Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training | Paper Citation Record

Observation ff2db643-9c88-4e11-b3ec-564fb156c327 · outbound

This paper cites Atmospherictransmissionandthermalinertia induced blind road segmentation with a large-scale dataset tbrsd, in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Atmospherictransmissionandthermalinertia induced blind road segmentation with a large-scale dataset tbrsd, in: Proc

Reference 1

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Observation eee635a4-d931-43cc-a82d-7535b52a03c1 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic im- age segmentation, in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Encoder-decoder with atrous separable convolution for semantic im- age segmentation, in: Proc

Reference 2

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Observation b547d91a-5578-432c-ab04-28266635dad3 · outbound

This paper cites Vision transformer adapter for dense predictions, in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Vision transformer adapter for dense predictions, in: Proc

Reference 3

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Observation 31ed6fc8-a1a9-41f0-b089-75d97cd8df67 · outbound

This paper cites Masked-attention mask transformer for universal image segmenta- tion,in:Proc.IEEEConf.Comput.Vis.PatternRecognit.,pp.1290– 1299.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Masked-attention mask transformer for universal image segmenta- tion,in:Proc.IEEEConf.Comput.Vis.PatternRecognit.,pp.1290– 1299

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Observation 68e0748c-5880-4881-9976-e33fe7d6c95c · outbound

This paper cites ViM-VQ: Efficientpost-trainingvectorquantizationforvisualmamba,in:Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training ViM-VQ: Efficientpost-trainingvectorquantizationforvisualmamba,in:Proc

Reference 5

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Observation bb33aeff-da42-48ff-b65c-248da38f822f · outbound

This paper cites Lraf-net: Long-range attention fusion network for visible–infrared object detection.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Lraf-net: Long-range attention fusion network for visible–infrared object detection

Reference 6

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Observation 33560ead-2c15-43e6-bd59-cdfd592e5e45 · outbound

This paper cites Cf- deformable detr: an end-to-end alignment-free model for weakly Qiwei Ma et al.:Preprint submitted to ElsevierPage 11 of 13 aligned visible-infrared object detection, in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Cf- deformable detr: an end-to-end alignment-free model for weakly Qiwei Ma et al.:Preprint submitted to ElsevierPage 11 of 13 aligned visible-infrared object detection, in: Proc

Reference 7

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Observation 62a0ac14-92c2-42e8-98df-2dfb775be9b9 · outbound

This paper cites MCMAE: Maskedconvolutionmeetsmaskedautoencoders,in:Proc.Adv.Neu- ral Inf.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training MCMAE: Maskedconvolutionmeetsmaskedautoencoders,in:Proc.Adv.Neu- ral Inf

Reference 8

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Observation 064330e3-cea5-4787-bed3-3f25c7c512b1 · outbound

This paper cites Imagebind:Oneembeddingspacetobindthemall,in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Imagebind:Oneembeddingspacetobindthemall,in: Proc

Reference 9

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Observation 93da5d5b-a715-4e34-8ff8-eabc30a3a674 · outbound

This paper cites an unresolved cited work.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Unresolved cited work

Reference 10

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Observation a2bf234c-2e72-4c9c-a0ef-089ee3787806 · outbound

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Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Unresolved cited work

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Observation 87157311-d29a-45bf-8834-83a9265bc39c · outbound

This paper cites Mfnet: Towards real-time semantic segmentation for autonomous vehicleswithmulti-spectralscenes,in:Proc.Int.Conf.Intell.Robots Systems, pp.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Mfnet: Towards real-time semantic segmentation for autonomous vehicleswithmulti-spectralscenes,in:Proc.Int.Conf.Intell.Robots Systems, pp

Reference 12

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Observation 345eff99-a428-4a86-9f31-eb4cb1fc72f5 · outbound

This paper cites Masked autoencoders are scalable vision learners, in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Masked autoencoders are scalable vision learners, in: Proc

Reference 13

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Observation 0ebae3f0-005f-4788-883d-ecc7c77fe3c9 · outbound

This paper cites MaskR-CNN,in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training MaskR-CNN,in: Proc

Reference 14

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Observation be8ea54c-377d-47c9-a520-ba96e70005de · outbound

This paper cites Global–local feature fusion networkforvisible–infraredvehicledetection.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Global–local feature fusion networkforvisible–infraredvehicledetection

Reference 15

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Observation 33bc0292-05b2-4825-afd0-6c4ccaea521d · outbound

This paper cites Configuring data augmentations to reduce variance shift in positional embedding of vision transformers, in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Configuring data augmentations to reduce variance shift in positional embedding of vision transformers, in: Proc

Reference 16

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Observation 1bb4080d-8076-4cb6-ae22-bb8eb91986ca · outbound

This paper cites Segmentingobjects in day and night: Edge-conditioned cnn for thermal image semantic segmentation.IEEETrans.Neural.Netw.Learn.Syst.32,3069–3082.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Segmentingobjects in day and night: Edge-conditioned cnn for thermal image semantic segmentation.IEEETrans.Neural.Netw.Learn.Syst.32,3069–3082

Reference 17

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Observation 09c4f0b6-ee67-4fcc-bbf4-d078f654ae80 · outbound

This paper cites Segmentingobjects in day and night: Edge-conditioned cnn for thermal image semantic segmentation.IEEETrans.Neural.Netw.Learn.Syst.32,3069–3082.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Segmentingobjects in day and night: Edge-conditioned cnn for thermal image semantic segmentation.IEEETrans.Neural.Netw.Learn.Syst.32,3069–3082

Reference 18

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Observation f3ed058b-7bbc-403f-8d0e-775424f39fad · outbound

This paper cites an unresolved cited work.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Unresolved cited work

Reference 19

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Observation 7c004698-9d54-4fd3-9089-04b094b1d160 · outbound

This paper cites Explicit attention-enhanced fusion for rgb-thermal perception tasks.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Explicit attention-enhanced fusion for rgb-thermal perception tasks

Reference 20

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Observation d141cd76-7a91-4cff-aa25-3c1a4f06d185 · outbound

This paper cites COMO:Cross- mamba interaction and offset-guided fusion for multimodal object detection.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training COMO:Cross- mamba interaction and offset-guided fusion for multimodal object detection

Reference 21

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Observation 301d9679-52a1-4449-b6fc-035a114c0e56 · outbound

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Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Unresolved cited work

Reference 22

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Observation 3a0d1908-62b9-4436-8521-e99e0d725874 · outbound

This paper cites InfMAE: A foundation model in the infrared modality, in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training InfMAE: A foundation model in the infrared modality, in: Proc

Reference 23

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Observation d3c9fc40-235a-4be8-b278-89d27801d598 · outbound

This paper cites an unresolved cited work.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Unresolved cited work

Reference 24

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Observation 0bf4be3a-c3a1-4b6c-86ae-4f0225c30439 · outbound

This paper cites Bridging rgb-t image fusion and semantic segmentation via multi-task collaborative learning.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Bridging rgb-t image fusion and semantic segmentation via multi-task collaborative learning

Reference 25

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Observation 4d1e1789-98e9-47ab-b09b-c4f0a76c867c · outbound

This paper cites an unresolved cited work.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Unresolved cited work

Reference 26

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Observation 8f6249ea-4837-4b18-833e-f9f41fcddbe7 · outbound

This paper cites Visualizing data using t-SNE.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Visualizing data using t-SNE

Reference 27

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Observation 5d2a49d0-3772-4110-b706-4cb64ef106b9 · outbound

This paper cites UNIV: Unified Foundation Model for Infrared and Visible Modalities.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training UNIV: Unified Foundation Model for Infrared and Visible Modalities

Reference 28

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Observation 97ff8a2c-1069-4011-acb2-b5762047d4bc · outbound

This paper cites Suppresscontentshift:Betterdiffusionfeaturesviaoff-the-shelfgen- eration techniques.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Suppresscontentshift:Betterdiffusionfeaturesviaoff-the-shelfgen- eration techniques

Reference 29

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Observation 6ec6c76f-e384-493d-829e-0a57f808a015 · outbound

This paper cites Connecting joint-embedding predictive architecture with contrastive self-supervised learning.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Connecting joint-embedding predictive architecture with contrastive self-supervised learning

Reference 30

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Observation 68deaa24-f4f5-4111-8402-ebf498505d14 · outbound

This paper cites Attributefilterbased infrared and visible image fusion.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Attributefilterbased infrared and visible image fusion

Reference 31

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Observation 54520e64-a796-42ef-96bc-e732a0d4bd15 · outbound

This paper cites Interactive visible and infrared image fusion and segmentation.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Interactive visible and infrared image fusion and segmentation

Reference 32

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Observation d57e5170-2cb5-4f7e-8f38-39ae2e7d57a6 · outbound

This paper cites Learning by aligning: Visible-infrared person re-identification using cross-modal corre- spondences, in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Learning by aligning: Visible-infrared person re-identification using cross-modal corre- spondences, in: Proc

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Observation e45e87e3-4118-4ce3-bc7a-1bafd87df7de · outbound

This paper cites Learning transferable visual models from natural language supervision, in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Learning transferable visual models from natural language supervision, in: Proc

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Observation e08fe7ba-3352-467b-8096-c9f70f5ad206 · outbound

This paper cites Drone-based rgb- infraredcross-modalityvehicledetectionviauncertainty-awarelearn- ing.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Drone-based rgb- infraredcross-modalityvehicledetectionviauncertainty-awarelearn- ing

Reference 35

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Observation 1e2431d5-b060-4a5b-b87e-df83d9fd8ff6 · outbound

This paper cites PIAFusion: A progressive infrared and visible image fusion network based on illumination aware.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training PIAFusion: A progressive infrared and visible image fusion network based on illumination aware

Reference 36

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Observation ab31d01c-b522-40dd-a5a9-db63bb971f22 · outbound

This paper cites Yolov8: A novel object detection algorithm with enhanced performance and robustness, in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Yolov8: A novel object detection algorithm with enhanced performance and robustness, in: Proc

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This paper cites Unifiedperceptual parsing for scene understanding, in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Unifiedperceptual parsing for scene understanding, in: Proc

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Observation 08a489e4-a965-4bbd-a11f-bcbb2aa2d257 · outbound

This paper cites SegFormer: Simple and efficient design for semantic segmentationwithtransformers.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training SegFormer: Simple and efficient design for semantic segmentationwithtransformers

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This paper cites MCNet:Multi-levelcorrectionnet- work for thermal image semantic segmentation of nighttime driving scene.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training MCNet:Multi-levelcorrectionnet- work for thermal image semantic segmentation of nighttime driving scene

Reference 40

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Observation c939e05a-4923-4f22-a071-64debfa929de · outbound

This paper cites Fast and robust matching for multimodal remote sensing image registration.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Fast and robust matching for multimodal remote sensing image registration

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Observation 78c615da-a0ac-401f-8ea7-9c66682dca63 · outbound

This paper cites SPGFusion: Semantic prior guided infrared and visible image fusion via pretrained vision models.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training SPGFusion: Semantic prior guided infrared and visible image fusion via pretrained vision models

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Observation b479e1a6-cf76-4135-ae47-abdb663ee2df · outbound

This paper cites CR2PQ:Continuousrelativerotarypositionalqueryfordense visual representation learning, in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training CR2PQ:Continuousrelativerotarypositionalqueryfordense visual representation learning, in: Proc

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Observation 94649f7a-e071-418f-9afe-9ef4a378fc63 · outbound

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Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Unresolved cited work

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Observation def11a75-4f83-4086-b1f9-48ac89f6eb86 · outbound

This paper cites UNIP:Rethinkingpre-trainedattentionpatternsforinfraredsemantic segmentation, in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training UNIP:Rethinkingpre-trainedattentionpatternsforinfraredsemantic segmentation, in: Proc

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Observation aa878abd-0705-444a-bc09-afb5f1006c32 · outbound

This paper cites IEEE Conf.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training IEEE Conf

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Observation 2befc47d-c8a5-43e6-a4bd-080fe08beb49 · outbound

This paper cites Cddfuse:Correlation-drivendual-branchfeature decompositionformulti-modalityimagefusion,in:Proc.IEEEConf.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Cddfuse:Correlation-drivendual-branchfeature decompositionformulti-modalityimagefusion,in:Proc.IEEEConf

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Observation 00decbcf-c16d-443d-bf09-321064fe01c3 · outbound

This paper cites PAD: Self-Supervised Pre-Training with Patchwise-Scale Adapter for Infrared Images.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training PAD: Self-Supervised Pre-Training with Patchwise-Scale Adapter for Infrared Images

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Observation 36a90e57-ddaa-4614-93dd-a6ec33365e1e · outbound

This paper cites Semantic understanding of scenes through the ade20k dataset.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Semantic understanding of scenes through the ade20k dataset

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Observation e6f9f5bd-afb0-45c8-8ef5-0f39d4d16465 · outbound

This paper cites Equivariant multi-modality image fusion, in: Proc.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Equivariant multi-modality image fusion, in: Proc

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Observation 1bc95a16-04b3-4cc4-85cf-b963b94b5d3f · outbound

This paper cites IEEE Trans.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training IEEE Trans

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Observation e31b036c-36df-45b5-9fd3-554a3b9de4d2 · outbound

This paper cites IEEE Conf.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training IEEE Conf

Reference 2023

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Observation 079263cd-40ce-46ee-95bf-fd2d8cb81b38 · outbound

This paper cites SARVLM: A Vision Language Foundation Model for Semantic Understanding in SAR Imagery.

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training SARVLM: A Vision Language Foundation Model for Semantic Understanding in SAR Imagery

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