Search Results for author:
Found 10 papers, 2 papers with code
Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks
We show that the methodology provides a high-quality unsupervised categorization of movements.
PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition
Given inputs of body keypoints sequences obtained during various movements, our system associates the sequences with actions.
BI-MAML: Balanced Incremental Approach for Meta Learning
The balanced learning strategy enables BI-MAML to both outperform other state-of-the-art models in terms of classification accuracy for existing tasks and also accomplish efficient adaption to similar new tasks with less required shots.
Audeo: Audio Generation for a Silent Performance Video
We present a novel system that gets as an input video frames of a musician playing the piano and generates the music for that video.
Multi-Instrumentalist Net: Unsupervised Generation of Music from Body Movements
We propose a novel system that takes as an input body movements of a musician playing a musical instrument and generates music in an unsupervised setting.
How Does it Sound?
It is often the case that the experience of watching the video can be enhanced by adding a musical soundtrack that is in-sync with the rhythmic features of these activities.
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain.
Be Everywhere - Hear Everything (BEE): Audio Scene Reconstruction by Sparse Audio-Visual Samples
The audio at the listener location is a complex outcome of sound propagation through the scene geometry and interacting with surfaces and also the locations of the emitters and the sounds they emit.
Physics-Driven Diffusion Models for Impact Sound Synthesis from Videos
Traditional methods of impact sound synthesis use physics simulation to obtain a set of physics parameters that could represent and synthesize the sound.
V2Meow: Meowing to the Visual Beat via Video-to-Music Generation
Video-to-music generation demands both a temporally localized high-quality listening experience and globally aligned video-acoustic signatures.
