Pith. sign in

Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 80 of 80 outbound references and 0 inbound Pith citation observations for arXiv:2507.03339.

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

pith.paper-citation-record.v1
2507.03339 v1

Coverage vector

measured 80 of 80 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:17:19.116974Z

measured 80 of 80 standing notices

One-hop event checks from named stored sources.

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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

80 of 80 outbound references displayed

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External citation measurements

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Outbound references

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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Observation 88f90463-cdb9-4199-99b2-3da0a09408f3 · outbound

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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Observation caa837c4-8ae6-4e43-9ddf-9c9fc1e2a66c · outbound

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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Source-reported events for the cited work

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Observation 48596207-1063-45b5-a852-b4710610c144 · outbound

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

Resolution
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Source-reported events for the cited work

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Observation 64c04c03-169e-4189-9c6b-32f81b65c73a · outbound

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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Source-reported events for the cited work

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Observation 4e9c4aef-bd7b-44b2-ad95-cd1cd7d6e013 · outbound

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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Source-reported events for the cited work

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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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Source-reported events for the cited work

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Observation 339d831e-25bc-4896-8e5f-4907d7c1ffab · outbound

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

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 8efe0889-cec4-4fce-8518-28e4adbbd63b · outbound

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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Observation 5b4f6d38-026b-484a-820d-fdac74b7a29b · outbound

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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Observation ea2bcf07-a67d-4f92-b907-55b83662eb7c · outbound

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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verified exact
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Source-reported events for the cited work

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Observation e360df12-2294-44fe-83d9-dc04374a6bbe · outbound

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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Source-reported events for the cited work

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Observation 7df8bed8-2eee-4ad0-b910-94c759d534ad · outbound

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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Source-reported events for the cited work

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Observation db83918d-ef45-4ecb-8bcd-798fbcd141bb · outbound

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

Resolution
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Source-reported events for the cited work

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Observation 7519fbee-a89e-4c04-9529-4ff101803610 · outbound

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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Source-reported events for the cited work

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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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Source-reported events for the cited work

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Observation c39a40b5-846e-407a-b233-614f75782d7f · outbound

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

Resolution
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Observation ee77c9a2-d5a2-4a88-ba2b-e26c091ac4b5 · outbound

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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Source-reported events for the cited work

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Observation 4b631793-746a-4435-83da-3567e6c890e4 · outbound

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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Observation 17962522-734c-41ea-a2a2-c79a1d054242 · outbound

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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Observation 14e0046d-3e20-4e92-ad43-b969b969b105 · outbound

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

Resolution
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Source-reported events for the cited work

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Observation 2b5b05c9-ba53-4b29-9d47-1317d7030c9a · outbound

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

Resolution
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Source-reported events for the cited work

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Observation 97db633a-bc0f-4df7-831b-116116dca5a9 · outbound

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

Resolution
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Observation 75f942c1-1279-4fd7-82b9-9bce8f3178a0 · outbound

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

Resolution
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Source-reported events for the cited work

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Observation 144af6aa-43b1-4ab8-a85b-dd4b67b359b5 · outbound

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

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 44286db8-9f64-49d3-8df9-e9664b7775b2 · outbound

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

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 4788dd88-6664-41cb-88e7-15b7e96a3c06 · outbound

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

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation e9b9e080-ab7f-4a7e-96f7-beb1bde82b21 · outbound

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

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation b8397038-ed5e-4cf9-a331-b85d79638397 · outbound

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

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation d684b0ce-f16d-4af3-82aa-76bba0a39508 · outbound

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

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 700d2cbc-240c-4d73-bba1-e3f93e7ab84e · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:25.576100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation d059cdb6-337e-46a4-8833-e8c26c8dc780 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:25.392087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation e275965e-bccb-4c3f-932e-49f70b6ddd64 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:25.255340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:15.541925Z digest=sha256:d8cc2299d899ad4b0cdc94c9ee5d19c68a6ff4a978c9dd9d84ade405ee3c837a

Observation 2830bb86-d85b-4715-852d-abd7c1290842 · outbound

This paper cites C2st: Cross-modal contextualized sequence transduction for continuous sign language recognition,.

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:25.141121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:15.681525Z digest=sha256:f223ca1bc69022a86cfd85564f32dd30c51520d49a4090d19e0e00d10df5b024

Observation c1207624-74d4-458a-9bf1-801751f60351 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:24.936440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:15.787524Z digest=sha256:7be2a2ada6c6b367cb83775857f3866c1f72ba8cec31c781f5496dd674f5edf9

Observation 723be608-1763-48ad-a190-b0c8e8f6c0a2 · outbound

This paper cites Auto-avsr: Audio-visual speech recognition with automatic labels,.

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:24.829162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:15.872130Z digest=sha256:8ee556f2c9fc5452088eba97e1717d2ad47462a1be7f3cf6eae939d79a66b74a

Observation 4df6146c-6541-4d85-9a60-129bb94a3084 · outbound

This paper cites End-to-end audio-visual speech recognition with conformers,.

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:24.680126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:15.906258Z digest=sha256:d422e7e4ddfcffe37627ac56a927a49188adf8fe7ab67106df53760d892b1045

Observation 2b4c250a-08d5-4ac8-bbc2-32cd82550c63 · outbound

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

Resolution
unresolved
no resolver link, observed 2026-08-06T20:17:16.008587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:17:16.008587Z digest=sha256:bc9ac927585f0ca06b1c3154bbaddcda94cddd63e61d811938bab06a6a8b0c08

Observation 60c7b0b8-d7f1-4e82-98fe-90d7febd7681 · outbound

This paper cites Re-sign: Re-aligned end-to-end sequence modelling with deep recurrent cnn-hmms,.

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:24.508103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:16.093326Z digest=sha256:546b46d9977d1f821c4f09309b242bea98b73eb66b341c7bb76a9d85a76ea783

Observation 4ced9555-5c31-448f-9925-ea21fb7168ad · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:24.346439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:16.180923Z digest=sha256:78cb405fd80cdf5a569a377de197ce109ede5bc065f21d3ddfbeae27dacf44d2

Observation 6f584b69-d2d6-4da6-91de-1c48fad57c39 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:24.216630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:16.285741Z digest=sha256:eb2837ac5787e2a77cd0095dbfc00ffd29832daf52934f82b7830e0a315d29dc

Observation 13e3d18b-0b27-4085-93b6-1845aef1390e · outbound

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

Resolution
unresolved
no resolver link, observed 2026-08-06T20:17:16.332385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:17:16.332385Z digest=sha256:1ebd1d4c30f825186159350575226054e1fd3b2907e7908e779c54a662c4efdb

Observation 8670fa75-4010-40cf-9b73-33d3d8e59270 · outbound

This paper cites Condconv: Conditionally parameterized convolutions for efficient inference,.

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

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:24.094184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:16.453795Z digest=sha256:bbae9bbaec71f54123167d3eab56d2638bd3d4326f2c85009b4d0f8758e45d08

Observation 1ec0332c-499b-4e1c-8523-3223af84fb91 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:23.935723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:16.572348Z digest=sha256:3c2f2132c37199e7110c2b799f44d576c4a797367ebc7eb54d7a211f04ab8a30

Observation f229d1b1-3cc8-48fe-999a-88607b28c6b9 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:23.805587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:16.667977Z digest=sha256:e7ee46c412e139b31a7a72ba69b46e772b75f33e0c002b2e805fd0b58bd1970b

Observation 90f84a98-35f6-4479-8184-66c5e826eae1 · outbound

This paper cites HyperNetworks.

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

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T20:17:16.791747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:17:16.791747Z digest=sha256:085c8b635f2293c0cae7e3b4cc0feda0efe1eb75c841ba87a686c18c5fd88ff0

Observation a82afbe7-20a7-4fa1-9347-6d4ac3e71dad · outbound

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

Resolution
unresolved
no resolver link, observed 2026-08-06T20:17:16.878904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:17:16.878904Z digest=sha256:9a713d1ac896e5d0f816c31bd1bba338873d52e728f8f42d37629fd9681d5a96

Observation 9b331859-ecda-4c7e-9f4f-77731cf6666c · outbound

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

Resolution
verified exact
local_arxiv, observed 2026-08-06T20:17:19.355902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:16.962756Z digest=sha256:2f85158e2dd33fae8b253eede01237336160b4e79b0b0f165c6cf2135cbbc418

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

Resolution
unresolved
no resolver link, observed 2026-08-06T20:17:17.086136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:17:17.086136Z digest=sha256:acc06867eff74915493735524df72620ec5dfa430dc997d200e7142a6b3b70ed

Observation fd2b71f8-cb12-4bb4-9412-92d2c4f6411f · outbound

This paper cites Boundary and context aware training for cif-based non-autoregressive end-to-end asr,.

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:23.638803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:17.200461Z digest=sha256:60d812b576e36466ac929db9c72a6acc660a6b5171d926b45197be1c10dbc33c

Observation 229927f7-a7da-439a-97ad-7fd423266d32 · outbound

This paper cites Cass-nat: Ctc alignment-based single step non-autoregressive transformer for speech recognition,.

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:23.450263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:17.299797Z digest=sha256:fa9492483882b736e5d29c23572ed8eee4dac2f81549772bd013fde3e2e436e3

Observation 579dc439-463e-415e-b504-db82fd98d929 · outbound

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

Resolution
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no resolver link, observed 2026-08-06T20:17:17.444801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:17:17.444801Z digest=sha256:d7f8033dc9bc2798afcac8bc4818b905252f8ebdaf49650dd6d43045318a5cb1

Observation ee1a4022-89ce-43f5-ba2e-b7085fe31836 · outbound

This paper cites Cif: Continuous integrate-and-fire for end-to- end speech recognition,.

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:23.201088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:17.508168Z digest=sha256:7407d23fae8a15914debf3aa994c322f066530af25a17c6df1a40d5178fe0393

Observation c604c4a4-6075-473b-a7a9-f9eb16e9aa87 · outbound

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

Resolution
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no resolver link, observed 2026-08-06T20:17:17.579335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:17:17.579335Z digest=sha256:dafef520e08bbffbcfd4ef954f916e1512d1f87a592d95f3a98d2258220db9be

Observation 19bf9826-b087-4981-84f6-5f9145df8d85 · outbound

This paper cites Squeeze-and-excitation networks,.

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

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-06T20:17:17.650840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:17:17.650840Z digest=sha256:079c6d8461e2999db974bebf16362d607b5839cf6193970ecdac8275e6038239

Observation 1ab116a6-5ae2-491f-96c3-9ed5bc92a77d · outbound

This paper cites Subunets: End-to-end hand shape and continuous sign language recognition,.

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:23.007544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:17.692621Z digest=sha256:bd3419f57f518f73b141dae0368ca0470fb7340db2e82213e7e36c9dbc10522f

Observation e3c867bc-9cb7-43ac-b2e5-7adf271e9a26 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:22.782832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:17.789823Z digest=sha256:08de3a262aa9138de12a535ba807218d6157367dde46bc8a88caa669ee795a2b

Observation 59e39ed6-cbf4-472a-9e8d-c81a461fceed · outbound

This paper cites Slowfast network for continuous sign language recognition,.

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

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:22.451427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:17.909097Z digest=sha256:5fe99b94a9d134ce21984b0c34e482f3c999f7ebd927da3ab93063c561118633

Observation 62b670a5-56a3-4d79-983e-702eec5c3e73 · outbound

This paper cites Signgraph: A sign sequence is worth graphs of nodes,.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:17.989230Z digest=sha256:27b3a9a74aeaf8f2a2934540e46d7878394b2f1180ed230f33b732a1f541deda

Observation e4274921-35d9-4cb2-84fd-7b6c4bac137d · outbound

This paper cites Signbert+: Hand-model-aware self-supervised pre-training for sign language understanding,.

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:22.111703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:18.024915Z digest=sha256:ab26e5a78e06c96b4c3671c45d6221ea3568eaec7615ccca313b4300ae7f4503

Observation eb9b9ce4-de60-4d88-a50a-1c246635c784 · outbound

This paper cites Gloss prior guided visual feature learning for continuous sign language recog- nition,.

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:21.870440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:18.091011Z digest=sha256:bc9a3b4247633c3cbf6d923f0b94d4f8d02e14a2f66ff52b6ce87c168ca654f1

Observation 2a5ea329-bf02-42ac-94f7-fdd7f043cc6d · outbound

This paper cites Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers,.

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:21.678510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:18.194879Z digest=sha256:c1a813cac04b76292fbf8e9529dedf07422d04edb1495f2c2c7be7cd7982334e

Observation c1258b18-6f8f-4e1b-8229-6f3467c0467e · outbound

This paper cites Neural sign language translation,.

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

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:21.525747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:18.260604Z digest=sha256:0b4148cb131d1514e939f32e9655891fb5177e5aff3a54d505fd83d88f79ba5b

Observation 6f917302-e791-47db-8f2c-b4376c936f59 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:21.342153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:18.344084Z digest=sha256:d9128ae4d5dc93db41c6b4e06c4c9284b9573bf2488885d0049c01a452e16ac9

Observation 42dcae62-6c5d-4537-b16f-0dd60bab11fd · outbound

This paper cites Deep residual learning for image recognition,.

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

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:21.202103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:17:18.430876Z digest=sha256:3350c3e3cbafcbddeffc5576e1101bee1c7adb612b96fcb680232abcc83d2359

Observation 09f7b267-34c2-4b59-8330-ad67f755974d · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:17:21.083917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 7ef078d2-fc1f-4f01-8271-95b3515bb05f · outbound

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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

This paper cites Swin-mstp: Swin transformer with multi- scale temporal perception for continuous sign language recognition,.

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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 27263585-69d2-4872-af2b-f1bb80d8b66c · outbound

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

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f4858e31-ca22-4e94-967d-3b4d5f175b74 · outbound

This paper cites Slowfast networks for video recognition,.

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

Reference 71

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3d6907d6-b700-416e-baa8-914607a61d37 · outbound

This paper cites Rethinking spatiotem- poral feature learning: Speed-accuracy trade-offs in video classification,.

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

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 87953fc7-d9ee-470e-be81-3419a2a662e3 · outbound

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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8e34a10a-62b1-4ef7-b2fb-26b9fc7ee703 · outbound

This paper cites Deep layer aggregation,.

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

Reference 76

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation ff48fd76-97ba-42f7-b215-e583f2d66ba9 · outbound

This paper cites an unresolved cited work.

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

Reference 80

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:17:19.793875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 0be70a25-8c50-477f-8d49-55bfa3d72e6a · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 195443370.

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

Reference 2019

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

This paper cites Available: https://api.semanticscholar.org/CorpusID: 260926078.

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

Reference 2023

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Pith citing papers

No inbound Pith citation observations are available.