A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP | Paper Citation Record

Observation 0b75b2e5-1c3a-41ef-b654-34161a5a6193 · outbound

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

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 1

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Observation 16f48e47-3aa3-4e18-b672-e4c201c07bc8 · outbound

This paper cites Masked autoencoders are scalable vision learners,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Masked autoencoders are scalable vision learners,

Reference 2

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Observation 92c9d89a-73d8-4c1b-ac04-65fb376299ef · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 3

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Observation 611b93f7-047a-4f4e-93b2-bb6f6c8d3918 · outbound

This paper cites Flava: A foundational language and vision alignment model,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Flava: A foundational language and vision alignment model,

Reference 4

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Observation 0fa5598e-b729-4188-9b37-d89559577fa9 · outbound

This paper cites Large Language Models Can Understanding Depth from Monocular Images.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Large Language Models Can Understanding Depth from Monocular Images

Reference 5

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Observation b723cfb6-611a-40cd-8b77-87c161635084 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Depth anything: Unleashing the power of large-scale unlabeled data,

Reference 6

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Observation 111a5061-44b3-4a7b-b6f5-f05989653757 · outbound

This paper cites Pad: Self-supervised pre-training with patchwise-scale adapter for infrared images,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Pad: Self-supervised pre-training with patchwise-scale adapter for infrared images,

Reference 7

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Observation 4d44a9db-978a-4e93-a2e7-8b5f2b33831d · outbound

This paper cites F-ViTA: Foundation Model Guided Visible to Thermal Translation.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP F-ViTA: Foundation Model Guided Visible to Thermal Translation

Reference 8

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Observation cb6d7a1b-7ea7-4cf0-8a8e-490e188954be · outbound

This paper cites VideoMAE: Masked autoencoders are data-efficient learners for self-supervised video pre-training,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP VideoMAE: Masked autoencoders are data-efficient learners for self-supervised video pre-training,

Reference 9

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Observation a1a9f6db-c3d4-4f08-a9f3-795658efce97 · outbound

This paper cites Videomae v2: Scaling video masked autoencoders with dual masking,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Videomae v2: Scaling video masked autoencoders with dual masking,

Reference 10

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Observation 5cd75477-a027-4bc3-a3bb-406a26e6666f · outbound

This paper cites Omnivl: One foundation model for image-language and video- language tasks,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Omnivl: One foundation model for image-language and video- language tasks,

Reference 11

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Observation 2488037c-a727-417a-86f3-ba826fa6122d · outbound

This paper cites P2p: Tuning pre-trained image models for point cloud analysis with point-to-pixel prompting,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP P2p: Tuning pre-trained image models for point cloud analysis with point-to-pixel prompting,

Reference 12

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Observation 8ff34b17-245a-4985-b3c0-d6a3df311583 · outbound

This paper cites Pointclip: Point cloud understanding by clip,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Pointclip: Point cloud understanding by clip,

Reference 13

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Observation e8ed976e-f73d-4c0e-a362-3c5a0ec23c39 · outbound

This paper cites Diffusion models as masked autoencoders,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Diffusion models as masked autoencoders,

Reference 14

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Observation e2978196-0f8b-4c2b-8286-b08a02d76dab · outbound

This paper cites Hierarchical recurrent neural network for skeleton based action recogni- tion,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Hierarchical recurrent neural network for skeleton based action recogni- tion,

Reference 15

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Observation 5523ccd9-7c6d-4732-8217-9691868bda82 · outbound

This paper cites Skeleton-based action recognition using spatio-temporal lstm network with trust gates,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Skeleton-based action recognition using spatio-temporal lstm network with trust gates,

Reference 16

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Observation 796176f6-e6ad-4eae-a3b5-6aef00f31692 · outbound

This paper cites View adaptive recurrent neural networks for high performance human action recognition from skeleton data,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP View adaptive recurrent neural networks for high performance human action recognition from skeleton data,

Reference 17

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Observation 20f3bb99-eea7-42ce-890b-d1798d643c43 · outbound

This paper cites Skeleton based action recognition with convolutional neural network,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Skeleton based action recognition with convolutional neural network,

Reference 18

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Observation 15bc7c7d-9e61-43b4-955d-1681751aecb2 · outbound

This paper cites A new representation of skeleton sequences for 3d action recognition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP A new representation of skeleton sequences for 3d action recognition,

Reference 19

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Observation 2e481554-1aec-4c89-a9af-94710de46594 · outbound

This paper cites Co-occurrence feature learning from skeleton data for action recog- nition and detection with hierarchical aggregation,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Co-occurrence feature learning from skeleton data for action recog- nition and detection with hierarchical aggregation,

Reference 20

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Observation 35da2927-a66f-4c57-8013-ed70c2d900e5 · outbound

This paper cites Spatial temporal graph convolutional networks for skeleton-based ac- tion recognition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Spatial temporal graph convolutional networks for skeleton-based ac- tion recognition,

Reference 21

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Observation 3b0ed4d5-9bcd-4331-8754-fc6dbfa591b0 · outbound

This paper cites Two- stream adaptive graph convolutional networks for skeleton-based action recognition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Two- stream adaptive graph convolutional networks for skeleton-based action recognition,

Reference 22

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Observation 685c5728-0bbd-42f3-a779-efdebabb2314 · outbound

This paper cites Channel-wise topology refinement graph convolution for skeleton-based action recognition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Channel-wise topology refinement graph convolution for skeleton-based action recognition,

Reference 23

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Observation 0dd03cef-bd49-4e90-8736-0dc1cf81cf84 · outbound

This paper cites Stst: Spatial-temporal specialized transformer for skeleton- based action recognition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Stst: Spatial-temporal specialized transformer for skeleton- based action recognition,

Reference 24

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Observation 40655a0a-789d-48f3-841e-4af85e150dc8 · outbound

This paper cites Hypergraph transformer for skeleton-based action recognition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Hypergraph transformer for skeleton-based action recognition,

Reference 25

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Observation 0e4e9a83-972a-4ea9-bee0-aafbbdb67098 · outbound

This paper cites 3d human action representation learning via cross-view consistency pursuit,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP 3d human action representation learning via cross-view consistency pursuit,

Reference 26

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Observation ff3fe65c-ac7b-4946-866c-0cf3d22890f4 · outbound

This paper cites Contrastive learning from extremely aug- mented skeleton sequences for self-supervised action recognition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Contrastive learning from extremely aug- mented skeleton sequences for self-supervised action recognition,

Reference 27

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Observation 36cbc4a1-ebc3-4c8d-9e15-b43af9bbe8e0 · outbound

This paper cites Contrastive positive mining for unsupervised 3d action represen- tation learning,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Contrastive positive mining for unsupervised 3d action represen- tation learning,

Reference 28

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Observation d7610019-3dee-4acd-ae89-32d6669e20f4 · outbound

This paper cites Masked motion predictors are strong 3d action representation learners,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Masked motion predictors are strong 3d action representation learners,

Reference 29

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Observation e1b9ff96-6b20-470d-83d8-866757908ec9 · outbound

This paper cites Macdiff: Unified skeleton modeling with masked conditional diffusion,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Macdiff: Unified skeleton modeling with masked conditional diffusion,

Reference 30

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Observation 8e3d93a3-9fd4-44d4-ad14-3bc87e06cc2c · outbound

This paper cites Skeletonmae: Spatial-temporal masked au- toencoders for self-supervised skeleton action recog- nition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Skeletonmae: Spatial-temporal masked au- toencoders for self-supervised skeleton action recog- nition,

Reference 31

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Observation c1d1644d-adc5-4f5d-9dae-0c4a96b9cc1a · outbound

This paper cites Momen- tum contrast for unsupervised visual representation learning,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Momen- tum contrast for unsupervised visual representation learning,

Reference 32

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Observation bc34bf90-a585-42aa-b5e5-4d1c071e875b · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP A simple framework for contrastive learning of visual representations,

Reference 33

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Observation 2d1cac2a-ec23-459a-ac93-88679ac68309 · outbound

This paper cites Spatiotemporal contrastive video representation learning,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Spatiotemporal contrastive video representation learning,

Reference 34

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Observation a1e20b50-8770-416c-9a95-5c0ebfe960be · outbound

This paper cites BEit: BERT pre-training of image transformers,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP BEit: BERT pre-training of image transformers,

Reference 35

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Observation 963da4a3-adcc-4625-9d6d-06b317d49c95 · outbound

This paper cites Masked feature prediction for self- supervised visual pre-training,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Masked feature prediction for self- supervised visual pre-training,

Reference 36

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Observation d72f2d0e-9b95-4758-a0ca-4fc926622d97 · outbound

This paper cites Dynamic multiscale graph neural networks for 3d skeleton based human motion prediction,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Dynamic multiscale graph neural networks for 3d skeleton based human motion prediction,

Reference 37

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Observation 45bf7f52-8a73-4808-89d0-3ae3b0957aa4 · outbound

This paper cites Exploiting spatial-temporal relationships for 3d pose estimation via graph con- volutional networks,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Exploiting spatial-temporal relationships for 3d pose estimation via graph con- volutional networks,

Reference 38

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Observation e7fc133c-520b-4a72-8b5e-26dd6b02075d · outbound

This paper cites Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition

Reference 39

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Observation d86fc034-19a8-4dd6-919a-b288fbf868cd · outbound

This paper cites Skele- ton cloud colorization for unsupervised 3d action rep- resentation learning,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Skele- ton cloud colorization for unsupervised 3d action rep- resentation learning,

Reference 40

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Observation 954ff6b5-b7a8-4f83-9fd0-fb72b4f4c071 · outbound

This paper cites Collaborating domain-shared and target-specific feature clustering for cross-domain 3d action recognition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Collaborating domain-shared and target-specific feature clustering for cross-domain 3d action recognition,

Reference 41

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Observation ad617174-7455-431d-a686-caf1be3f010a · outbound

This paper cites Ntu rgb+d: A large scale dataset for 3d human activity analysis,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Ntu rgb+d: A large scale dataset for 3d human activity analysis,

Reference 42

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Observation 740dac90-8092-4db9-8de0-d5d43b59df45 · outbound

This paper cites Ntu rgb+d 120: A large-scale bench- mark for 3d human activity understanding,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Ntu rgb+d 120: A large-scale bench- mark for 3d human activity understanding,

Reference 43

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Observation ff22536a-2572-415b-b8c0-3906f6120615 · outbound

This paper cites A bench- mark dataset and comparison study for multi-modal human action analytics,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP A bench- mark dataset and comparison study for multi-modal human action analytics,

Reference 44

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Observation f5abd89c-e6a2-404d-9f42-56e41e5d601f · outbound

This paper cites Cross- view action modeling, learning and recognition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Cross- view action modeling, learning and recognition,

Reference 45

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Observation d2685dd8-9338-4a53-9c5f-5a8340194899 · outbound

This paper cites Toyota smarthome: Real-world activities of daily living,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Toyota smarthome: Real-world activities of daily living,

Reference 46

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Observation e3c34f84-7f86-4f64-bbe4-10bc643a5541 · outbound

This paper cites Semantics-guided neural networks for efficient skeleton-based human action recognition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Semantics-guided neural networks for efficient skeleton-based human action recognition,

Reference 47

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Observation 07840b59-f146-4267-82a8-24f6ea7549ab · outbound

This paper cites Skeleton-based action recognition with shift graph convolutional network,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Skeleton-based action recognition with shift graph convolutional network,

Reference 48

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Observation b3831f3d-0de6-44a6-a7fa-d9684c4d738e · outbound

This paper cites Unsupervised representation learning with JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 13 long-term dynamics for skeleton based action recogni- tion,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Unsupervised representation learning with JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 13 long-term dynamics for skeleton based action recogni- tion,

Reference 49

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Observation a4dc1a56-1fcd-4595-93c2-7a8bcba78426 · outbound

This paper cites Predict & cluster: Unsupervised skeleton based action recognition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Predict & cluster: Unsupervised skeleton based action recognition,

Reference 50

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Observation 366592a3-c611-4679-9308-2319924da831 · outbound

This paper cites Ms2l: Multi- task self-supervised learning for skeleton based action recognition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Ms2l: Multi- task self-supervised learning for skeleton based action recognition,

Reference 51

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Observation a19d53fe-c254-490d-9dd5-be4d7fee82b3 · outbound

This paper cites Skeleton- contrastive 3d action representation learning,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Skeleton- contrastive 3d action representation learning,

Reference 52

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Observation 802284f4-345b-49f0-ac4c-5ccd63fcca5c · outbound

This paper cites Global- local motion transformer for unsupervised skeleton- based action learning,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Global- local motion transformer for unsupervised skeleton- based action learning,

Reference 53

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Observation 7e277e30-1c62-4f1d-9927-ac0c2ce9a2f2 · outbound

This paper cites Cmd: Self-supervised 3d action representation learning with cross-modal mutual distillation,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Cmd: Self-supervised 3d action representation learning with cross-modal mutual distillation,

Reference 54

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Observation c8c519ac-761c-4d41-afd3-011be7da6b74 · outbound

This paper cites Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition,

Reference 55

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Observation 90aecfd8-ccd5-491b-92e3-9ecb43fed72d · outbound

This paper cites Self-supervised 3d skeleton action representation learning with motion consis- tency and continuity,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Self-supervised 3d skeleton action representation learning with motion consis- tency and continuity,

Reference 56

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Observation 6db2b46a-de0f-4d0c-ba3b-f61c51bc18c4 · outbound

This paper cites View-invariant skele- ton action representation learning via motion retarget- ing,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP View-invariant skele- ton action representation learning via motion retarget- ing,

Reference 57

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Observation d3d46f76-0065-48ac-8b8a-6ff8312fb83e · outbound

This paper cites Hierarchically self- supervised transformer for human skeleton represen- tation learning,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Hierarchically self- supervised transformer for human skeleton represen- tation learning,

Reference 58

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Observation c66f9d00-dc82-41f9-aac8-616cf56e6707 · outbound

This paper cites Adversarial self-supervised learning for semi-supervised 3d action recognition,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Adversarial self-supervised learning for semi-supervised 3d action recognition,

Reference 59

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Observation 3d8de597-38e6-4b43-a71f-51f897f56250 · outbound

This paper cites UNIK: A Unified Framework for Real-world Skeleton-based Action Recognition.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP UNIK: A Unified Framework for Real-world Skeleton-based Action Recognition

Reference 60

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Observation 0d29d8a5-a399-450c-a9ac-72ec1b85f4ca · outbound

This paper cites Attention is all you need,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Attention is all you need,

Reference 61

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Observation 074e8d82-25c1-415f-b0b8-120a18f04c28 · outbound

This paper cites Layer Normalization.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Layer Normalization

Reference 62

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Observation 1649a5a0-0926-4997-bedf-1ebce901ada5 · outbound

This paper cites Denoising diffusion probabilistic models,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Denoising diffusion probabilistic models,

Reference 63

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Observation 9cb9a11b-b15b-47ae-9fe5-27f1c93b65f1 · outbound

This paper cites Lcr-net++: Multi-person 2d and 3d pose detection in natural im- ages,.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Lcr-net++: Multi-person 2d and 3d pose detection in natural im- ages,

Reference 64

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