{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/accurate-and-resource-efficient-lipreading","title":"Accurate and Resource-Efficient Lipreading with Efficientnetv2 and Transformers","arxiv_id":null,"date":"2022-05-23","proceeding":"ICASSP 2022 5","authors":["Alexandros Koumparoulis","Gerasimos Potamianos"],"abstract":"We present a novel resource-efficient end-to-end architecture for lipreading that achieves state-of-the-art results on a popular and challenging benchmark. In particular, we make the following contributions: First, inspired by the recent success of the EfficientNet architecture in image classification and our earlier work on resource-efficient lipreading models (MobiLipNet), we introduce Efficient-Nets to the lipreading task. Second, we show that the currently most popular in the literature 3D front-end contains a max-pool layer that prohibits networks from reaching superior performance and propose its removal. Finally, we improve our system’s back-end robustness by including a Transformer encoder. We evaluate our proposed system on the “Lipreading In-The-Wild” (LRW) corpus, a database containing short video segments from BBC TV broadcasts. The proposed network (T-variant) attains 88.53% word accuracy, a 0.17% absolute improvement over the current state-of-the-art, while being five times less computationally intensive. Further, an up-scaled version of our model (L-variant) achieves 89.52%, a new state-of-the-art result on the LRW corpus.","url_abs":"https://ieeexplore.ieee.org/document/9747729","url_pdf":"https://ieeexplore.ieee.org/document/9747729","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"lipreading","task_name":"Lipreading"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"depthwise-separable-convolution","method_name":"Depthwise Separable Convolution"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"inverted-residual-block","method_name":"Inverted Residual Block"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"rmsprop","method_name":"RMSProp"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"squeeze-and-excitation-block","method_name":"Squeeze-and-Excitation Block"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lipreading-on-lip-reading-in-the-wild","task":"Lipreading","dataset":"Lip Reading in the Wild","model":"3D Conv + EfficientNetV2 + Transformer + TCN","rank_in_archive_order":5,"of":22,"metrics":{"Top-1 Accuracy":"89.52"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}