57 citations · 142 across the 14 of their papers we have counts for
16 papers · 1 filter
Relevance-based Margin for Contrastively-trained Video Retrieval Models
Alex Falcon, Swathikiran Sudhakaran, Giuseppe Serra +2
Video retrieval using natural language queries has attracted increasing interest due to its relevance in real-world applications, from intelligent access in private media galleries…
Gate-Shift-Fuse for Video Action Recognition
Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz
Convolutional Neural Networks are the de facto models for image recognition. However 3D CNNs, the straight forward extension of 2D CNNs for video recognition, have not achieved the…
SAIC_Cambridge-HuPBA-FBK Submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021
Swathikiran Sudhakaran, Adrian Bulat, Juan-Manuel Perez-Rua +5
This report presents the technical details of our submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021. To participate in the challenge we deployed spatio-temporal…
Space-time Mixing Attention for Video Transformer
Adrian Bulat, Juan-Manuel Perez-Rua, Swathikiran Sudhakaran +2
This paper is on video recognition using Transformers. Very recent attempts in this area have demonstrated promising results in terms of recognition accuracy, yet they have been al…
Learning to Recognize Actions on Objects in Egocentric Video with Attention Dictionaries
Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz
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-no…
FBK-HUPBA Submission to the EPIC-Kitchens Action Recognition 2020 Challenge
Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz
In this report we describe the technical details of our submission to the EPIC-Kitchens Action Recognition 2020 Challenge. To participate in the challenge we deployed spatio-tempor…
Gate-Shift Networks for Video Action Recognition
Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz
Deep 3D CNNs for video action recognition are designed to learn powerful representations in the joint spatio-temporal feature space. In practice however, because of the large numbe…
An Analysis of Deep Neural Networks with Attention for Action Recognition from a Neurophysiological Perspective
Swathikiran Sudhakaran, Oswald Lanz
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. We posit…
FBK-HUPBA Submission to the EPIC-Kitchens 2019 Action Recognition Challenge
Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz
In this report we describe the technical details of our submission to the EPIC-Kitchens 2019 action recognition challenge. To participate in the challenge we have developed a numbe…
Hierarchical Feature Aggregation Networks for Video Action Recognition
Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz
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 aggregatio…
LSTA: Long Short-Term Attention for Egocentric Action Recognition
Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz
Egocentric activity recognition is one of the most challenging tasks in video analysis. It requires a fine-grained discrimination of small objects and their manipulation. While som…
Top-down Attention Recurrent VLAD Encoding for Action Recognition in Videos
Swathikiran Sudhakaran, Oswald Lanz
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 classi…
Attention is All We Need: Nailing Down Object-centric Attention for Egocentric Activity Recognition
Swathikiran Sudhakaran, Oswald Lanz
In this paper we propose an end-to-end trainable deep neural network model for egocentric activity recognition. Our model is built on the observation that egocentric activities are…
Learning to Detect Violent Videos using Convolutional Long Short-Term Memory
Swathikiran Sudhakaran, Oswald Lanz
Developing a technique for the automatic analysis of surveillance videos in order to identify the presence of violence is of broad interest. In this work, we propose a deep neural…
Convolutional Long Short-Term Memory Networks for Recognizing First Person Interactions
Swathikiran Sudhakaran, Oswald Lanz
In this paper, we present a novel deep learning based approach for addressing the problem of interaction recognition from a first person perspective. The proposed approach uses a p…
Sparse distributed localized gradient fused features of objects
Swathikiran Sudhakarana, Alex Pappachen James
The sparse, hierarchical, and modular processing of natural signals is related to the ability of humans to recognize objects with high accuracy. In this study, we report a sparse f…