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Found 4 papers, 3 papers with code
LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration
To address this challenge, we propose a novel approach for distribution-aware latent augmentation that leverages the relationships across samples to guide the augmentation procedure.
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image.
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
By enabling flexible pre-training that can be transferred to neural recordings of different size and order, our work provides a first step towards creating a foundation model for neural decoding.
Multi-Task Learning for Audio Visual Active Speaker Detection
This report describes the approach underlying our submission to the active speaker detection task (task B-2) of ActivityNet Challenge 2019.
Ranked #17 on
Audio-Visual Active Speaker Detection
on AVA-ActiveSpeaker
(using extra training data)
