Hongbin Wang

Bo Yan, Leilei Cao, Zhuang Li et al.

The goal of AVA challenge is to provide vision-based benchmarks and methods relevant to accessibility. In this paper, we introduce the technical details of our submission to the CVPR2022 AVA Challenge. Firstly, we conducted some experiments to help employ proper model and data augmentation strategy for this task. Secondly, an effective training strategy was applied to improve the performance. Thirdly, we integrated the results from two different segmentation frameworks to improve the performance further. Experimental results demonstrate that our approach can achieve a competitive result on the AVA test set. Finally, our approach achieves 63.008\%AP@0.50:0.95 on the test set of CVPR2022 AVA Challenge.

Chunyong Yang, Pengfei Liu, Yanli Chen et al.

This paper presents our MSXF TTS system for Task 3.1 of the Audio Deep Synthesis Detection (ADD) Challenge 2022. We use an end to end text to speech system, and add a constraint loss to the system when training stage. The end to end TTS system is VITS, and the pre-training self-supervised model is wav2vec 2.0. And we also explore the influence of the speech speed and volume in spoofing. The faster speech means the less the silence part in audio, the easier to fool the detector. We also find the smaller the volume, the better spoofing ability, though we normalize volume for submission. Our team is identified as C2, and we got the fourth place in the challenge.