Extract video features from raw videos using multiple GPUs. We support RAFT flow frames as well as S3D, I3D, R(2+1)D, VGGish, CLIP, and TIMM models.
Source code for "Taming Visually Guided Sound Generation" (Oral at the BMVC 2021)
Source code for "Bi-modal Transformer for Dense Video Captioning" (BMVC 2020)
Source code for "Synchformer: Efficient Synchronization from Sparse Cues" (ICASSP 2024)
Source code for "Sparse in Space and Time: Audio-visual Synchronisation with Trainable Selectors." (Spotlight at the BMVC 2022)
PyTorch/Tensorflow solutions for Stanford's CS231n: "CNNs for Visual Recognition"
Personal webpage