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

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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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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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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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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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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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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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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

Reference 7

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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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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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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

Reference 11

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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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This paper cites an unresolved cited work.

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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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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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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This paper cites Squeeze-and-excitation networks, in: Proceedings of the IEEE Confer- ence on Computer Vision and Pattern Recognition (CVPR), pp.

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

Reference 18

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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

Reference 19

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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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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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This paper cites an unresolved cited work.

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

Reference 23

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This paper cites Imagenet classification with deep convolutional neural networks.

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

Reference 25

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This paper cites Decoupled Weight Decay Regularization.

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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This paper cites MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer.

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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This paper cites Bangla sign alphabet recognition with zero-shot and transfer learning.

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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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

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

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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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This paper cites RISE: Randomized Input Sampling for Explanation of Black-box Models.

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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This paper cites Bengali-sign: A machine learning-based bengali sign language interpretation for deaf and non-verbal people.

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

Reference 36

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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

Reference 39

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This paper cites Sign language: a systematic review on classification and recognition.

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

Reference 41

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This paper cites Multimodal ensemble approach leveraging spatial, skeletal, and edge features for enhanced bangla sign language recognition.

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

Reference 42

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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

Reference 43

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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

Reference 44

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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

Reference 45

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This paper cites On the importance of initialization and momen- tum in deep learning, in: International Conference on Machine Learning (ICML), pp.

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

Reference 46

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This paper cites The linguistics of British Sign Language: an introduction.

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

Reference 47

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This paper cites Eﬀicientnetv2: Smaller models and faster training, in: International Conference on Machine Learning (ICML), pp.

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

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:29.886413Z

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This paper cites Ghostnetv2: Enhance cheap operation with long-range attention.

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

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:29.759629Z

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

Reference 50

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

Reference 51

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This paper cites Disabling hearing impairment in the bangladeshi population.

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

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:29.497202Z

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This paper cites A vision transformer-based fine-tuned dinov2 model for bangla sign language recognition.

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

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:29.360466Z

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Observation ad4000e1-caf6-42c5-aae6-1d3946bbf3a0 · outbound

This paper cites Eﬀicient object localization using convolutional networks, 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 Eﬀicient object localization using convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:29.249847Z

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Observation 05c72db0-8cfc-41d9-90de-c2d0804ae844 · outbound

This paper cites Linguistics of American sign language: An introduction.

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

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:29.128238Z

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Observation c7bf6418-937b-4d98-bca6-77230f36ba7a · outbound

This paper cites Cbam: Convolutional block attention module, in: Proceedings of the European Conference on Computer Vision (ECCV), pp.

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

Reference 56

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This paper cites Aggregated residual transformations for deep neural networks, 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 Aggregated residual transformations for deep neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:29.015788Z

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