Pith. sign in

Paper Citation Record · LEDGER

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model

As of 22 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2608.06252.

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

pith.paper-citation-record.v1
2608.06252 v2

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measured 57 of 57 reference resolution

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57 of 57 outbound references displayed

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Outbound references

Observation a958387d-2baa-4e25-a3cb-c80afcb4c2e1 · outbound

This paper cites Sanity checks for saliency maps, in: Advances in Neural Information Processing Systems (NeurIPS), pp.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Sanity checks for saliency maps, in: Advances in Neural Information Processing Systems (NeurIPS), pp

Reference 1

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Observation 2782a4a3-09a0-474a-8c74-c881cafdea1d · outbound

This paper cites RSBdSL38-v1.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model RSBdSL38-v1

Reference 2

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Observation 622e2195-d15f-4c97-9e4f-fd474fd561d7 · outbound

This paper cites Deep learning for sign language recognition: Current techniques, benchmarks, and open issues.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Deep learning for sign language recognition: Current techniques, benchmarks, and open issues

Reference 3

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Observation 8cc386a8-c9f9-4e2c-91ae-ed79c5b9942d · outbound

This paper cites Two dimensional convolutional neural network approach for real-time bangla sign language characters recognition and translation.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Two dimensional convolutional neural network approach for real-time bangla sign language characters recognition and translation

Reference 4

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Observation d10ff238-e630-46c8-b468-a58b237611f0 · outbound

This paper cites Recognition of bangla sign language characters and digits using cnn, in: 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET), IEEE.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Recognition of bangla sign language characters and digits using cnn, in: 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET), IEEE

Reference 5

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Observation d4e6eb9c-07cf-4995-9748-ea365367f298 · outbound

This paper cites Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks, in: IEEE Winter Conference on Applications of Computer Vision (W ACV), IEEE.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks, in: IEEE Winter Conference on Applications of Computer Vision (W ACV), IEEE

Reference 6

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Observation e19f2830-0cfb-4f3c-93a0-047b810e31d6 · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Xception: Deep learning with depthwise separable convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

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Observation 16325e40-207f-447a-b2b0-70e411cd1744 · outbound

This paper cites A hybrid approach for bangla sign language recognition using deep transfer learning model with random forest classifier.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model A hybrid approach for bangla sign language recognition using deep transfer learning model with random forest classifier

Reference 8

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Observation 1490d7a7-7117-49de-8538-1d7fa4a158a1 · outbound

This paper cites Imagenet: A large-scale hierarchical image database, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Imagenet: A large-scale hierarchical image database, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 9

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Observation 70457036-ebf0-4c2e-8aae-c9602b4846e2 · outbound

This paper cites Explainable federated learning for privacy-preserving bangla sign language detection.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Explainable federated learning for privacy-preserving bangla sign language detection

Reference 10

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Unresolved cited work

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Observation 60159ae7-e402-40d0-b349-64df3d5a2ef0 · outbound

This paper cites Baust lipi: A bdsl dataset with deep learning based bangla sign language recognition, in: Proceedings of the 3rd International Conference on Computing Advancements, pp.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Baust lipi: A bdsl dataset with deep learning based bangla sign language recognition, in: Proceedings of the 3rd International Conference on Computing Advancements, pp

Reference 12

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This paper cites Recognition of bangladeshi sign language (bdsl) words using deep convolutional neural networks (dcnns).

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Recognition of bangladeshi sign language (bdsl) words using deep convolutional neural networks (dcnns)

Reference 13

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Unresolved cited work

Reference 14

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This paper cites Bdsl 49: A comprehensive dataset of bangla sign language.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Bdsl 49: A comprehensive dataset of bangla sign language

Reference 15

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Observation 6960b4df-6102-4e7a-b471-4caec3f64254 · outbound

This paper cites Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 16

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Observation 0a98a701-2159-4039-bd0e-a158f627a8e8 · outbound

This paper cites Bdsl36: A dataset for bangladeshi sign letters recognition, in: Proceedings of the Asian Conference on Computer Vision (ACCV) Workshops.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Bdsl36: A dataset for bangladeshi sign letters recognition, in: Proceedings of the Asian Conference on Computer Vision (ACCV) Workshops

Reference 17

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Observation 9a34e743-228c-48f7-bdf7-1d9e69f2c840 · outbound

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Squeeze-and-excitation networks, in: Proceedings of the IEEE Confer- ence on Computer Vision and Pattern Recognition (CVPR), pp

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Observation d0d0281a-0100-4bec-901a-d6060e990f30 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International Conference on Machine Learning (ICML), pp.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International Conference on Machine Learning (ICML), pp

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This paper cites Sign language recognition for bangla alphabets using deep learning methods, in: 2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI), IEEE.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Sign language recognition for bangla alphabets using deep learning methods, in: 2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI), IEEE

Reference 20

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Observation 2c458db0-df3e-463d-97a0-5bd53ca6c402 · outbound

This paper cites Ku-bdsl: An open dataset for bengali sign language recognition.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Ku-bdsl: An open dataset for bengali sign language recognition

Reference 21

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This paper cites Combining state-of-the-art pre-trained deep learning models: A novel approach for bangla sign language recognition using max voting ensemble.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Combining state-of-the-art pre-trained deep learning models: A novel approach for bangla sign language recognition using max voting ensemble

Reference 22

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Unresolved cited work

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Imagenet classification with deep convolutional neural networks

Reference 24

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Rethink- ing vision transformers for mobilenet size and speed, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Decoupled Weight Decay Regularization

Reference 26

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model A unified approach to interpreting model predictions

Reference 27

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Reference 28

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Separable Self-attention for Mobile Vision Transformers

Reference 29

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Bangla sign alphabet recognition with zero-shot and transfer learning

Reference 30

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model DINOv2: Learning Robust Visual Features without Supervision

Reference 31

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Deafness and hearing loss

Reference 32

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model RISE: Randomized Input Sampling for Explanation of Black-box Models

Reference 33

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This paper cites Bangla sign language (bdsl) alphabets and numerals classification using a deep learning model.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Bangla sign language (bdsl) alphabets and numerals classification using a deep learning model

Reference 34

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This paper cites Mobilenetv4: Universal models for the mobile ecosystem, in: European Conference on Computer Vision (ECCV), pp.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Mobilenetv4: Universal models for the mobile ecosystem, in: European Conference on Computer Vision (ECCV), pp

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Bengali-sign: A machine learning-based bengali sign language interpretation for deaf and non-verbal people

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This paper cites Searching for Activation Functions.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Searching for Activation Functions

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This paper cites Sign language recognition: A deep survey.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Sign language recognition: A deep survey

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This paper cites Bdsl47: A complete depth-based bangla sign alphabet and digit dataset.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Bdsl47: A complete depth-based bangla sign alphabet and digit dataset

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Sign language: a systematic review on classification and recognition

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This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE Interna- tional Conference on Computer Vision (ICCV), pp.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Grad-cam: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE Interna- tional Conference on Computer Vision (ICCV), pp

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Multimodal ensemble approach leveraging spatial, skeletal, and edge features for enhanced bangla sign language recognition

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This paper cites Deep learning-based bangla sign language detection with an edge device.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Deep learning-based bangla sign language detection with an edge device

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This paper cites Real-time bangla sign language recognition using transfer learn- ing model, in: 2024 International Conference on Innovations in Science, Engineering and Technology (ICISET), IEEE.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Real-time bangla sign language recognition using transfer learn- ing model, in: 2024 International Conference on Innovations in Science, Engineering and Technology (ICISET), IEEE

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This paper cites Dropout: A simple way to prevent neural networks from overfitting.

Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Dropout: A simple way to prevent neural networks from overfitting

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model On the importance of initialization and momen- tum in deep learning, in: International Conference on Machine Learning (ICML), pp

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model The linguistics of British Sign Language: an introduction

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Efficientnetv2: Smaller models and faster training, in: International Conference on Machine Learning (ICML), pp

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Ghostnetv2: Enhance cheap operation with long-range attention

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Unresolved cited work

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Unresolved cited work

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Disabling hearing impairment in the bangladeshi population

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model A vision transformer-based fine-tuned dinov2 model for bangla sign language recognition

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Observation 7b104dd4-4c22-4a99-843f-a8a1d59aa376 · outbound

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Efficient object localization using convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Linguistics of American sign language: An introduction

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Cbam: Convolutional block attention module, in: Proceedings of the European Conference on Computer Vision (ECCV), pp

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Observation c90a3217-7a11-41f8-b056-c63fbd342c2e · outbound

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Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model Aggregated residual transformations for deep neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

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