Search Results for author:
Found 26 papers, 10 papers with code
Your Image is My Video: Reshaping the Receptive Field via Image-To-Video Differentiable AutoAugmentation and Fusion
The landscape of deep learning research is moving towards innovative strategies to harness the true potential of data.
Inductive Attention for Video Action Anticipation
Consequently, existing solutions based on the action recognition models are only suboptimal.
A Feature-space Multimodal Data Augmentation Technique for Text-video Retrieval
Data augmentation techniques were introduced to increase the performance on unseen test examples by creating new training samples with the application of semantics-preserving techniques, such as color space or geometric transformations on images.
UniUD-FBK-UB-UniBZ Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2022
This report presents the technical details of our submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2022.
Ranked #3 on
Multi-Instance Retrieval
on EPIC-KITCHENS-100
NVIDIA-UNIBZ Submission for EPIC-KITCHENS-100 Action Anticipation Challenge 2022
In this report, we describe the technical details of our submission for the EPIC-Kitchen-100 action anticipation challenge.
Unified Recurrence Modeling for Video Action Anticipation
To this end, we propose a unified recurrence modeling for video action anticipation via message passing framework.
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
Learning video retrieval models with relevance-aware online mining
Due to the amount of videos and related captions uploaded every hour, deep learning-based solutions for cross-modal video retrieval are attracting more and more attention.
Ranked #5 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.
Higher Order Recurrent Space-Time Transformer for Video Action Prediction
Endowing visual agents with predictive capability is a key step towards video intelligence at scale.
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.
Data augmentation techniques for the Video Question Answering task
Video Question Answering (VideoQA) is a task that requires a model to analyze and understand both the visual content given by the input video and the textual part given by the question, and the interaction between them in order to produce a meaningful answer.
Novel-View Human Action Synthesis
We first estimate the 3D mesh of the target body and transfer the rough textures from the 2D images to the mesh.
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
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.
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.
Uncovering Interactions and Interactors: Joint Estimation of Head, Body Orientation and F-Formations From Surveillance Videos
We present a novel approach for jointly estimating tar- gets' head, body orientations and conversational groups called F-formations from a distant social scene (e. g., a cocktail party captured by surveillance cameras).
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social settings (e. g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels.
