Search Results for author:
Found 15 papers, 6 papers with code
Relevance-based Margin for Contrastively-trained Video Retrieval Models
We show that even if we carefully tuned the fixed margin, our technique (which does not have the margin as a hyper-parameter) would still achieve better performance.
Ranked #7 on
Multi-Instance Retrieval
on EPIC-KITCHENS-100
Gate-Shift-Fuse for Video Action Recognition
3D kernel factorization approaches have been proposed to reduce the complexity of 3D CNNs.
Ranked #17 on
Action Recognition
on EPIC-KITCHENS-100
(using extra training data)
SAIC_Cambridge-HuPBA-FBK Submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021
This report presents the technical details of our submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021.
Space-time Mixing Attention for Video Transformer
In this work, we propose a Video Transformer model the complexity of which scales linearly with the number of frames in the video sequence and hence induces no overhead compared to an image-based Transformer model.
Ranked #32 on
Action Classification
on Kinetics-600
Learning to Recognize Actions on Objects in Egocentric Video with Attention Dictionaries
We present EgoACO, a deep neural architecture for video action recognition that learns to pool action-context-object descriptors from frame level features by leveraging the verb-noun structure of action labels in egocentric video datasets.
FBK-HUPBA Submission to the EPIC-Kitchens Action Recognition 2020 Challenge
In this report we describe the technical details of our submission to the EPIC-Kitchens Action Recognition 2020 Challenge.
Gate-Shift Networks for Video Action Recognition
Deep 3D CNNs for video action recognition are designed to learn powerful representations in the joint spatio-temporal feature space.
Ranked #26 on
Action Recognition
on Something-Something V1
(using extra training data)
An Analysis of Deep Neural Networks with Attention for Action Recognition from a Neurophysiological Perspective
We review three recent deep learning based methods for action recognition and present a brief comparative analysis of the methods from a neurophyisiological point of view.
FBK-HUPBA Submission to the EPIC-Kitchens 2019 Action Recognition Challenge
In this report we describe the technical details of our submission to the EPIC-Kitchens 2019 action recognition challenge.
Hierarchical Feature Aggregation Networks for Video Action Recognition
Most action recognition methods base on a) a late aggregation of frame level CNN features using average pooling, max pooling, or RNN, among others, or b) spatio-temporal aggregation via 3D convolutions.
Ranked #51 on
Action Recognition
on HMDB-51
(using extra training data)
LSTA: Long Short-Term Attention for Egocentric Action Recognition
Egocentric activity recognition is one of the most challenging tasks in video analysis.
Ranked #5 on
Egocentric Activity Recognition
on EGTEA
Top-down Attention Recurrent VLAD Encoding for Action Recognition in Videos
Most recent approaches for action recognition from video leverage deep architectures to encode the video clip into a fixed length representation vector that is then used for classification.
Attention is All We Need: Nailing Down Object-centric Attention for Egocentric Activity Recognition
Our model is built on the observation that egocentric activities are highly characterized by the objects and their locations in the video.
Ranked #6 on
Egocentric Activity Recognition
on EGTEA
Learning to Detect Violent Videos using Convolutional Long Short-Term Memory
A convolutional neural network is used to extract frame level features from a video.
Convolutional Long Short-Term Memory Networks for Recognizing First Person Interactions
The proposed approach uses a pair of convolutional neural networks, whose parameters are shared, for extracting frame level features from successive frames of the video.
