Update SignX README docs

README.md

@@ -1,97 +1,38 @@
-# SLTUNET: A Simple Unified Model for Sign Language Translation (ICLR 2023)
+# SignX
 
-[**Paper**](https://openreview.net/forum?id=EBS4C77p_5S) | 
-[**Highlights**](#paper-highlights) |
-[**Overview**](#model-visualization) |
-[**DGS3-T**](#dgs3-t) |
-[**Training&Eval**](#training-and-evaluation) |
-[**Model Performance**](#performance) |
-[**Citation**](#citation)
+https://signerx.github.io/SignX/
 
+This is a CSLR model. In the future, it will be further improved to become an open-world SLT model. 
 
-* Update (2023/07/09): We release the trained model for phoenix and csldaily at [here](https://data.statmt.org/bzhang/iclr2023_sltunet/). See `infer.sh` for details.
+Currently, it has been pluginized, that is, the root directory contains viser_backend.py and index.js. These two files will enable this repository to be recognized by the control panel as a plugin. 
 
+Therefore, users should first clone the panel, configure the panel environment, and then place this repository in the plugin folder.
 
-## Paper Highlights
+## Environment Setup
 
-Among thousands of languages globally, some are written, some are spoken, while some are signed.
-Sign languages are unique **natural** languages widely used in Deaf communities. They express
-meaning through hand gestures, body movements and facial expressions, and are often in a video form.
-We refer the readers to [Sign language Processing](https://research.sign.mt/) for a better understanding
-of sign languages.
+SignX inference uses two conda environments:
 
-In this study, we aim at improving sign language translation, i.e. translating information from
-sign languages (in a video) to spoken languages (in text). We address the video-text modality gap and 
-the training data scarcity issue via multi-task learning and unified modeling. 
+- `signx-slt`: PyTorch / SMKD stage (RGB feature extraction)
+- `slt_tf1`: TensorFlow 1.15 / SLTUNET stage (Recognition based on feature space)
 
-Briefly,
-- We propose a simple unified model, SLTUNET, for SLT, and show that jointly modeling
-multiple SLT-related tasks benefits the translation.
-- We propose a set of optimization techniques for SLTUNET aiming at an improved trade-off
-between model capacity and regularization, which also helps SLT models for single tasks.
-- SLTUNET performs competitively to previous methods and yields the new state-of-the-art
-performance on CSL-Daily.
-- We use the DGS Corpus and propose [DGS3-T](#dgs3-t) for end-to-end SLT, with larger
-scale, richer topics and more significant challenges than existing datasets.
 
-## Model Visualization
+```bash
+conda env create -f env/signx-slt.yml
+conda activate signx-slt
+pip install -r env/signx-slt.txt
 
-![Overview of ur proposal](model.png)
-
-
-## DGS3-T
-
-* Similar to PHOENIX-2014T and CSL-Daily, DGS3-T is a dataset used for the study of SLT, consisting of sign videos and text translations.
-
-* Different from these previous datasets, DGS3-T is larger at scale, covering broader domains and topics with more signers.
-
-* DGS3-T represents more practical challenges in SLT. We encourage researchers to consider it for SLT research.
-
-
-**DGS3-T Licensing**
-
-DGS3-T is based on [the Public DGS Corpus](https://www.sign-lang.uni-hamburg.de/meinedgs/ling/license_en.html). The license of the Public DGS Corpus does not allow any computational research except if express
-permission is given by the University of Hamburg.
-
-**Constructing DGS3-T**
-
-Please check out [dgs3-t](./dgs3-t) for details.
-
-## Requirement
-
-The source code is based on older tensorflow.
-
-- python==3.8
-- tensorflow==1.15
-
-
-## Training and Evaluation
-
-Training includes two phrase: 1) pretrain sign embeddings; 2) train SLTUNet model.
-
-Please check out [example](./example) for details.
-
-
-## Performance
-
-![Resulst on Phoenix](phoenix.png)
-
-Check out [our paper](https://openreview.net/forum?id=EBS4C77p_5S) for more results on CSLDaily and DGS3-T.
+conda env create -f env/slt_tf1.yml
+conda activate slt_tf1
+pip install -r env/slt_tf1.txt
+```
 
-* Update (2023/04/02): note, for CSL-Daily, **we always adopt subword preprocessing (NOT character) for the target text 
-and gloss sequence during training and inference**; 
-We post-process the generated subword sequence into a character sequence at evaluation for char-level BLEU.
+Quick check:
 
-## Citation
+```bash
+conda activate signx-slt
+python -V
 
-If you draw any inspiration from our study, please consider to cite our paper:
-```
-@inproceedings{
-zhang2023sltunet,
-title={{SLTUNET}: A Simple Unified Model for Sign Language Translation},
-author={Biao Zhang and Mathias M{\"u}ller and Rico Sennrich},
-booktitle={The Eleventh International Conference on Learning Representations },
-year={2023},
-url={https://openreview.net/forum?id=EBS4C77p_5S}
-}
+conda activate slt_tf1
+python -V
 ```
+

README_baseline.md

@@ -0,0 +1,97 @@
+# SLTUNET: A Simple Unified Model for Sign Language Translation (ICLR 2023)
+
+[**Paper**](https://openreview.net/forum?id=EBS4C77p_5S) | 
+[**Highlights**](#paper-highlights) |
+[**Overview**](#model-visualization) |
+[**DGS3-T**](#dgs3-t) |
+[**Training&Eval**](#training-and-evaluation) |
+[**Model Performance**](#performance) |
+[**Citation**](#citation)
+
+
+* Update (2023/07/09): We release the trained model for phoenix and csldaily at [here](https://data.statmt.org/bzhang/iclr2023_sltunet/). See `infer.sh` for details.
+
+
+## Paper Highlights
+
+Among thousands of languages globally, some are written, some are spoken, while some are signed.
+Sign languages are unique **natural** languages widely used in Deaf communities. They express
+meaning through hand gestures, body movements and facial expressions, and are often in a video form.
+We refer the readers to [Sign language Processing](https://research.sign.mt/) for a better understanding
+of sign languages.
+
+In this study, we aim at improving sign language translation, i.e. translating information from
+sign languages (in a video) to spoken languages (in text). We address the video-text modality gap and 
+the training data scarcity issue via multi-task learning and unified modeling. 
+
+Briefly,
+- We propose a simple unified model, SLTUNET, for SLT, and show that jointly modeling
+multiple SLT-related tasks benefits the translation.
+- We propose a set of optimization techniques for SLTUNET aiming at an improved trade-off
+between model capacity and regularization, which also helps SLT models for single tasks.
+- SLTUNET performs competitively to previous methods and yields the new state-of-the-art
+performance on CSL-Daily.
+- We use the DGS Corpus and propose [DGS3-T](#dgs3-t) for end-to-end SLT, with larger
+scale, richer topics and more significant challenges than existing datasets.
+
+## Model Visualization
+
+![Overview of ur proposal](model.png)
+
+
+## DGS3-T
+
+* Similar to PHOENIX-2014T and CSL-Daily, DGS3-T is a dataset used for the study of SLT, consisting of sign videos and text translations.
+
+* Different from these previous datasets, DGS3-T is larger at scale, covering broader domains and topics with more signers.
+
+* DGS3-T represents more practical challenges in SLT. We encourage researchers to consider it for SLT research.
+
+
+**DGS3-T Licensing**
+
+DGS3-T is based on [the Public DGS Corpus](https://www.sign-lang.uni-hamburg.de/meinedgs/ling/license_en.html). The license of the Public DGS Corpus does not allow any computational research except if express
+permission is given by the University of Hamburg.
+
+**Constructing DGS3-T**
+
+Please check out [dgs3-t](./dgs3-t) for details.
+
+## Requirement
+
+The source code is based on older tensorflow.
+
+- python==3.8
+- tensorflow==1.15
+
+
+## Training and Evaluation
+
+Training includes two phrase: 1) pretrain sign embeddings; 2) train SLTUNet model.
+
+Please check out [example](./example) for details.
+
+
+## Performance
+
+![Resulst on Phoenix](phoenix.png)
+
+Check out [our paper](https://openreview.net/forum?id=EBS4C77p_5S) for more results on CSLDaily and DGS3-T.
+
+* Update (2023/04/02): note, for CSL-Daily, **we always adopt subword preprocessing (NOT character) for the target text 
+and gloss sequence during training and inference**; 
+We post-process the generated subword sequence into a character sequence at evaluation for char-level BLEU.
+
+## Citation
+
+If you draw any inspiration from our study, please consider to cite our paper:
+```
+@inproceedings{
+zhang2023sltunet,
+title={{SLTUNET}: A Simple Unified Model for Sign Language Translation},
+author={Biao Zhang and Mathias M{\"u}ller and Rico Sennrich},
+booktitle={The Eleventh International Conference on Learning Representations },
+year={2023},
+url={https://openreview.net/forum?id=EBS4C77p_5S}
+}
+```