Add BOBSL, ISL-HS, Sign-BD datasets to the datasets table and references.bi

src/datasets/BOBSL.json

@@ -0,0 +1,19 @@
+{
+    "pub": {
+      "name": "BOBSL",
+      "year": 2022,
+      "publication": "dataset:momeniAutomaticDenseAnnotation2022",
+      "url": "https://www.robots.ox.ac.uk/~vgg/data/bobsl/"
+    },
+    "features": [
+      "video:RGB",
+      "text:English"
+    ],
+    "language": "British Sign Language (BSL)",
+    "#items": 2281,
+    "#samples": "1.2M Sentences",
+    "#signers": 37,
+    "license": "non-commercial authorized academics",
+    "licenseUrl": "https://www.bbc.co.uk/rd/projects/extol-dataset",
+    "contact": "Samuel Albanie albanie[AT]robots.ox.ac.uk"
+  }

src/datasets/ISL-HS.json

@@ -0,0 +1,18 @@
+{
+    "pub": {
+      "name": "ISL-HS",
+      "year": 2017,
+      "publication": "dataset:oliveiraDatasetIrishSign2017",
+      "url": "https://github.com/marlondcu/ISL"
+    },
+    "features": [
+      "video:RGB",
+      "gloss:ISL-HandShapes"
+    ],
+    "language": "Irish Sign Language (ISL)",
+    "#items": 23,
+    "#samples": "468 videos available, 58,114 images extracted to show 23 handshapes",
+    "#signers": 6,
+    "license": null,
+    "licenseUrl": null
+  }

src/references.bib

@@ -2076,4 +2076,35 @@ @inproceedings{muller-etal-2023-considerations
     url = "https://aclanthology.org/2023.acl-short.60",
     doi = "10.18653/v1/2023.acl-short.60",
     pages = "682--693",
-}
+}
+
+@inproceedings{dataset:momeniAutomaticDenseAnnotation2022,
+  title = {Automatic {{Dense Annotation}} of~{{Large-Vocabulary Sign Language Videos}}},
+  booktitle = {Computer {{Vision}} -- {{ECCV}} 2022},
+  author = {Momeni, Liliane and Bull, Hannah and Prajwal, K. R. and Albanie, Samuel and Varol, G{\"u}l and Zisserman, Andrew},
+  editor = {Avidan, Shai and Brostow, Gabriel and Ciss{\'e}, Moustapha and Farinella, Giovanni Maria and Hassner, Tal},
+  year = {2022},
+  pages = {671--690},
+  publisher = {Springer Nature Switzerland},
+  address = {Cham},
+  doi = {10.1007/978-3-031-19833-5_39},
+  abstract = {Recently, sign language researchers have turned to sign language interpreted TV broadcasts, comprising (i) a video of continuous signing and (ii) subtitles corresponding to the audio content, as a readily available and large-scale source of training data. One key challenge in the usability of such data is the lack of sign annotations. Previous work exploiting such weakly-aligned data only found sparse correspondences between keywords in the subtitle and individual signs. In this work, we propose a simple, scalable framework to vastly increase the density of automatic annotations. Our contributions are the following: (1)~we significantly improve previous annotation methods by making use of synonyms and subtitle-signing alignment; (2)~we show the value of pseudo-labelling from a sign recognition model as a way of sign spotting; (3)~we propose a novel approach for increasing our annotations of known and unknown classes based on in-domain exemplars; (4)~on the BOBSL BSL sign language corpus, we increase the number of confident automatic annotations from 670K to 5M. We make these annotations publicly available to support the sign language research community.},
+  isbn = {978-3-031-19833-5},
+  langid = {english},
+  keywords = {Automatic dataset construction,Novel class discovery,Sign language recognition}
+}
+
+@inproceedings{dataset:samsSignBDWordVideoBasedBangla2023,
+  title = {{{SignBD-Word}}: {{Video-Based Bangla Word-Level Sign Language}} and {{Pose Translation}}},
+  shorttitle = {{{SignBD-Word}}},
+  booktitle = {2023 14th {{International Conference}} on {{Computing Communication}} and {{Networking Technologies}} ({{ICCCNT}})},
+  author = {Sams, Ataher and Akash, Ahsan Habib and Rahman, S. M. Mahbubur},
+  year = {2023},
+  month = jul,
+  pages = {1--7},
+  issn = {2473-7674},
+  doi = {10.1109/ICCCNT56998.2023.10306914},
+  urldate = {2024-03-13},
+  abstract = {Bangla sign language (BdSL) is a complete and independent natural sign language with its own linguistic characteristics. While there exists video datasets for well-known sign languages, there is currently no available dataset for word-level BdSL. In this study, we present a video-based word-level dataset for Bangla sign language, called SignBD-Word, consisting of 6000 sign videos representing 200 unique words. The dataset includes full and upper-body views of the signers, along with 2D body pose information. To evaluate the dataset, a number of deep learning-based algorithms for video classification and pose translation are used. The experimental results reveal that the recently developed SlowFast and I3D methods achieve higher accuracy for RGB and body pose data respectively, compared to the traditional attention-based approach. Furthermore, the experiments indicate that pix2pixHD outperforms CycleGAN and SPADE for human- to-human sign pose translation from bodypose data.},
+  keywords = {Assistive technologies,BdSL,Deep learning,Face recognition,Generative adversarial networks,Generative Adversarial Networks,Gesture recognition,Linguistics,Pose Translation,Production,Sign Language,Vocabulary}
+}