4c39-8414-beb5c8b6a232 · outbound

This paper cites Deformable convolutional networks.

Deformable Mamba for Wide Field of View Segmentation Deformable convolutional networks

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 8dbcc046-eba0-407f-a2fc-df0ef7354470 · outbound

This paper cites Restricted deformable convolution-based road scene semantic segmentation using surround view cam- eras.

Deformable Mamba for Wide Field of View Segmentation Restricted deformable convolution-based road scene semantic segmentation using surround view cam- eras

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 4051a41c-b74d-40ea-8791-f8d9b83962c8 · outbound

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

Deformable Mamba for Wide Field of View Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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Observation e1e20b1c-3a32-4ca0-a504-8d5d04ef8565 · outbound

This paper cites Carla: An open urban driv- ing simulator.

Deformable Mamba for Wide Field of View Segmentation Carla: An open urban driv- ing simulator

Reference 14

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Observation 2fd7d544-424d-4860-835e-5af30d94f527 · outbound

This paper cites Tangent images for mitigating spherical distortion.

Deformable Mamba for Wide Field of View Segmentation Tangent images for mitigating spherical distortion

Reference 15

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 5612b076-1561-49da-bb90-599695226de8 · outbound

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

Deformable Mamba for Wide Field of View Segmentation Dual attention network for scene seg- mentation

Reference 16

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Observation 6b09d86d-d779-4bfc-a517-f2da544f3855 · outbound

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

Deformable Mamba for Wide Field of View Segmentation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 17

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Observation 4e9b7a2d-f699-4464-9a14-b3b70ce0268f · outbound

This paper cites Multi-scale high-resolution vision transformer for se- mantic segmentation.

Deformable Mamba for Wide Field of View Segmentation Multi-scale high-resolution vision transformer for se- mantic segmentation

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.294447Z digest=sha256:7d71e056ddaf1f2f0b28747c1ab11f07df26f5a02ca3817b15b6d71caa518f32

Observation fc94b182-15ff-4064-9ca0-700bca762a09 · outbound

This paper cites Dynamic task prioritization for multitask learning.

Deformable Mamba for Wide Field of View Segmentation Dynamic task prioritization for multitask learning

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.298161Z digest=sha256:8a0fa3a3f8667c2c3e64737a3528518a5d06ac94a18b450897226742c39162b8

Observation 7ba82fa4-34fc-415d-ad84-3349a45bd25b · outbound

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

Deformable Mamba for Wide Field of View Segmentation Segnext: Rethink- ing convolutional attention design for semantic segmenta- tion

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation f31a3bb9-7c43-4ccd-a418-036f3cc5d6d8 · outbound

This paper cites Single frame se- mantic segmentation using multi-modal spherical images.

Deformable Mamba for Wide Field of View Segmentation Single frame se- mantic segmentation using multi-modal spherical images

Reference 21

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.304992Z digest=sha256:3137e76a343151bdd4320521d3aba1b2d5c8a5be98567001b6aa9220177676ea

Observation 3e58c55c-84d9-429e-ac12-7b190c616c96 · outbound

This paper cites Deep residual learning for image recognition.

Deformable Mamba for Wide Field of View Segmentation Deep residual learning for image recognition

Reference 22

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source=pdf_text observed=2026-08-12T13:06:35.308235Z digest=sha256:8cdf34d6e9285f16b55475270f7ec669314908daf286cddb8cf9b10d5d77c069

Observation a563e63b-de0b-44c3-94ad-1b034f880d5f · outbound

This paper cites ZigMa: A DiT-style Zigzag Mamba Diffusion Model.

Deformable Mamba for Wide Field of View Segmentation ZigMa: A DiT-style Zigzag Mamba Diffusion Model

Reference 23

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Observation 091f7902-3137-4d33-ab54-c51680538a4c · outbound

This paper cites LocalMamba: Visual State Space Model with Windowed Selective Scan.

Deformable Mamba for Wide Field of View Segmentation LocalMamba: Visual State Space Model with Windowed Selective Scan

Reference 24

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source=pdf_text observed=2026-08-12T13:06:35.315624Z digest=sha256:5257ebf8d7ca65ebf4e08e07f7b6017f886f8908ad19e0b433697b73eed2e97b

Observation 9ce5e4a0-7620-4968-aeb5-b383dd1d7fc4 · outbound

This paper cites Ccnet: Criss-cross attention for semantic segmentation.

Deformable Mamba for Wide Field of View Segmentation Ccnet: Criss-cross attention for semantic segmentation

Reference 25

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T13:06:35.319869Z digest=sha256:9e53a7a6ab9d80e80b0750eed5bfd479ee4dc3d408dfb9e98a6eeaf9fb121f20

Observation 230b0508-ae9c-42ce-b663-6104919c932b · outbound

This paper cites Panoramic panoptic segmentation: Towards complete sur- rounding understanding via unsupervised contrastive learn- ing.

Deformable Mamba for Wide Field of View Segmentation Panoramic panoptic segmentation: Towards complete sur- rounding understanding via unsupervised contrastive learn- ing

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.323288Z digest=sha256:85b03a428cd07dbf3321a1f4a729683b0e113bc67c596663a130e9e6329f0dbf

Observation 73af63de-63ef-4065-9eca-352d2fce0a13 · outbound

This paper cites Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics.

Deformable Mamba for Wide Field of View Segmentation Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics

Reference 27

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Observation bfb12d6c-2597-4a42-bf84-fbd141f55129 · outbound

This paper cites Imagenet classification with deep convolutional neural net- works.

Deformable Mamba for Wide Field of View Segmentation Imagenet classification with deep convolutional neural net- works

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.330531Z digest=sha256:99e25e60578f069056c28d7ce3bbc12482598bfe6ab932133d946f278b521ef7

Observation ff242144-c2a3-47de-9d28-3957a881604a · outbound

This paper cites Omnidet: Surround view cameras based multi-task visual perception network for autonomous driv- ing.

Deformable Mamba for Wide Field of View Segmentation Omnidet: Surround view cameras based multi-task visual perception network for autonomous driv- ing

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:36.185914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.333808Z digest=sha256:3accd0759a3baf5a62c0ed7ff85cff2d500749fdc54f6ee846967f87bfa4d478

Observation 91f95b12-c9b6-4d7f-9e96-d60d91046bf7 · outbound

This paper cites VideoMamba: State Space Model for Efficient Video Understanding.

Deformable Mamba for Wide Field of View Segmentation VideoMamba: State Space Model for Efficient Video Understanding

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.337073Z digest=sha256:246ceca3250157fc8c0ee2374840f60fbdbfd83fddbf38607607521a9da94f46

Observation 42e8c432-9adb-4d68-8c6e-2d2e325761bc · outbound

This paper cites SGAT4PASS: Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation.

Deformable Mamba for Wide Field of View Segmentation SGAT4PASS: Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation

Reference 31

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.340149Z digest=sha256:69249035631cc0ecab478530942fdb574e8cda3235e27a8ca46bec660f342949

Observation eda74d4b-b18c-40a0-b1b2-f900699c2e43 · outbound

This paper cites Ct- net: Context-based tandem network for semantic segmenta- tion.

Deformable Mamba for Wide Field of View Segmentation Ct- net: Context-based tandem network for semantic segmenta- tion

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:36.174514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.343040Z digest=sha256:3fdc51c7145d230fa430d40062703a16ecfedd4aa9506937ff3b121a99ceb1b0

Observation 805fde6f-b0bf-44df-ae21-a06b64d357db · outbound

This paper cites Covariance attention for semantic segmentation.

Deformable Mamba for Wide Field of View Segmentation Covariance attention for semantic segmentation

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-12T13:06:36.163677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.345663Z digest=sha256:1a781a760f0adbd47d264cfa0ad5d6544db36e7a76b3b8e609d915470b613f0a

Observation 6511eefd-16c9-4e7a-911d-b54adc61be4e · outbound

This paper cites VMamba: Visual State Space Model.

Deformable Mamba for Wide Field of View Segmentation VMamba: Visual State Space Model

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.348653Z digest=sha256:3496f7b72d6e96fe419cd208de886c5aee15bb4b510888560f3bd0978c08facc

Observation 2290a70b-b6b7-4217-916a-7c09e1282716 · outbound

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

Deformable Mamba for Wide Field of View Segmentation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.355376Z digest=sha256:db050ce135bb024bf43613605f8c4e1fd4b16125479d19c43a4f56d114d09b55

Observation 63e4d20a-6ccc-4453-8f4f-b001a8c09d71 · outbound

This paper cites Fully convolutional networks for semantic segmentation.

Deformable Mamba for Wide Field of View Segmentation Fully convolutional networks for semantic segmentation

Reference 36

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T13:06:35.358753Z digest=sha256:2c4709e9a9a2d62b51073d662e34693e91ec236dc6599dfcde0dbca368a53bc5

Observation a9264d9a-d3e9-4809-b4d6-6b2cb50d7a82 · outbound

This paper cites De- formable convolution based road scene semantic segmenta- tion of fisheye images in autonomous driving.

Deformable Mamba for Wide Field of View Segmentation De- formable convolution based road scene semantic segmenta- tion of fisheye images in autonomous driving

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:36.133859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.362590Z digest=sha256:d44f8e49d8c63f9e5873ed03e26e5556fbffd365c2c65139da07c416f70d7148

Observation 480de709-6fcf-4f80-b114-810fcce08115 · outbound

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

Deformable Mamba for Wide Field of View Segmentation Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-12T13:06:35.366451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.366451Z digest=sha256:d694ed819224811f4fc8d452e95a0a93769ac4bc16fb0070ee0a8b7d0b91de4e

Observation 0b6d763b-96eb-414e-b890-efed4d419b4b · outbound

This paper cites Seman- tic segmentation using transfer learning on fisheye images.

Deformable Mamba for Wide Field of View Segmentation Seman- tic segmentation using transfer learning on fisheye images

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:36.123926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.370321Z digest=sha256:3c0e3af1198c33cf496725843bb7f0bca8516ad453496333d148f3ad12dba2ab

Observation f7454338-7ac4-4c47-b36d-1fb2e0d6d01d · outbound

This paper cites Fish- segssl: A semi-supervised semantic segmentation frame- work for fish-eye images.

Deformable Mamba for Wide Field of View Segmentation Fish- segssl: A semi-supervised semantic segmentation frame- work for fish-eye images

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:36.114453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.373911Z digest=sha256:4f122745b4223b53f1a22b11ea151f3508ac4401d3a2f5a9cbce08cea32704f2

Observation bd1160b6-2db7-40b7-a0ce-e1872d3909c6 · outbound

This paper cites EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba.

Deformable Mamba for Wide Field of View Segmentation EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-12T13:06:35.377263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.377263Z digest=sha256:6b6f0734c6e40e98a312d9c2dbc64d80d8168dcbce9e936f0c6ea9c92ea097c2

Observation d8eac57b-03a3-48d4-a04a-754da2cbf435 · outbound

This paper cites Adaptable Deformable Convolutions for Semantic Segmentation of Fisheye Images in Autonomous Driving Systems.

Deformable Mamba for Wide Field of View Segmentation Adaptable Deformable Convolutions for Semantic Segmentation of Fisheye Images in Autonomous Driving Systems

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-08-12T13:06:35.701244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.381459Z digest=sha256:b65c896c397ee38a4a2f8363972bcf7d78515f945a325e3906ab5c3c69ae7ab0

Observation 822834e2-52c3-44ad-9319-ee0188487870 · outbound

This paper cites Fusionnet: A deep fully residual convo- lutional neural network for image segmentation in connec- tomics.

Deformable Mamba for Wide Field of View Segmentation Fusionnet: A deep fully residual convo- lutional neural network for image segmentation in connec- tomics

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:36.103529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.385268Z digest=sha256:a10290ca93b0e7fd3cc320c1def18fef3475ffb1ac68708301f6143f196c8b8a

Observation ea5fbce7-4776-475e-b997-a48f2ff945cc · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

Deformable Mamba for Wide Field of View Segmentation U- net: Convolutional networks for biomedical image segmen- tation

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-12T13:06:35.388837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.388837Z digest=sha256:d3aff3f0c754dd135b9c9cc967b9c09c66cd0d21872c6580854273f1f1b13843

Observation 1cd8db31-6dc5-49d9-9f19-13e6c1466518 · outbound

This paper cites Synwoodscape: Synthetic surround-view fisheye camera dataset for autonomous driving.

Deformable Mamba for Wide Field of View Segmentation Synwoodscape: Synthetic surround-view fisheye camera dataset for autonomous driving

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:36.086289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.392501Z digest=sha256:5f684014df5941ed1f47d429dc3190d21c90b1684cc901dc02c2ced91961d6e8

Observation ce0c9042-1d89-4961-9dab-9a2e569eef2e · outbound

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

Deformable Mamba for Wide Field of View Segmentation Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-12T13:06:35.396550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.396550Z digest=sha256:d76bbd7fbd618522d06ef8fc185c91fcec101e3e91a62baceb49121e25f4a64c

Observation 9f2a3277-d506-48f3-8ed5-8d65e6532a11 · outbound

This paper cites Segmenter: Transformer for semantic segmenta- tion.

Deformable Mamba for Wide Field of View Segmentation Segmenter: Transformer for semantic segmenta- tion

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:36.066787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.400373Z digest=sha256:6303880e1a692d62972e54dcc8447713bb891aa8a4d47c8d1c2aef93a71a0673

Observation 2aae2fec-b3de-482e-b46c-2d3437738644 · outbound

This paper cites Hohonet: 360 indoor holistic understanding with latent horizontal fea- tures.

Deformable Mamba for Wide Field of View Segmentation Hohonet: 360 indoor holistic understanding with latent horizontal fea- tures

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-12T13:06:35.403962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.403962Z digest=sha256:9bfbb2969723587c0c4c1440bcb38c8de71da53a11e3a933362fc273b58dd1c8

Observation d290563e-800a-489a-b2f3-d5560c5b141a · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.

Deformable Mamba for Wide Field of View Segmentation Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:36.049709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.407286Z digest=sha256:b45217409d815a6cb20c3effdf4067f3f02825fbf4c646719bd2ae070f63dae2

Observation 6074990b-4d41-4435-aae8-d1047fa1537f · outbound

This paper cites 360bev: Panoramic semantic mapping for indoor bird’s-eye view.

Deformable Mamba for Wide Field of View Segmentation 360bev: Panoramic semantic mapping for indoor bird’s-eye view

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:36.039051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.516878Z digest=sha256:fbe1063cdffacbf2b37b7bc594f2f29c48f9eb82eced791930508afe032887bd

Observation 3b1c4071-ef13-4c24-88b2-b13d5ae28cdc · outbound

This paper cites Attention is all you need.

Deformable Mamba for Wide Field of View Segmentation Attention is all you need

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-12T13:06:35.520934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.520934Z digest=sha256:a401d05f297640ea8641bbe565a3d8e41d3ab791476e23e1ad09bdffca5df735

Observation 3093c450-1291-45d8-ab49-d9382f8b5f90 · outbound

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

Deformable Mamba for Wide Field of View Segmentation Pvt v2: Improved baselines with pyramid vision transformer

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:36.021734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.524152Z digest=sha256:90a87c2a1649e434fa720489cfdc87c0e74efb694bf1efc8e86e1f7024600328

Observation 44f5b717-821f-4f0e-a575-2b151ad8d97a · outbound

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

Deformable Mamba for Wide Field of View Segmentation Internimage: Exploring large-scale vi- sion foundation models with deformable convolutions

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:36.011332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.527188Z digest=sha256:94e99800558516d13a7940007b9e0d8eeec1913766237865b2fb23ecd6801b62

Observation 85af49b4-c9c9-44e4-acb3-f0e5601e1f80 · outbound

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

Deformable Mamba for Wide Field of View Segmentation Unified perceptual parsing for scene understand- ing

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:36.000964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.530283Z digest=sha256:61d7dae39342623e304ec0d1f10ba7405ba1b0196fb2ae8df08a61df71ede50f

Observation cc460209-30bd-4493-bceb-948c7c2b700b · outbound

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

Deformable Mamba for Wide Field of View Segmentation Segformer: Simple and efficient design for semantic segmentation with transform- ers

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.991733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.533398Z digest=sha256:51ac70ac7d46bff6827c20c79a31aa07f3038234953ea98e95bbd37e3dbd3a1f

Observation 75993d12-f613-49dc-ac87-860236d85369 · outbound

This paper cites Efficient deformable convnets: Rethinking dynamic and sparse operator for vision applications.

Deformable Mamba for Wide Field of View Segmentation Efficient deformable convnets: Rethinking dynamic and sparse operator for vision applications

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.980473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.536872Z digest=sha256:39ca1b3a0e98d50253a8657198b809d0eeefd97707dcae3d91c9d0a2ac4bae78

Observation 581cc1be-351b-48e8-b6e8-dcd8e30bbdb1 · outbound

This paper cites MUSTER: A Multi-scale Transformer-based Decoder for Semantic Segmentation.

Deformable Mamba for Wide Field of View Segmentation MUSTER: A Multi-scale Transformer-based Decoder for Semantic Segmentation

Reference 57

Resolution
verified exact
local_arxiv, observed 2026-08-12T13:06:35.685322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.540498Z digest=sha256:29b7a8ab2ce4a6bb24a2df91db5e0a5b5201ad379b78d72968defc4a27955e16

Observation aa586047-9564-4666-8dff-9aed63f1de90 · outbound

This paper cites PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition.

Deformable Mamba for Wide Field of View Segmentation PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-12T13:06:35.544812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.544812Z digest=sha256:31d8b99d43d90494a3850348828d7b3df6caa0b1668660d1173ee28afa9ee63f

Observation f13c5187-8768-46e1-890e-c68d399a24a5 · outbound

This paper cites Can we pass beyond the field of view? panoramic annular semantic segmentation for real-world surrounding perception.

Deformable Mamba for Wide Field of View Segmentation Can we pass beyond the field of view? panoramic annular semantic segmentation for real-world surrounding perception

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.969608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.548680Z digest=sha256:5281f7121a6845a04099490e02b35a4d848c188ebf3e477418853353807d79c1

Observation 2b083b4e-0352-4890-9354-174929f070ce · outbound

This paper cites Pass: Panoramic annular semantic seg- mentation.

Deformable Mamba for Wide Field of View Segmentation Pass: Panoramic annular semantic seg- mentation

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.958412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.552248Z digest=sha256:00d56880977ef5de5d26c738935a23d9d9b7fbe2170860478a8fb6640bc9a0df

Observation 63221f28-e180-41c2-a065-bb5c0580232a · outbound

This paper cites Vivim: a Video Vision Mamba for Medical Video Segmentation.

Deformable Mamba for Wide Field of View Segmentation Vivim: a Video Vision Mamba for Medical Video Segmentation

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-12T13:06:35.556109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.556109Z digest=sha256:aba9b0338aa01f9fd83542a852cb4ce93a2f75b3ebd38d21ac208cc25b0ede54

Observation 3eda3d1a-39a9-4d95-a4d7-0b469650f1ef · outbound

This paper cites Woodscape: A multi-task, multi-camera fisheye dataset for autonomous driving.

Deformable Mamba for Wide Field of View Segmentation Woodscape: A multi-task, multi-camera fisheye dataset for autonomous driving

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.947578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.560243Z digest=sha256:78d3e65978e79a18ee5d0c6aec9ad572a356891f34a779730f57d1b7f9e6394d

Observation f9c72d86-5a87-4703-b0d0-a706add59cf7 · outbound

This paper cites Ocnet: Object context for seman- tic segmentation.

Deformable Mamba for Wide Field of View Segmentation Ocnet: Object context for seman- tic segmentation

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.936575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.563610Z digest=sha256:9d510e90ade9ae56409b2492ec729b7625fa4970de76d803ace59a2c487aaf74

Observation 087ff6a7-5670-4251-8e26-8ab61d096649 · outbound

This paper cites Bending reality: Distortion-aware transformers for adapting to panoramic se- mantic segmentation.

Deformable Mamba for Wide Field of View Segmentation Bending reality: Distortion-aware transformers for adapting to panoramic se- mantic segmentation

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.925019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.567243Z digest=sha256:e34bc5b5552cc56e8c462702b4766521f5d194bb015b26f1d479e4f8f89fa19a

Observation e5df1ceb-cedc-4090-a5d6-a013b9c6c54c · outbound

This paper cites Behind every domain there is a shift: Adapting distortion-aware vision transformers for panoramic semantic segmentation.

Deformable Mamba for Wide Field of View Segmentation Behind every domain there is a shift: Adapting distortion-aware vision transformers for panoramic semantic segmentation

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.913664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.571270Z digest=sha256:ab1cde5bc989bd70bd3d819b75d7b1cde973392aff6989bdd6d6d9ddc2891888

Observation e657431f-da4f-4009-b643-dd58d6253b18 · outbound

This paper cites Motion mamba: Efficient and long sequence motion generation.

Deformable Mamba for Wide Field of View Segmentation Motion mamba: Efficient and long sequence motion generation

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.902627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.575252Z digest=sha256:b5a85620298152a9c92904b85652080ca8750c5064b66b40d72d92fdaae4be76

Observation 8e48dca2-66f9-42ee-8db3-da4bd12d0015 · outbound

This paper cites Materobot: Material recognition in wearable robotics for people with visual impairments.

Deformable Mamba for Wide Field of View Segmentation Materobot: Material recognition in wearable robotics for people with visual impairments

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.892686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.578810Z digest=sha256:3b93a4ca7314ace15f305624f6dae54af6d3094fb0c43bd812b43eb195c50c16

Observation d3e504b4-7920-4853-9d3a-b5b4fc62eab8 · outbound

This paper cites Open panoramic segmentation.

Deformable Mamba for Wide Field of View Segmentation Open panoramic segmentation

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.882846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.582806Z digest=sha256:fb38524d1fa9dfeefa70cf3585855539cb83d50e4c5f60920994206ecdb85892

Observation 64ae126b-a55a-4189-aa05-4d41ea2e9e01 · outbound

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

Deformable Mamba for Wide Field of View Segmentation Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-12T13:06:35.586430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.586430Z digest=sha256:60ee167cf4fc3d0951f52b6f408e14976b6a1ae0c4435c62d6100d8fabb563f7

Observation b4c53118-922a-41df-8f93-713dafe8e835 · outbound

This paper cites Semantics distortion and style matter: Towards source-free uda for panoramic segmentation.

Deformable Mamba for Wide Field of View Segmentation Semantics distortion and style matter: Towards source-free uda for panoramic segmentation

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.867498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.589938Z digest=sha256:16dacc535fb2ba2b360230e15925a6575be198c06565128a6809cfcdb2b80b51

Observation 7ad4d82c-a4bb-4f81-a2f8-009d89d6f27b · outbound

This paper cites Complementary bi-directional fea- ture compression for indoor 360deg semantic segmentation with self-distillation.

Deformable Mamba for Wide Field of View Segmentation Complementary bi-directional fea- ture compression for indoor 360deg semantic segmentation with self-distillation

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.856636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.593580Z digest=sha256:b2855222c5cf5281bcd86480ede77f147257b4ab581dac2828bc84ea99b71066

Observation 529097a8-2f77-493d-9015-e3a4d3efcbbe · outbound

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

Deformable Mamba for Wide Field of View Segmentation Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-12T13:06:35.597029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.597029Z digest=sha256:4c38e95ae35c43b99b6cb760818c1c89abee41078895a6b6f04d2a20a3348d3b

Observation c97d48c3-4500-416b-9408-058588f677b6 · outbound

This paper cites Samba: Semantic seg- mentation of remotely sensed images with state space model.

Deformable Mamba for Wide Field of View Segmentation Samba: Semantic seg- mentation of remotely sensed images with state space model

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.846009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.600116Z digest=sha256:7aaa89a785c32aa1a5ec51c75874f9c9324c83d4e21e0cde66190f3bbf9fe331

Observation 4e42676a-8a02-42aa-983f-77aff57cdcb2 · outbound

This paper cites De- formable convnets v2: More deformable, better results.

Deformable Mamba for Wide Field of View Segmentation De- formable convnets v2: More deformable, better results

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:06:35.834318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T13:06:35.603218Z digest=sha256:8d049b832a18410096e1e90d5924b3837cb70ca09326f535df694cf41f972d9b

Observation b9abda1a-5454-4cf0-9f91-3ce0094c4115 · outbound

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

Deformable Mamba for Wide Field of View Segmentation Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-12T13:06:35.606135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.606135Z digest=sha256:ebcb1fe554c54187f2c127936ed0960b31d1169dcc9a8bb7101ad20e4b63822a

Observation 57660e1e-640f-4f2f-a500-11638148d886 · outbound

This paper cites an unresolved cited work.

Deformable Mamba for Wide Field of View Segmentation Unresolved cited work

Reference 2024

Resolution
parse uncertain
no resolver link, observed 2026-08-12T13:06:35.352218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:06:35.352218Z digest=sha256:f00e6f6bbfdc33ba386747dba463af6a185cb7fcf5dd39a9606e005bba6cabe8

Pith citing papers

Observation e9cbe1e1-5b61-4cdf-b484-e61102ac6541 · inbound

More than the Sum: Panorama-Language Models for Adverse Omni-Scenes cites this paper.

More than the Sum: Panorama-Language Models for Adverse Omni-Scenes Deformable Mamba for Wide Field of View Segmentation

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-15T14:00:02.877033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-15T13:58:18.635091Z digest=sha256:f8bc721c2478c3a0213879aeec5b009a40a68e1181cfe6f1b891389a3dabacaf

Observation ef139859-4748-4315-b0f5-a4c14ca5805b · inbound

Panoramic Scene Understanding: A Survey from Distortion-Aware Engineering to Sphere-Native Modeling cites this paper.

Panoramic Scene Understanding: A Survey from Distortion-Aware Engineering to Sphere-Native Modeling Deformable Mamba for Wide Field of View Segmentation

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-06-29T20:03:56.690570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-06-29T04:35:58.372801Z digest=sha256:540b8720cda5a11464e635cf016a9c6eac96ecfff4e8af3614994739a63ef8ec

Observation 1c427092-741e-4ae6-a17f-85704acfc8e4 · inbound

Panoramic Scene Understanding: A Survey from Distortion-Aware Engineering to Sphere-Native Modeling cites this paper.

Panoramic Scene Understanding: A Survey from Distortion-Aware Engineering to Sphere-Native Modeling Deformable Mamba for Wide Field of View Segmentation

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-02T09:58:49.383667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:58:49.383667Z digest=sha256:8b80d2becee320ac99a0b8277f43b0f68fcd4c1bebf53444de2bfa72ad769660

Observation 529df281-5550-4508-a948-ef0e4a899458 · inbound

SeqLoc: Beyond the Single Frame for Cross-View Geo-Localization in Feature-Sparse Scenes cites this paper.

SeqLoc: Beyond the Single Frame for Cross-View Geo-Localization in Feature-Sparse Scenes Deformable Mamba for Wide Field of View Segmentation

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-12T00:53:07.326199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T00:53:07.326199Z digest=sha256:c207f24e0c1801468a4dbc5e93fa6896cd553d0e5d5f7fb8b60c7c80b1e16752

Observation 100f0b0d-c41f-44df-9760-43329801877b · inbound

SeqLoc: Beyond the Single Frame for Cross-View Geo-Localization in Feature-Sparse Scenes cites this paper.

SeqLoc: Beyond the Single Frame for Cross-View Geo-Localization in Feature-Sparse Scenes Deformable Mamba for Wide Field of View Segmentation

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-14T04:43:12.722696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:43:12.722696Z digest=sha256:c219778b55d3aa09c0ed27a9c8be416e3e9c0035bf99b28849a7684e5458e6b3