Summary of Jep-kd: Joint-embedding Predictive Architecture Based Knowledge Distillation For Visual Speech Recognition, by Chang Sun et al.
JEP-KD: Joint-Embedding Predictive Architecture Based Knowledge Distillation for Visual Speech Recognition
by Chang Sun, Hong Yang, Bo Qin
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Joint-Embedding Predictive Architecture (JEPA) approach, dubbed JEP-KD, attempts to bridge the gap in theoretical performance between Visual Speech Recognition (VSR) and Automatic Speech Recognition (ASR). This is achieved through knowledge distillation using a generative network within the embedding layer. The goal is to enhance semantic feature extraction from video encoders while aligning them with pre-trained ASR model encoders. A comprehensive multimodal, multistage training regimen bolsters the robustness and efficacy of JEP-KD. Experimental results show significant performance improvements for VSR models across various platforms, hinting at broader applications in multimodal tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual Speech Recognition (VSR) is harder than Automatic Speech Recognition (ASR). To make VSR better, researchers created a new way to train models using information from both audio and video. They called it Joint-Embedding Predictive Architecture (JEPA), or JEP-KD for short. It’s like a special tool that helps the model learn more about what people are saying by looking at their faces. The results show that this approach makes VSR better, and it might even be useful for other tasks where we need to understand both audio and video. |
Keywords
* Artificial intelligence * Embedding * Feature extraction * Knowledge distillation
