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This technical report describes our QuAVF@NTU-NVIDIA submission to the Ego4DTalking to Me (TTM) Challenge 2023. Based on the observation from the TTM taskand the provided dataset, we propose to use two separate models to process theinput videos and audio. By doing so, we can utilize all the labeled trainingdata, including those without bounding box labels. Furthermore, we leverage theface quality score from a facial landmark prediction model for filtering noisyface input data. The face quality score is also employed in our proposedquality-aware fusion for integrating the results from two branches. With thesimple architecture design, our model achieves 67.4% mean average precision(mAP) on the test set, which ranks first on the leaderboard and outperforms thebaseline method by a large margin. Code is available at:https://github.com/hsi-che-lin/Ego4D-QuAVF-TTM-CVPR23

QuAVF: Quality-aware Audio-Visual Fusion for Ego4D Talking to Me Challenge