DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition | Paper Citation Record

Observation f7307fe1-d6d3-47a2-9a48-c5c12a2b6c39 · outbound

This paper cites Speech recognition techniques for a sign language recognition system,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Speech recognition techniques for a sign language recognition system,

Reference 1

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Observation 17b32b9c-65fd-48c5-822f-611792989f29 · outbound

This paper cites Automatic sign language analysis: A survey and the future beyond lexical meaning,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Automatic sign language analysis: A survey and the future beyond lexical meaning,

Reference 2

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This paper cites Sign language and linguistic universals, wendy sandler and diane lillo-martin,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Sign language and linguistic universals, wendy sandler and diane lillo-martin,

Reference 3

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This paper cites Reviewing 25 years of continuous sign language recognition research: Advances, challenges, and prospects,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Reviewing 25 years of continuous sign language recognition research: Advances, challenges, and prospects,

Reference 4

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This paper cites Orientation histograms for hand gesture recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Orientation histograms for hand gesture recognition,

Reference 5

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This paper cites Discriminative exemplar coding for sign language recognition with kinect,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Discriminative exemplar coding for sign language recognition with kinect,

Reference 6

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This paper cites Pose-based sign language recognition using gcn and bert,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Pose-based sign language recognition using gcn and bert,

Reference 7

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Observation 8ef4eabf-17ad-4a3b-b068-f6d47b7301fa · outbound

This paper cites Signbert: pre-training of hand-model-aware representation for sign language recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Signbert: pre-training of hand-model-aware representation for sign language recognition,

Reference 8

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This paper cites Visual alignment constraint for continuous sign language recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Visual alignment constraint for continuous sign language recognition,

Reference 9

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This paper cites Self-mutual distillation learning for continuous sign language recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Self-mutual distillation learning for continuous sign language recognition,

Reference 10

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This paper cites Continuous sign language recognition with correlation network,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Continuous sign language recognition with correlation network,

Reference 11

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This paper cites Two-Stream Network for Sign Language Recognition and Translation.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Two-Stream Network for Sign Language Recognition and Translation

Reference 12

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This paper cites Tcnet: Continuous sign language recognition from trajectories and correlated regions,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Tcnet: Continuous sign language recognition from trajectories and correlated regions,

Reference 13

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This paper cites CorrNet+: Sign Language Recognition and Translation via Spatial-Temporal Correlation.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition CorrNet+: Sign Language Recognition and Translation via Spatial-Temporal Correlation

Reference 14

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This paper cites Dynamic filter networks,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Dynamic filter networks,

Reference 15

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This paper cites Dynamic convolution: Attention over convolution kernels,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Dynamic convolution: Attention over convolution kernels,

Reference 16

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Observation a1bc1830-c0ca-46b3-8aa9-5dcf966acd3f · outbound

This paper cites Omni-Dimensional Dynamic Convolution.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Omni-Dimensional Dynamic Convolution

Reference 17

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This paper cites TAda! Temporally-Adaptive Convolutions for Video Understanding.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition TAda! Temporally-Adaptive Convolutions for Video Understanding

Reference 18

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This paper cites Temporally-Adaptive Models for Efficient Video Understanding.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Temporally-Adaptive Models for Efficient Video Understanding

Reference 19

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This paper cites Improving continuous sign language recognition via cross-frame interactions in expanded contextual spaces,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Improving continuous sign language recognition via cross-frame interactions in expanded contextual spaces,

Reference 20

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This paper cites Olmd: Orientation-aware long-term motion decoupling for con- tinuous sign language recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Olmd: Orientation-aware long-term motion decoupling for con- tinuous sign language recognition,

Reference 21

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This paper cites Tam: Temporal adaptive module for video recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Tam: Temporal adaptive module for video recognition,

Reference 22

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This paper cites Self-emphasizing network for continuous sign language recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Self-emphasizing network for continuous sign language recognition,

Reference 23

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This paper cites A deep neural framework for continuous sign language recognition by iterative training,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition A deep neural framework for continuous sign language recognition by iterative training,

Reference 24

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This paper cites Iterative alignment network for continuous sign language recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Iterative alignment network for continuous sign language recognition,

Reference 25

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This paper cites Deep radial embedding for visual sequence learning.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Deep radial embedding for visual sequence learning

Reference 26

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This paper cites Temporal lift pooling for continuous sign language recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Temporal lift pooling for continuous sign language recognition,

Reference 27

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This paper cites Intermediate loss regularization for ctc- based speech recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Intermediate loss regularization for ctc- based speech recognition,

Reference 28

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This paper cites Cosign: Explor- ing co-occurrence signals in skeleton-based continuous sign language recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Cosign: Explor- ing co-occurrence signals in skeleton-based continuous sign language recognition,

Reference 29

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This paper cites Cvt-slr: Contrastive visual-textual transformation for sign language recognition with variational alignment,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Cvt-slr: Contrastive visual-textual transformation for sign language recognition with variational alignment,

Reference 30

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This paper cites Spatial-temporal multi-cue net- work for sign language recognition and translation,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Spatial-temporal multi-cue net- work for sign language recognition and translation,

Reference 31

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This paper cites Learning sign language by watching tv (using weakly aligned subtitles),.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Learning sign language by watching tv (using weakly aligned subtitles),

Reference 32

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This paper cites Video-based signer- independent arabic sign language recognition using hidden markov models,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Video-based signer- independent arabic sign language recognition using hidden markov models,

Reference 33

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This paper cites C2slr: Consistency-enhanced continuous sign language recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition C2slr: Consistency-enhanced continuous sign language recognition,

Reference 34

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition C2st: Cross-modal contextualized sequence transduction for continuous sign language recognition,

Reference 35

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This paper cites Joint ctc-attention based end- to-end speech recognition using multi-task learning,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Joint ctc-attention based end- to-end speech recognition using multi-task learning,

Reference 36

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Auto-avsr: Audio-visual speech recognition with automatic labels,

Reference 37

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition End-to-end audio-visual speech recognition with conformers,

Reference 38

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This paper cites Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions

Reference 39

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Re-sign: Re-aligned end-to-end sequence modelling with deep recurrent cnn-hmms,

Reference 40

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This paper cites Weakly supervised learning with multi-stream cnn-lstm-hmms to discover sequential paral- lelism in sign language videos,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Weakly supervised learning with multi-stream cnn-lstm-hmms to discover sequential paral- lelism in sign language videos,

Reference 41

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This paper cites Distilling cross-temporal contexts for continuous sign language recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Distilling cross-temporal contexts for continuous sign language recognition,

Reference 42

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This paper cites SignVTCL: Multi-Modal Continuous Sign Language Recognition Enhanced by Visual-Textual Contrastive Learning.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition SignVTCL: Multi-Modal Continuous Sign Language Recognition Enhanced by Visual-Textual Contrastive Learning

Reference 43

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Condconv: Conditionally parameterized convolutions for efficient inference,

Reference 44

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This paper cites Bi-volution: a static and dynamic coupled filter,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Bi-volution: a static and dynamic coupled filter,

Reference 45

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This paper cites A dynamic convolutional layer for short range weather prediction,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition A dynamic convolutional layer for short range weather prediction,

Reference 46

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This paper cites HyperNetworks.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition HyperNetworks

Reference 47

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This paper cites Connection- ist temporal classification: labelling unsegmented sequence data with recurrent neural networks,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Connection- ist temporal classification: labelling unsegmented sequence data with recurrent neural networks,

Reference 48

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This paper cites InterAug: Augmenting Noisy Intermediate Predictions for CTC-based ASR.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition InterAug: Augmenting Noisy Intermediate Predictions for CTC-based ASR

Reference 49

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Observation 865007d9-3dc4-4248-8b4c-9ed4d19647ea · outbound

This paper cites CR-CTC: Consistency regularization on CTC for improved speech recognition.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition CR-CTC: Consistency regularization on CTC for improved speech recognition

Reference 50

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Boundary and context aware training for cif-based non-autoregressive end-to-end asr,

Reference 51

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Cass-nat: Ctc alignment-based single step non-autoregressive transformer for speech recognition,

Reference 52

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This paper cites Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical Distillation.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical Distillation

Reference 53

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Cif: Continuous integrate-and-fire for end-to- end speech recognition,

Reference 54

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This paper cites Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM

Reference 55

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Squeeze-and-excitation networks,

Reference 56

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Subunets: End-to-end hand shape and continuous sign language recognition,

Reference 57

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This paper cites Adabrowse: Adaptive video browser for efficient continuous sign language recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Adabrowse: Adaptive video browser for efficient continuous sign language recognition,

Reference 58

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Slowfast network for continuous sign language recognition,

Reference 59

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Signgraph: A sign sequence is worth graphs of nodes,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:17:22.281907Z

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Signbert+: Hand-model-aware self-supervised pre-training for sign language understanding,

Reference 61

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Gloss prior guided visual feature learning for continuous sign language recog- nition,

Reference 62

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers,

Reference 63

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Neural sign language translation,

Reference 64

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This paper cites Improving sign language translation with monolingual data by sign back-translation,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Improving sign language translation with monolingual data by sign back-translation,

Reference 65

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Deep residual learning for image recognition,

Reference 66

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This paper cites Imagenet: A large-scale hierarchical image database,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Imagenet: A large-scale hierarchical image database,

Reference 67

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This paper cites Video- based sign language recognition without temporal segmentation,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Video- based sign language recognition without temporal segmentation,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:17:20.909450Z

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Observation 3deea2bd-318d-4482-8627-e3c90da6b12c · outbound

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Swin-mstp: Swin transformer with multi- scale temporal perception for continuous sign language recognition,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:17:20.780400Z

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This paper cites Uni-Sign: Toward Unified Sign Language Understanding at Scale.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Uni-Sign: Toward Unified Sign Language Understanding at Scale

Reference 70

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Slowfast networks for video recognition,

Reference 71

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Rethinking spatiotem- poral feature learning: Speed-accuracy trade-offs in video classification,

Reference 72

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Observation 02f3a784-e8b4-496d-a72c-27fe797c6c81 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:17:20.438465Z

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This paper cites Dynamical semantic enhancement network for continuous sign language recognition,.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Dynamical semantic enhancement network for continuous sign language recognition,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:17:20.301245Z

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Observation 34e88a37-8fd4-42bf-92b7-d79665e219dd · outbound

This paper cites SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Reference 75

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Deep layer aggregation,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:17:20.146408Z

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Observation 16e7a78c-48e5-48be-aad4-c2564bddeb12 · outbound

This paper cites 1 and Fig.

DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition 1 and Fig

Reference 79

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Reference 80

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Available: https://api.semanticscholar.org/CorpusID: 195443370

Reference 2019

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Observation c51fa0e0-6a9e-4297-9123-7665ac4c28ac · outbound

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DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition Available: https://api.semanticscholar.org/CorpusID: 260926078

Reference 2023

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