Paper Citation Record · LEDGER

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision

As of 9 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 5 inbound Pith citation observations for arXiv:2509.08959.

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

pith.paper-citation-record.v1
2509.08959 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:58:23.640265Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:59:10.670721Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 7b6cb07b-8ec2-4f1f-8404-cae2df7425d1 · outbound

This paper cites Gradient-based learning applied to document recognition,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Gradient-based learning applied to document recognition,

Reference 1

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Observation ae41014f-b0fe-4d1b-9509-51f6ff42bd5f · outbound

This paper cites You only look once: Unified, real-time object detection,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision You only look once: Unified, real-time object detection,

Reference 2

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Observation 38eda33b-4bd1-4e7a-876e-a32da7f61e8e · outbound

This paper cites Fully convolutional net- works for semantic segmentation,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Fully convolutional net- works for semantic segmentation,

Reference 3

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Observation 2ab2c3c4-d64a-4763-8937-0a9102f9aa72 · outbound

This paper cites Deep residual learning for image recognition,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Deep residual learning for image recognition,

Reference 4

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Observation 011244c0-1a9f-42d2-8a4e-acd96c75ac11 · outbound

This paper cites Densely connected convolutional networks.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Densely connected convolutional networks

Reference 5

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Observation e8318e24-16cb-4644-b411-fb9c9f2d7a45 · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Xception: Deep learning with depthwise separable convolutions,

Reference 6

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Observation d5132a38-0f1e-452d-9289-e4dc8f37b167 · outbound

This paper cites Aggregated resid- ual transformations for deep neural networks,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Aggregated resid- ual transformations for deep neural networks,

Reference 7

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Observation ef5fbdb6-cb8c-404b-981e-24fa05e96d78 · outbound

This paper cites Attention is all you need,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Attention is all you need,

Reference 8

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Observation 06474b49-8cdf-423f-9272-1e856de123a7 · outbound

This paper cites Bert: Pre- training of deep bidirectional transformers for language un- derstanding,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Bert: Pre- training of deep bidirectional transformers for language un- derstanding,

Reference 9

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Observation 370fda7b-97ed-40c6-b0ca-18d8ce88a26e · outbound

This paper cites Language models are few-shot learners,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Language models are few-shot learners,

Reference 10

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Observation 8df30c06-ea15-40a7-ba71-6a64bbfd3f37 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 11

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Observation 6ece51b2-4642-4a8a-b4de-09af37958658 · outbound

This paper cites Atten- tion augmented convolutional networks.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Atten- tion augmented convolutional networks

Reference 12

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Observation 4cfe69b0-0088-499c-aa26-63320b1ec7e3 · outbound

This paper cites Exploring self-attention for image recognition,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Exploring self-attention for image recognition,

Reference 13

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Observation f6947403-24c8-4a08-91c6-89a330ce96c4 · outbound

This paper cites Bottleneck transformers for visual recognition,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Bottleneck transformers for visual recognition,

Reference 14

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Observation 45127c65-f3c9-49ea-94cc-64a5fc1b7cdf · outbound

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

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,

Reference 15

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Observation 32ed51ce-4f01-4c91-83cf-e7abdcdc1a5d · outbound

This paper cites An image is 11 worth 16x16 words: Transformers for image recognition atscale,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision An image is 11 worth 16x16 words: Transformers for image recognition atscale,

Reference 16

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Observation 3272b451-cf02-447a-a243-c6ee251d4a0c · outbound

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

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 17

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Observation aad4f33b-b6ae-47a5-85f9-0f9c0649db78 · outbound

This paper cites Transformers in vision: A survey,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Transformers in vision: A survey,

Reference 18

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Observation 8b285519-a0d8-4c86-ad40-09fcf4a2bd1d · outbound

This paper cites Learning multiple layers of features from tiny images,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Learning multiple layers of features from tiny images,

Reference 19

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Observation c00656e8-4966-489a-89f2-142f39f9c22f · outbound

This paper cites Mnist hand- written digit database,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Mnist hand- written digit database,

Reference 20

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Observation ba0a7cc1-b58f-41fc-9e50-ed22450ebab6 · outbound

This paper cites Reading digits in natural images with unsupervised fea- ture learning,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Reading digits in natural images with unsupervised fea- ture learning,

Reference 21

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Imagenet classifi- cation with deep convolutional neural networks,

Reference 22

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Very deep convolutional net- works for large-scale image recognition,

Reference 23

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Multi-scale context aggregation by dilated convolutions,

Reference 24

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Multiscale vision transformers,

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This paper cites Mobilevit: Light-weight, general- purpose, and mobile-friendly vision transformer,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Mobilevit: Light-weight, general- purpose, and mobile-friendly vision transformer,

Reference 26

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Flexivit: One model for all patch sizes,

Reference 27

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Tokens-to-token vit: Training vision transformers from scratch on imagenet,

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Gradient- based learning applied to document recognition,

Reference 29

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Training data-efficient image transformers & distil- lation through attention,

Reference 30

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Observation 19ae81bd-b7d7-4711-9dc6-7a9c320cc921 · outbound

This paper cites Patchrot: Self- supervised training of vision transformers by rotation predic- tion,.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Patchrot: Self- supervised training of vision transformers by rotation predic- tion,

Reference 31

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Observation aeed180e-77d6-41d9-8f85-4a90dcdc669e · outbound

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Vision Transformer for Small-Size Datasets

Reference 32

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This paper cites How to train vision trans- former on small-scale datasets?.

CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision How to train vision trans- former on small-scale datasets?

Reference 33

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Efficient training of visual transformers with small datasets,

Reference 34

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Layer Normalization

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Mobilenetv2: Inverted residuals and linear bottlenecks,

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Tiny imagenet visual recognition chal- lenge,

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Pytorch: An imperative style, high-performance deep learning library,

Reference 38

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision mixup: Beyond Empirical Risk Minimization

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Cutmix: Regularization strategy to train strong classifiers with localizable features,

Reference 40

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Random erasing data augmentation,

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Randaugment: Practical automated data augmentation with a reduced search space,

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Autoaugment: Learning augmentation policies from data,

Reference 43

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Deep networks with stochastic depth,

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Decoupled weight decay regular- ization,

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CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision Grad-cam: Visual explanations from deep networks via gradient-based localization,

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Pith citing papers

Observation a09d9311-4512-4275-bea1-ee236fc6dca1 · inbound

On What We Can Learn from Low-Resolution Data cites this paper.

On What We Can Learn from Low-Resolution Data CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision

Reference 68

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Explainable Novel Category Discovery in Semantic Concept Space cites this paper.

Explainable Novel Category Discovery in Semantic Concept Space CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision

Reference 24

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Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs cites this paper.

Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision

Reference 29

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Learning to Transmit: Volatility-Aware Predictive Communication for Energy-Efficient IoT Networks cites this paper.

Learning to Transmit: Volatility-Aware Predictive Communication for Energy-Efficient IoT Networks CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision

Reference 43

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI cites this paper.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision

Reference 26

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