Search Results for author:
Found 7 papers, 6 papers with code
End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network
While efficient architectures and a plethora of augmentations for end-to-end image classification tasks have been suggested and heavily investigated, state-of-the-art techniques for audio classifications still rely on numerous representations of the audio signal together with large architectures, fine-tuned from large datasets.
Ranked #2 on
Environmental Sound Classification
on UrbanSound8K
(using extra training data)
ML-Decoder: Scalable and Versatile Classification Head
In this paper, we introduce ML-Decoder, a new attention-based classification head.
Ranked #2 on
Fine-Grained Image Classification
on Stanford Cars
(using extra training data)
PETA: Photo Albums Event Recognition using Transformers Attention
We address this gap with a tailor-made solution, combining the power of CNNs for image representation and transformers for album representation to perform global reasoning on image collection, offering a practical and efficient solution for photo albums event recognition.
An Image is Worth 16x16 Words, What is a Video Worth?
Methods that reach State of the Art (SotA) accuracy, usually make use of 3D convolution layers as a way to abstract the temporal information from video frames.
Ranked #23 on
Action Recognition
on UCF101
(using extra training data)
TResNet: High Performance GPU-Dedicated Architecture
In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency.
Ranked #7 on
Fine-Grained Image Classification
on Oxford 102 Flowers
(using extra training data)
Graph Embedded Pose Clustering for Anomaly Detection
We propose a new method for anomaly detection of human actions.
Ranked #7 on
Video Anomaly Detection
on HR-UBnormal
Video Object Segmentation using Tracked Object Proposals
We present an approach to semi-supervised video object segmentation, in the context of the DAVIS 2017 challenge.
