2023/10/27 · We propose a Supervised Cross-modal Contrastive Learning Framework for Audio-Visual Coding (SCLAV). Our framework includes an audio-visual coding network.
This is an open source repository for our paper SCLAV: Supervised Cross-modal Contrastive Learning for Audio-Visual Coding based on the pytorch framework.
ADSH treats the query points and database points in an asymmetric way. More specifically, ADSH learns a deep hash function only for query points, while the hash ...
SCLAV: Supervised Cross-modal Contrastive Learning for Audio-Visual Coding. In the 31st ACM International Conference on Multimedia (MM'23). Never-Ending ...
SCLAV: Supervised Cross-modal Contrastive Learning for Audio-Visual Coding. C Sun, M Chen, J Cheng, H Liang, C Zhu, J Chen. Proceedings of the 31st ACM ...
We present CrissCross , a self-supervised framework for learning audio-visual representations. A novel notion is introduced in our framework ...
A novel self-supervised method that leverages unsupervised clustering in one modality as a supervisory signal for the other modality, is proposed.
2023/02/15 · We propose a self-supervised learning approach for videos that learns representations of both the RGB frames and the accompanying audio without human ...
2023/10/30 · SCLAV: Supervised Cross-modal Contrastive Learning for Audio-Visual Coding mmfp3292. MTSN: Multiscale Temporal Similarity Network for ...
2024/01/17 · This paper introduces a novel approach named CrossVideo, which aims to enhance self-supervised cross-modal contrastive learning in the field of point cloud ...
含まれない: SCLAV: Audio-