activity
20142022
most citedOn Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization

119 citations · 221 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV2022

Consistency Regularization for Domain Adaptation

Kian Boon Koh, Basura Fernando

Collection of real world annotations for training semantic segmentation models is an expensive process. Unsupervised domain adaptation (UDA) tries to solve this problem by studying…

cs.CV20213 cited

LocFormer: Enabling Transformers to Perform Temporal Moment Localization on Long Untrimmed Videos With a Feature Sampling Approach

Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Basura Fernando +2

We propose LocFormer, a Transformer-based model for video grounding which operates at a constant memory footprint regardless of the video length, i.e. number of frames. LocFormer i…

cs.CV201613 cited

Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation

Mohammad Sadegh Aliakbarian, Fatemehsadat Saleh, Basura Fernando +3

Action recognition and anticipation are key to the success of many computer vision applications. Existing methods can roughly be grouped into those that extract global, context-awa…

cs.CV201644 cited

Generalized BackPropagation, Étude De Cas: Orthogonality

Mehrtash Harandi, Basura Fernando

This paper introduces an extension of the backpropagation algorithm that enables us to have layers with constrained weights in a deep network. In particular, we make use of the Rie…

cs.CV20161 cited

SPICE: Semantic Propositional Image Caption Evaluation

Peter Anderson, Basura Fernando, Mark Johnson +1

There is considerable interest in the task of automatically generating image captions. However, evaluation is challenging. Existing automatic evaluation metrics are primarily sensi…

cs.CV2016119 cited

On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization

Stephen Gould, Basura Fernando, Anoop Cherian +3

Some recent works in machine learning and computer vision involve the solution of a bi-level optimization problem. Here the solution of a parameterized lower-level problem binds va…