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Paper Citation Record · LEDGER

Few-Shot Object Detection via Spatial-Channel State Space Model

As of 21 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2507.15308.

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

pith.paper-citation-record.v1
2507.15308 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:39:16.294880Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

53 of 53 outbound references displayed

  • verified exact1
  • verified fuzzy38
  • unresolved14
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0c58d682-7d59-4abc-9ef4-3b4e78db08a0 · outbound

This paper cites Binary similarity few- shot object detection with modeling of hard negative samples,.

Few-Shot Object Detection via Spatial-Channel State Space Model Binary similarity few- shot object detection with modeling of hard negative samples,

Reference 1

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Observation a9c4fbdd-f8fe-457b-a1cc-2626f4e673c5 · outbound

This paper cites Temporal speciation network for few-shot object detection,.

Few-Shot Object Detection via Spatial-Channel State Space Model Temporal speciation network for few-shot object detection,

Reference 2

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Observation 83ee4366-3934-42fc-8eed-7606b8b7a3e1 · outbound

This paper cites Dual-awareness attention for few-shot object detection,.

Few-Shot Object Detection via Spatial-Channel State Space Model Dual-awareness attention for few-shot object detection,

Reference 3

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Observation ab55f944-b0dc-4c6f-b821-8ba05253096a · outbound

This paper cites SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection.

Few-Shot Object Detection via Spatial-Channel State Space Model SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection

Reference 4

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Source-reported events for the cited work

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Observation 6acfb7bf-e0f8-4459-aee8-956416945207 · outbound

This paper cites Defrcn: Decoupled faster r-cnn for few-shot object detection,.

Few-Shot Object Detection via Spatial-Channel State Space Model Defrcn: Decoupled faster r-cnn for few-shot object detection,

Reference 5

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Observation c267bebe-8b36-4308-a779-ea188475d42d · outbound

This paper cites Uncertainty-based forgetting mitigation for generalized few-shot object detection,.

Few-Shot Object Detection via Spatial-Channel State Space Model Uncertainty-based forgetting mitigation for generalized few-shot object detection,

Reference 6

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Observation cc1cef20-6f6e-45ba-8262-35f6b19f1e57 · outbound

This paper cites Ecea: Extensible co-existing attention for few-shot object detection,.

Few-Shot Object Detection via Spatial-Channel State Space Model Ecea: Extensible co-existing attention for few-shot object detection,

Reference 7

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Observation 135e006d-400a-4fc2-8826-4d5765ada112 · outbound

This paper cites Few-shot object detection with foundation models,.

Few-Shot Object Detection via Spatial-Channel State Space Model Few-shot object detection with foundation models,

Reference 8

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Observation 802fffc4-e3e2-49a6-9773-6d5aca1f056b · outbound

This paper cites Adversarial feature training for few-shot object detection,.

Few-Shot Object Detection via Spatial-Channel State Space Model Adversarial feature training for few-shot object detection,

Reference 9

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Observation 150e4b79-b89a-4627-8c3f-3f5e8a993568 · outbound

This paper cites Snida: Unlocking few-shot object detection with non-linear semantic decoupling augmen- tation,.

Few-Shot Object Detection via Spatial-Channel State Space Model Snida: Unlocking few-shot object detection with non-linear semantic decoupling augmen- tation,

Reference 10

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Observation 95948bb6-27bf-492c-8e26-0a1f0e55b9a0 · outbound

This paper cites Squeeze-and-excitation networks,.

Few-Shot Object Detection via Spatial-Channel State Space Model Squeeze-and-excitation networks,

Reference 11

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Source-reported events for the cited work

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Observation befc5550-53e3-41bc-8efc-76c0a44f7712 · outbound

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

Few-Shot Object Detection via Spatial-Channel State Space Model Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 12

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Unavailable: canonical work link unavailable.

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Observation 512f3270-7033-4883-90e8-bf3f11b166b8 · outbound

This paper cites Cbam: Convolutional block attention module,.

Few-Shot Object Detection via Spatial-Channel State Space Model Cbam: Convolutional block attention module,

Reference 13

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Observation acb68a0d-618e-44a2-8ef2-b4ca924d6c6c · outbound

This paper cites Eca-net: Efficient channel attention for deep convolutional neural networks,.

Few-Shot Object Detection via Spatial-Channel State Space Model Eca-net: Efficient channel attention for deep convolutional neural networks,

Reference 14

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Observation c0f90ba9-6855-42c6-84cc-2e4aa6b8f05b · outbound

This paper cites Attention is all you need,.

Few-Shot Object Detection via Spatial-Channel State Space Model Attention is all you need,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 5f7c102f-148d-454b-94d0-114b7be71c6d · outbound

This paper cites Few-shot object detection: Research advances and challenges,.

Few-Shot Object Detection via Spatial-Channel State Space Model Few-shot object detection: Research advances and challenges,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation e7397963-48a8-40f1-bf67-35a058799130 · outbound

This paper cites Decoupling classifier for boosting few- shot object detection and instance segmentation,.

Few-Shot Object Detection via Spatial-Channel State Space Model Decoupling classifier for boosting few- shot object detection and instance segmentation,

Reference 17

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 013ce7bc-2996-4be1-b23e-a261ef2bdf87 · outbound

This paper cites Explicit margin equilibrium for few-shot object detection,.

Few-Shot Object Detection via Spatial-Channel State Space Model Explicit margin equilibrium for few-shot object detection,

Reference 18

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Observation 32b70ee3-4f6f-4568-8fc2-a26f3a7f5ce8 · outbound

This paper cites Proposal distribution calibration for few-shot object detection,.

Few-Shot Object Detection via Spatial-Channel State Space Model Proposal distribution calibration for few-shot object detection,

Reference 19

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Observation bd903207-0007-40c4-ad1b-0fb177f49386 · outbound

This paper cites Fsodv2: A deep calibrated few-shot object detection network,.

Few-Shot Object Detection via Spatial-Channel State Space Model Fsodv2: A deep calibrated few-shot object detection network,

Reference 20

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Observation 02cb487e-a647-42fa-9977-f45b6d8c22a2 · outbound

This paper cites Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment.

Few-Shot Object Detection via Spatial-Channel State Space Model Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment

Reference 21

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation ea98acc9-4ec0-486b-9bd6-850b8fd19963 · outbound

This paper cites Meta r-cnn: Towards general solver for instance-level low-shot learning,.

Few-Shot Object Detection via Spatial-Channel State Space Model Meta r-cnn: Towards general solver for instance-level low-shot learning,

Reference 22

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Observation 2de79f04-c7b1-4720-88f5-1b56fabb04d4 · outbound

This paper cites Query adaptive few- shot object detection with heterogeneous graph convolutional networks,.

Few-Shot Object Detection via Spatial-Channel State Space Model Query adaptive few- shot object detection with heterogeneous graph convolutional networks,

Reference 23

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 0f5ffaa6-93bb-42b5-bbf7-c944d0df395f · outbound

This paper cites Frustrat- ingly simple few-shot object detection,.

Few-Shot Object Detection via Spatial-Channel State Space Model Frustrat- ingly simple few-shot object detection,

Reference 24

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Observation 9e897997-110d-4858-90b2-33ea4e0a0640 · outbound

This paper cites Semantic relation reasoning for shot-stable few-shot object detection,.

Few-Shot Object Detection via Spatial-Channel State Space Model Semantic relation reasoning for shot-stable few-shot object detection,

Reference 25

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 7e05df2b-bd84-4ad9-84e5-8ae7247a1929 · outbound

This paper cites Niff: Alleviating forgetting in generalized few-shot object detection via neural instance feature forging,.

Few-Shot Object Detection via Spatial-Channel State Space Model Niff: Alleviating forgetting in generalized few-shot object detection via neural instance feature forging,

Reference 26

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 011b1cdf-1625-4079-9bb6-7ec9adccaf95 · outbound

This paper cites Fsce: Few-shot object detection via contrastive proposal encoding,.

Few-Shot Object Detection via Spatial-Channel State Space Model Fsce: Few-shot object detection via contrastive proposal encoding,

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 370c5398-07b7-489f-a98b-1a3e938ddda7 · outbound

This paper cites Accurate few-shot object detection with support-query mutual guidance and hybrid loss,.

Few-Shot Object Detection via Spatial-Channel State Space Model Accurate few-shot object detection with support-query mutual guidance and hybrid loss,

Reference 28

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation a99d8128-aaaf-4fd3-92df-b6d566efcf2a · outbound

This paper cites Repmet: representative-based metric learning for classification and few-shot object detection,.

Few-Shot Object Detection via Spatial-Channel State Space Model Repmet: representative-based metric learning for classification and few-shot object detection,

Reference 29

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation debfc2d1-e31a-43aa-92a5-63d08b59ebdb · outbound

This paper cites Feature reconstruction and metric based network for few-shot object detection,.

Few-Shot Object Detection via Spatial-Channel State Space Model Feature reconstruction and metric based network for few-shot object detection,

Reference 30

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raw_fallback, observed 2026-08-06T15:39:16.612479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation e251bd6e-e9a2-4589-a8d2-6c73c8e0e1e6 · outbound

This paper cites Few-Shot Object Detection via Variational Feature Aggregation.

Few-Shot Object Detection via Spatial-Channel State Space Model Few-Shot Object Detection via Variational Feature Aggregation

Reference 31

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 55555871-f890-4934-9e36-1b6f6a7742fe · outbound

This paper cites Generating features with increased crop- related diversity for few-shot object detection,.

Few-Shot Object Detection via Spatial-Channel State Space Model Generating features with increased crop- related diversity for few-shot object detection,

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 38ef4f9d-5d58-4479-b400-6c5a1fcd95a3 · outbound

This paper cites Few-shot object detection with fully cross-transformer,.

Few-Shot Object Detection via Spatial-Channel State Space Model Few-shot object detection with fully cross-transformer,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation b392b3fc-4e13-46e0-aa6c-f36782ea9322 · outbound

This paper cites Meta-detr: Image- level few-shot detection with inter-class correlation exploitation,.

Few-Shot Object Detection via Spatial-Channel State Space Model Meta-detr: Image- level few-shot detection with inter-class correlation exploitation,

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 54265608-b325-4c41-b512-989e40159ce6 · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

Few-Shot Object Detection via Spatial-Channel State Space Model Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 35

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Observation ad9977a5-0e2f-4aa0-909f-dee0562fc13e · outbound

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

Few-Shot Object Detection via Spatial-Channel State Space Model VideoMamba: State Space Model for Efficient Video Understanding

Reference 36

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Observation e866c5b9-99e4-472a-b3ce-95eb033229ac · outbound

This paper cites Visual Mamba: A Survey and New Outlooks.

Few-Shot Object Detection via Spatial-Channel State Space Model Visual Mamba: A Survey and New Outlooks

Reference 37

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Observation 29a21846-f1f6-4ae0-b1b0-c7fdc321f399 · outbound

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

Few-Shot Object Detection via Spatial-Channel State Space Model Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 38

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Observation e8324a18-ee0b-42fa-ad8b-5a346708a1df · outbound

This paper cites VMamba: Visual State Space Model.

Few-Shot Object Detection via Spatial-Channel State Space Model VMamba: Visual State Space Model

Reference 39

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Observation 9a60c445-ed65-4fdf-b181-35e4d860a244 · outbound

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

Few-Shot Object Detection via Spatial-Channel State Space Model LocalMamba: Visual State Space Model with Windowed Selective Scan

Reference 40

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Observation e22a3aca-7843-42af-a7fe-7a63d0c51886 · outbound

This paper cites Fcanet: Frequency channel attention networks,.

Few-Shot Object Detection via Spatial-Channel State Space Model Fcanet: Frequency channel attention networks,

Reference 41

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 1ef2e494-563a-43a7-8580-3ba4fb14d175 · outbound

This paper cites Global context networks,.

Few-Shot Object Detection via Spatial-Channel State Space Model Global context networks,

Reference 42

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Observation 48f5eee5-0d8b-43fc-8545-bfd7c60cb981 · outbound

This paper cites Deep residual learning for image recognition,.

Few-Shot Object Detection via Spatial-Channel State Space Model Deep residual learning for image recognition,

Reference 43

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 2e96b023-8ac2-4a05-99aa-6c9f504910f3 · outbound

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

Few-Shot Object Detection via Spatial-Channel State Space Model Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 44

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation fecf06d3-a9c6-4b2c-96e3-2a51a91d8c31 · outbound

This paper cites How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections.

Few-Shot Object Detection via Spatial-Channel State Space Model How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections

Reference 45

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Observation 0fd614ce-da70-4fb3-8786-cdfaaf19fbf5 · outbound

This paper cites Meta-learning to detect rare objects,.

Few-Shot Object Detection via Spatial-Channel State Space Model Meta-learning to detect rare objects,

Reference 46

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation ca0dfc4d-219e-4252-8889-d6769d9ed9c7 · outbound

This paper cites Fine-grained prototypes distillation for few-shot object detection,.

Few-Shot Object Detection via Spatial-Channel State Space Model Fine-grained prototypes distillation for few-shot object detection,

Reference 47

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 7dc68e03-42c5-461d-81af-6a5bd2971bf5 · outbound

This paper cites Detect Everything with Few Examples.

Few-Shot Object Detection via Spatial-Channel State Space Model Detect Everything with Few Examples

Reference 48

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Observation ecbafeb2-32cc-488c-b6a3-10efa7f86980 · outbound

This paper cites The pascal visual object classes (voc) challenge,.

Few-Shot Object Detection via Spatial-Channel State Space Model The pascal visual object classes (voc) challenge,

Reference 49

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation cb503b0f-1c4a-4e7f-94bf-9131e8be7b1b · outbound

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

Few-Shot Object Detection via Spatial-Channel State Space Model Microsoft coco: Common objects in context,

Reference 50

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raw_fallback, observed 2026-08-06T15:39:16.504215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation f520601a-fce1-4a0c-86cb-98a92680ed1f · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks,.

Few-Shot Object Detection via Spatial-Channel State Space Model Faster r-cnn: Towards real-time object detection with region proposal networks,

Reference 51

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Observation d7cad26d-f247-42db-a850-09cf38a7b9e7 · outbound

This paper cites Imagenet large scale visual recognition challenge,.

Few-Shot Object Detection via Spatial-Channel State Space Model Imagenet large scale visual recognition challenge,

Reference 52

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 80547828-bd69-4f23-8646-d79c49b2fd7f · outbound

This paper cites AdapterFusion: Non-Destructive Task Composition for Transfer Learning.

Few-Shot Object Detection via Spatial-Channel State Space Model AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Reference 53

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Pith citing papers

No inbound Pith citation observations are available.